Autism RNASeq analysis

Preparation

Load packages and set options

Setup the default knitr options for the R code sections below.

Load the packages used throughout this analysis.

setwd("/share/users/Mike/AutismPaper/RNASeq/R")
suppressMessages({
  library('kableExtra')
  library('formattable')
  library('goseq')
  library('readr')
  library('stringr')
  library('tibble')
  library('dplyr')
  library('tidyr')
  library('VennDiagram')
  library('tximport')
  library('DESeq2')
  library('regionReport')
  library('ggplot2')
  library('BiocParallel')
  library('RColorBrewer')
  library('colorspace')
  library('dendextend')
  library('gplots')
  library('superheat')
  library('GGally')
  library('corrplot')
  library('pheatmap')
  library('DT')
  library('devtools')
  library('VennDiagram')
  library('ggrepel')
  library('gridExtra')
  library('vsn')
  library('MASS')
  library('plotly')
  library('htmlwidgets')
  register(MulticoreParam(detectCores()))
  options(tibble.print_max=100)
})

Import metadata

This section assembles the metadata for the datasets included in this analysis.

meta_int <- read_tsv('../../metadata/RNASeq.internal.tsv') %>%
  mutate(library = 'paired end') %>%
  mutate(readlength = 101) %>%
  mutate(tissue = 'SVZ') %>%
  mutate(path = paste0('/share/users/Mike/AutismPaper/RNASeq/OurData/', 
                           sample, '.refseqnorrna.kallisto.paired/abundance.h5')
  )

meta_ext <- read_tsv('../../metadata/RNASeq.external.tsv') %>%
  mutate(library = 'single end') %>%
  mutate(readlength = 75) %>%
  mutate(condition = 'Control') %>%
  mutate(path = paste0('/share/users/Mike/AutismPaper/RNASeq/external/', 
                           sample, '.refseqnorrna.kallisto.single/abundance.h5')
  )

meta_ext1 <-read_tsv('../../metadata/RNASeq.external2.tsv') %>%
  mutate(path = paste0('/share/users/Mike/AutismPaper/RNASeq/external2/', 
                           sample, '.refseqnorrna.kallisto.single/abundance.h5') 
)

metadata <- bind_rows(meta_int, meta_ext, meta_ext1) %>%
  mutate(age = factor(if_else(!is.na(age_wpc), 'Fetal', 
                              if_else(between(age_years,1,19),'Child','Adult')),
                      levels = c('Fetal', 'Child', 'Adult')
                      )
         ) %>%
  mutate(tissue1 = if_else(grepl('SVZ$', tissue), 'SVZ', 
                           if_else(grepl('frontal', tissue), 'FC',
                                   if_else(grepl('temporal', tissue), 'TC',
                                           tissue)
                                   )
                           )
         ) %>%
  mutate(sample = if_else(grepl('OurData', path),
                          paste(sep="_",sub("(\\w).*", "\\1", condition), 
                                paste0(age_years, 'y'),
                                sample), 
                          if_else(grepl('external2',path), 
                                  paste(sep='_', sub("(\\w).*", "\\1", condition), 
                                        tissue1, 
                                        paste0(age_years, 'y'), 
                                        sub(".*(\\d{2})$", "\\1", sample)
                                        ),
                                  paste(sep='_', tissue, 
                                        paste0(age_wpc, 'w'), 
                                        sub(".*(\\d{2})$", "\\1", sample)
                                        )
                                  )
                          )
  ) %>%
  filter(is.na(sex) | sex == 'M')

SVZ tissue analysis

DESeq2 analysis

Select SVZ metadata

Select the metadata corresponding to the SVZ tissue samples:

metadata1 <- metadata %>%
  filter(grepl('SVZ',tissue) & !grepl('H16$', sample)) %>%
  mutate(condition = factor(condition, levels=c('Control', 'Autism'))) %>%
  mutate(grp = factor(paste0(age,condition), levels=c('FetalControl', 'ChildControl', 'ChildAutism', 'AdultControl', 'AdultAutism'))) %>%
  arrange(sample) %>%
  dplyr::select( c('sample', 'condition', 'library', 'readlength', 'tissue', 'age', 'grp', 'path'))
metadata1

Read kallisto output files

Read the files with the kallisto estimated counts for each of the transcripts in RefSeq version 108 (http://www.ncbi.nlm.nih.gov/genome/annotation_euk/Homo_sapiens/108/).

files <- metadata1 %>% dplyr::select('sample', 'path') %>% spread('sample', 'path') %>% as.list() %>% unlist()
txi.kallisto <- tximport(files, type = "kallisto", txOut = TRUE)
metadata1.df <- metadata1 %>%  column_to_rownames(var="sample") %>% as.data.frame()
rownames(txi.kallisto$counts) <- as.character(rownames(txi.kallisto$counts))

Read RefSeq transcript-to-genes data

Read the transcript-to-genes metadata table that associates RefSeq accessions (e.g. NM_xxxxxx) to Entrez Gene IDs, symbols, etc.

ttgfile <- '/share/db/refseq/human/refseq.108.rna.t2g.txt'
ttg <- read_tsv(ttgfile)

Perform Wald test

• Create a DESeq dataset from the imported estimated counts object with a model design based on a factor made by combining the age group and the condition (grp) with a baseline level corresponding to the Control condition and Fetal age group.
• Filter out all the rows that have at least 1 estimated read in the fetal samples, at least 1 in the control samples and at least 1 in the autism samples .
• Perform a Wald test on this model.
• List the available contrasts

dds <- DESeqDataSetFromTximport(txi = txi.kallisto, colData = metadata1.df, design = ~grp)
c <- counts(dds) %>% as.tibble()
c[['target_id']] <- rownames(counts(dds))
c <- c %>% mutate(sum1 = rowSums(dplyr::select(., contains("SVZ"))),
                  sum2 = rowSums(dplyr::select(., starts_with("A_"))),
                  sum3 = rowSums(dplyr::select(., starts_with("C_"))))
# n1 <- colnames(c) %>% as.tibble() %>% filter(grepl('SVZ', value)) %>% summarise(n()) %>% as.numeric()
# n2 <- colnames(c) %>% as.tibble() %>% filter(grepl('A_', value)) %>% summarise(n()) %>% as.numeric()
# n3 <- colnames(c) %>% as.tibble() %>% filter(grepl('C_', value)) %>% summarise(n()) %>% as.numeric()
c <- c %>% filter((sum1 > 1) & (sum2 > 1) & (sum3 > 1))
dds <- dds[c$target_id,]
rm('c')
ddsWald <- DESeq(dds, test = "Wald", betaPrior = TRUE, parallel = TRUE)
resultsNames(ddsWald)

## [1] "Intercept"       "grpFetalControl" "grpChildControl" "grpChildAutism" 
## [5] "grpAdultControl" "grpAdultAutism"

DESeq results

Extract the results from the model.

resultsChild   <- results(ddsWald, alpha = 0.05, 
                          contrast = c('grp', 'ChildControl', 'FetalControl'))
resultsAdult   <- results(ddsWald, alpha = 0.05, 
                          contrast = c('grp', 'AdultControl', 'FetalControl'))
resultsChildA  <- results(ddsWald, alpha = 0.05, 
                          contrast = c('grp', 'ChildAutism', 'FetalControl'))
resultsAdultA  <- results(ddsWald, alpha = 0.05, 
                          contrast = c('grp', 'AdultAutism', 'FetalControl'))
resultsChildCA <- results(ddsWald, alpha = 0.05, 
                          contrast = c('grp','ChildControl','ChildAutism'))
resultsAdultCA <- results(ddsWald, alpha = 0.05, 
                          contrast = c('grp','AdultControl','AdultAutism'))


# From the DESeq2 vignette: 
#
# "Regarding multiple test correction, if a user is planning to contrast all pairs of many levels
#  and then selectively reporting the results of only a subset of those pairs, one needs to perform multiple testing across contrasts 
#  as well as genes to control for this additional form of multiple testing. 
#  This can be done by using the p.adjust function across a long vector of p values from all pairs of contrasts, 

#  then re-assigning these adjusted p values to the appropriate results table."
# pChild <- resultsChild %>% as.tibble() %>% rownames_to_column(var='target_id') %>%
#   dplyr::select(c('target_id', 'pvalue')) %>% spread('target_id', 'pvalue') %>%
#   as.list() %>% unlist()
# pAdult <- resultsAdult %>% as.tibble() %>% rownames_to_column(var='target_id') %>%
#   dplyr::select(c('target_id', 'pvalue')) %>% spread('target_id', 'pvalue') %>%
#   as.list() %>% unlist()

signifChild <- resultsChild %>%
  as.tibble(.) %>% rownames_to_column(var='target_id') %>%
  filter(padj<0.05) %>% arrange(padj) %>%
  left_join(ttg, by = 'target_id') %>%
  dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

signifAdult <- resultsAdult %>%
  as.tibble(.) %>% rownames_to_column(var='target_id') %>%
  filter(padj<0.05) %>% arrange(padj) %>%
  left_join(ttg, by = 'target_id') %>%
  dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

signifChildA <- resultsChildA %>%
  as.tibble(.) %>% rownames_to_column(var='target_id') %>%
  filter(padj<0.05) %>% arrange(padj) %>%
  left_join(ttg, by = 'target_id') %>%
  dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

signifAdultA <- resultsAdultA %>%
  as.tibble(.) %>% rownames_to_column(var='target_id') %>%
  filter(padj<0.05) %>% arrange(padj) %>%
  left_join(ttg, by = 'target_id') %>%
  dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

signifChildCA <- resultsChildCA %>%
  as.tibble(.) %>% rownames_to_column(var='target_id') %>%
  filter(padj<0.05) %>% arrange(padj) %>%
  left_join(ttg, by = 'target_id') %>%
  dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

signifAdultCA <- resultsAdultCA %>%
  as.tibble(.) %>% rownames_to_column(var='target_id') %>%
  filter(padj<0.05) %>% arrange(padj) %>%
  left_join(ttg, by = 'target_id') %>%
  dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

devChildCA  <- intersect(signifChild$target_id, signifChildCA$target_id)
devAdultCA  <- intersect(signifAdult$target_id, signifAdultCA$target_id)
devChildCAg <- tibble(target_id=devChildCA) %>% left_join(ttg, by='target_id') %>% 
  dplyr::select('gene_id') %>% as.list() %>% unlist() %>% unique()
devAdultCAg <- tibble(target_id=devAdultCA) %>% left_join(ttg, by='target_id') %>% 
  dplyr::select('gene_id') %>% as.list() %>% unlist() %>% unique()
devChildCAs <- tibble(target_id=devChildCA) %>% left_join(ttg, by='target_id') %>% 
  dplyr::select('symbol') %>% as.list() %>% unlist() %>% unique()
devAdultCAs <- tibble(target_id=devAdultCA) %>% left_join(ttg, by='target_id') %>% 
  dplyr::select('symbol') %>% as.list() %>% unlist() %>% unique()

Control - Fetal

Summary of the number of transcripts and genes differentially expressed in control samples of different age groups vs. the fetal samples (“developmental genes”):

rsum_control_fetal <- tribble( ~Counts, ~Child, ~Adult,
                               'Transcripts', 
                               length(signifChild$target_id), 
                               length(signifAdult$target_id), 
                               'Genes', 
                               length(unique(signifChild$gene_id)),
                               length(unique(signifAdult$gene_id))
                               )
kable(rsum_control_fetal, format = 'markdown')

Counts	Child	Adult
Transcripts	12258	14368
Genes	9046	10052

Venn diagram:

grid.draw(venn.diagram(list(Child=signifChild$target_id, Adult=signifAdult$target_id), 
             filename = NULL,
             alpha = 0.5,
             inverted = TRUE, 
             cat.pos = c(45, 315),
             fill = c("cornflowerblue", "darkorchid1"),
             cat.col = c("darkblue", "darkorchid4")
             )
)

Autism - Fetal

Summary of the number of transcripts and genes differentially expressed in autism samples of different age groups vs. the fetal samples:

rsum_autism_fetal <- tribble( ~Counts, ~Child, ~Adult, 
                               'Transcripts', 
                               length(signifChildA$target_id), 
                               length(signifAdultA$target_id), 
                               'Genes', 
                               length(unique(signifChildA$gene_id)),
                               length(unique(signifAdultA$gene_id))
                               )
kable(rsum_autism_fetal, format = 'markdown')

Counts	Child	Adult
Transcripts	14570	13832
Genes	10335	9870

Venn diagram:

grid.draw(venn.diagram(list(Child=signifChildA$target_id, Adult=signifAdultA$target_id),
             filename = NULL,
             alpha = 0.5,
             fill = c("cornflowerblue", "darkorchid1"),
             cat.col = c("darkblue", "darkorchid4")
             )
)

Control - Autism

Summary of the number of transcripts and genes differentially expressed in autism samples of different age groups vs. the same age control samples:

rsum_autism_control <- tribble( ~Counts, ~Child, ~Adult,
                               'Transcripts', 
                               length(signifChildCA$target_id), 
                               length(signifAdultCA$target_id), 
                               'Genes', 
                               length(unique(signifChildCA$gene_id)),
                               length(unique(signifAdultCA$gene_id))
                               )
kable(rsum_autism_control, format = 'markdown')

Counts	Child	Adult
Transcripts	21	46
Genes	21	44

Venn diagram:

grid.draw(venn.diagram(list(Child=signifChildCA$target_id, Adult=signifAdultCA$target_id), 
                       rotation.degree = 180,
                       filename = NULL, 
                       alpha = 0.5, 
                       fill = c("cornflowerblue", "darkorchid1"), 
                       cat.col = c("darkblue", "darkorchid4") 
                       ) 
          )

Developmental Control - Autism

Summary of the number of transcripts and genes differentially expressed in autism samples of different age groups vs. the same age control samples AND also different in the control age group vs. the fetal samples (“developmental”):

rsum_autism_control_fetal <- tribble( ~Counts, ~Child, ~Adult,
                               'Transcripts', 
                               length(devChildCA), 
                               length(devAdultCA), 
                               'Genes', 
                               length(devChildCAg),
                               length(devAdultCAg)
                               )
kable(rsum_autism_control_fetal, format = 'markdown')

Counts	Child	Adult
Transcripts	16	37
Genes	16	35

Venn diagram:

grid.draw(venn.diagram(list(Child=devChildCA, Adult=devAdultCA), 
                       filename = NULL, 
                       rotation.degree = 180, 
                       alpha = 0.5, 
                       fill = c("cornflowerblue", "darkorchid1"),
                       cat.col = c("darkblue", "darkorchid4")
                       )
          )

QC

PCA

## Transform count data
intgroup = "grp"
rld <- rlog(ddsWald, blind = FALSE)
vst <- varianceStabilizingTransformation(ddsWald, blind=FALSE)

## Perform PCA analysis and get percent of variance explained
data_pca <- plotPCA(rld, intgroup = intgroup, ntop = 1000, returnData = TRUE)
percentVar <- round(100 * attr(data_pca, "percentVar"))

## Make plot
data_pca %>%
ggplot(aes_string(x = "PC1", y = "PC2", color = "grp")) +
 geom_point(size = 3) +
 xlab(paste0("PC1: ", percentVar[1], "% variance")) +
 ylab(paste0("PC2: ", percentVar[2], "% variance")) +
 coord_fixed() +
 geom_text(aes(label=name), nudge_x = 1, hjust = 0, size = 3, check_overlap = FALSE)

The above plot shows the first two principal components that explain the variability in the data using the regularized log count data. If you are unfamiliar with principal component analysis, you might want to check the Wikipedia entry or this interactive explanation. In this case, the first and second principal component explain percentVar[1] and percentVar[2] percent of the variance respectively.

Sample-to-sample distances

## Obtain the sample euclidean distances
sampleDists <- dist(t(assay(rld)))
sampleDistMatrix <- as.matrix(sampleDists)

## Add names based on intgroup
rownames(sampleDistMatrix) <- rownames(colData(rld))
colnames(sampleDistMatrix) <- NULL

## Define colors to use for the heatmap if none were supplied
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)

## Make the heatmap
pheatmap(sampleDistMatrix, clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists, color = colors)

This plot shows how samples are clustered based on their euclidean distance using the regularized log transformed count data. This figure gives an overview of how the samples are hierarchically clustered. It is a complementary figure to the PCA plot.

MA Plots

Control - Fetal

This section contains the MA plots (see Wikipedia) that compare the mean of the normalized counts against the log fold change. They show one point per feature. The points are shown in red if the feature has an adjusted p-value less than alpha, that is, the statistically significant features are shown in red.

ChildControl-Fetal

## MA plot with alpha used in DESeq2::results()
plotMA(resultsChild, alpha = 0.05, main = 'MA plot with alpha = 0.05', ylim = c(-10, 10))
abline(h=c(-1,1),col="dodgerblue",lwd=2)

This plot uses alpha = 0.05, which is the alpha value used to determine which resulting features were significant when running the function DESeq2::results().

AdultControl-Fetal

## MA plot with alpha used in DESeq2::results()
plotMA(resultsAdult, alpha = 0.05, main = 'MA plot with alpha = 0.05', ylim = c(-10, 10))
abline(h=c(-1,1),col="dodgerblue",lwd=2)

This plot uses alpha = 0.05, which is the alpha value used to determine which resulting features were significant when running the function DESeq2::results().

Autism - Fetal

This section contains the MA plots (see Wikipedia) that compare the mean of the normalized counts against the log fold change. They show one point per feature. The points are shown in red if the feature has an adjusted p-value less than alpha, that is, the statistically significant features are shown in red.

ChildAutism-Fetal

## MA plot with alpha used in DESeq2::results()
plotMA(resultsChildA, alpha = 0.05, main = 'MA plot with alpha = 0.05', ylim = c(-10, 10))
abline(h=c(-1,1),col="dodgerblue",lwd=2)

This plot uses alpha = 0.05, which is the alpha value used to determine which resulting features were significant when running the function DESeq2::results().

AdultAutism-Fetal

## MA plot with alpha used in DESeq2::results()
plotMA(resultsAdultA, alpha = 0.05, main = 'MA plot with alpha = 0.05', ylim = c(-10, 10))
abline(h=c(-1,1),col="dodgerblue",lwd=2)

This plot uses alpha = 0.05, which is the alpha value used to determine which resulting features were significant when running the function DESeq2::results().

Control - Autism

This section contains the MA plots (see Wikipedia) that compare the mean of the normalized counts against the log fold change. They show one point per feature. The points are shown in red if the feature has an adjusted p-value less than alpha, that is, the statistically significant features are shown in red.

Child

## MA plot with alpha used in DESeq2::results()
plotMA(resultsChildCA, alpha = 0.05, main = 'MA plot with alpha = 0.05', ylim = c(-10, 10))
abline(h=c(-1,1),col="dodgerblue",lwd=2)

This plot uses alpha = 0.05, which is the alpha value used to determine which resulting features were significant when running the function DESeq2::results().

Adult

## MA plot with alpha used in DESeq2::results()
plotMA(resultsAdultCA, alpha = 0.05, main = 'MA plot with alpha = 0.05', ylim = c(-10, 10))
abline(h=c(-1,1),col="dodgerblue",lwd=2)

This plot uses alpha = 0.05, which is the alpha value used to determine which resulting features were significant when running the function DESeq2::results().

P-values distributions

Control - Fetal

This plot shows a histogram of the unadjusted p-values. It might be skewed right or left, or flat as shown in the Wikipedia examples. The shape depends on the percent of features that are differentially expressed. For further information on how to interpret a histogram of p-values check David Robinson’s post on this topic.

ChildControl-Fetal

## P-value histogram plot
resultsChild %>% as.tibble() %>%
  filter(!is.na(pvalue)) %>%
  ggplot(aes(x = pvalue)) +
  geom_histogram(alpha=.5, position='identity', bins = 50) +
  labs(title='Histogram of unadjusted p-values') +
  xlab('Unadjusted p-values') +
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,20000))

This is the numerical summary of the distribution of the p-values.

## P-value distribution summary
res <- resultsChild %>%
  as.tibble() %>%
  filter(!is.na(pvalue))
summary(res$pvalue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0552  0.3419  0.3862  0.6726  1.0000

AdultControl-Fetal

## P-value histogram plot
resultsAdult %>% as.tibble() %>%
  filter(!is.na(pvalue)) %>%
  ggplot(aes(x = pvalue)) +
  geom_histogram(alpha=.5, position='identity', bins = 50) +
  labs(title='Histogram of unadjusted p-values') +
  xlab('Unadjusted p-values') +
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,20000))

This is the numerical summary of the distribution of the p-values.

## P-value distribution summary
res <- resultsAdult %>%
  as.tibble() %>%
  filter(!is.na(pvalue))
summary(res$pvalue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.03371 0.29400 0.36157 0.64621 0.99994

Autism - Fetal

This plot shows a histogram of the unadjusted p-values. It might be skewed right or left, or flat as shown in the Wikipedia examples. The shape depends on the percent of features that are differentially expressed. For further information on how to interpret a histogram of p-values check David Robinson’s post on this topic.

ChildAutism-Fetal

## P-value histogram plot
resultsChildA %>% as.tibble() %>%
  filter(!is.na(pvalue)) %>%
  ggplot(aes(x = pvalue)) +
  geom_histogram(alpha=.5, position='identity', bins = 50) +
  labs(title='Histogram of unadjusted p-values') +
  xlab('Unadjusted p-values') +
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,20000))

This is the numerical summary of the distribution of the p-values.

## P-value distribution summary
res <- resultsChildA %>%
  as.tibble() %>%
  filter(!is.na(pvalue))
summary(res$pvalue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.03463 0.30691 0.36608 0.64983 0.99995

AdultAutism-Fetal

## P-value histogram plot
resultsAdultA %>% as.tibble() %>%
  filter(!is.na(pvalue)) %>%
  ggplot(aes(x = pvalue)) +
  geom_histogram(alpha=.5, position='identity', bins = 50) +
  labs(title='Histogram of unadjusted p-values') +
  xlab('Unadjusted p-values') +
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,20000))

This is the numerical summary of the distribution of the p-values.

## P-value distribution summary
res <- resultsAdultA %>%
  as.tibble() %>%
  filter(!is.na(pvalue))
summary(res$pvalue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.03664 0.30336 0.36591 0.65259 1.00000

Control - Autism

This plot shows a histogram of the unadjusted p-values. It might be skewed right or left, or flat as shown in the Wikipedia examples. The shape depends on the percent of features that are differentially expressed. For further information on how to interpret a histogram of p-values check David Robinson’s post on this topic.

Child

## P-value histogram plot
resultsChildCA %>% as.tibble() %>%
  filter(!is.na(pvalue)) %>%
  ggplot(aes(x = pvalue)) +
  geom_histogram(alpha=.5, position='identity', bins = 50) +
  labs(title='Histogram of unadjusted p-values') +
  xlab('Unadjusted p-values') +
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,20000))

This is the numerical summary of the distribution of the p-values.

## P-value distribution summary
res <- resultsChildCA %>%
  as.tibble() %>%
  filter(!is.na(pvalue))
summary(res$pvalue)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000001 0.4699726 0.6940816 0.6433850 0.8584663 0.9999953

Adult

## P-value histogram plot
resultsAdultCA %>% as.tibble() %>%
  filter(!is.na(pvalue)) %>%
  ggplot(aes(x = pvalue)) +
  geom_histogram(alpha=.5, position='identity', bins = 50) +
  labs(title='Histogram of unadjusted p-values') +
  xlab('Unadjusted p-values') +
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,20000))

This is the numerical summary of the distribution of the p-values.

## P-value distribution summary
res <- resultsAdultCA %>%
  as.tibble() %>%
  filter(!is.na(pvalue))
summary(res$pvalue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.4639  0.6936  0.6421  0.8596  1.0000

P-values cumulative sums

This table shows the number of features with p-values less or equal than some commonly used cutoff values.

Control - Fetal

ChildControl-Fetal

## Split features by different p-value cutoffs
pval_table <- resultsChild %>% 
  as.tibble() %>% 
  filter(!is.na(pvalue)) %>%
  mutate(Cut = as.numeric(
    as.character(
      cut(pvalue, include.lowest = TRUE, 
          breaks=c(0, 1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
          labels = c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))))) %>%
  group_by(Cut) %>%
  summarise(nCut=n()) %>%
  mutate(Count=cumsum(nCut)) %>%
  dplyr::select(c('Cut', 'Count'))
  
# pval_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
#     0.6, 0.7, 0.8, 0.9, 1), function(x) {
#     data.frame('Cut' = x, 'Count' = sum(resultsChild$pvalue <= x, na.rm = TRUE))
# })
# pval_table <- do.call(rbind, pval_table)
if (outputIsHTML) {
    kable(pval_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(pval_table)
}

Cut	Count
0.0001	6044
0.0010	8255
0.0100	12463
0.0250	15155
0.0500	18142
0.1000	22538
0.2000	29191
0.3000	35051
0.4000	40621
0.5000	46145
0.6000	51844
0.7000	57646
0.8000	63375
0.9000	69044
1.0000	74814

AdultControl-Fetal

## Split features by different p-value cutoffs
pval_table <- resultsAdult %>% 
  as.tibble() %>% 
  filter(!is.na(pvalue)) %>%
  mutate(Cut = as.numeric(
    as.character(
      cut(pvalue, include.lowest = TRUE, 
          breaks=c(0, 1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
          labels = c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))))) %>%
  group_by(Cut) %>%
  summarise(nCut=n()) %>%
  mutate(Count=cumsum(nCut)) %>%
  dplyr::select(c('Cut', 'Count'))
  
# pval_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
#     0.6, 0.7, 0.8, 0.9, 1), function(x) {
#     data.frame('Cut' = x, 'Count' = sum(resultsAdult$pvalue <= x, na.rm = TRUE))
# })
# pval_table <- do.call(rbind, pval_table)
if(outputIsHTML) {
    kable(pval_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(pval_table)
}

Cut	Count
0.0001	6927
0.0010	9524
0.0100	14302
0.0250	17449
0.0500	20691
0.1000	25303
0.2000	31965
0.3000	37735
0.4000	43042
0.5000	48234
0.6000	53677
0.7000	58999
0.8000	64247
0.9000	69543
1.0000	74814

Autism - Fetal

ChildAutism-Fetal

## Split features by different p-value cutoffs
pval_table <- resultsChildA %>% 
  as.tibble() %>% 
  filter(!is.na(pvalue)) %>%
  mutate(Cut = as.numeric(
    as.character(
      cut(pvalue, include.lowest = TRUE, 
          breaks=c(0, 1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
          labels = c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))))) %>%
  group_by(Cut) %>%
  summarise(nCut=n()) %>%
  mutate(Count=cumsum(nCut)) %>%
  dplyr::select(c('Cut', 'Count'))
  
# pval_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
#     0.6, 0.7, 0.8, 0.9, 1), function(x) {
#     data.frame('Cut' = x, 'Count' = sum(resultsChild$pvalue <= x, na.rm = TRUE))
# })
# pval_table <- do.call(rbind, pval_table)
if(outputIsHTML) {
    kable(pval_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(pval_table)
}

Cut	Count
0.0001	7431
0.0010	9930
0.0100	14377
0.0250	17364
0.0500	20509
0.1000	24816
0.2000	31322
0.3000	37005
0.4000	42432
0.5000	47956
0.6000	53422
0.7000	58718
0.8000	64140
0.9000	69434
1.0000	74814

AdultAutism-Fetal

## Split features by different p-value cutoffs
pval_table <- resultsAdultA %>% 
  as.tibble() %>% 
  filter(!is.na(pvalue)) %>%
  mutate(Cut = as.numeric(
    as.character(
      cut(pvalue, include.lowest = TRUE, 
          breaks=c(0, 1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
          labels = c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))))) %>%
  group_by(Cut) %>%
  summarise(nCut=n()) %>%
  mutate(Count=cumsum(nCut)) %>%
  dplyr::select(c('Cut', 'Count'))
  
# pval_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
#     0.6, 0.7, 0.8, 0.9, 1), function(x) {
#     data.frame('Cut' = x, 'Count' = sum(resultsAdult$pvalue <= x, na.rm = TRUE))
# })
# pval_table <- do.call(rbind, pval_table)
if(outputIsHTML) {
    kable(pval_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(pval_table)
}

Cut	Count
0.0001	6601
0.0010	9159
0.0100	13827
0.0250	17042
0.0500	20245
0.1000	24771
0.2000	31484
0.3000	37231
0.4000	42641
0.5000	47958
0.6000	53355
0.7000	58607
0.8000	64084
0.9000	69446
1.0000	74814

Control - Autism

Child

## Split features by different p-value cutoffs
pval_table <- resultsChildCA %>% 
  as.tibble() %>% 
  filter(!is.na(pvalue)) %>%
  mutate(Cut = as.numeric(
    as.character(
      cut(pvalue, include.lowest = TRUE, 
          breaks=c(0, 1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
          labels = c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))))) %>%
  group_by(Cut) %>%
  summarise(nCut=n()) %>%
  mutate(Count=cumsum(nCut)) %>%
  dplyr::select(c('Cut', 'Count'))
  
# pval_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
#     0.6, 0.7, 0.8, 0.9, 1), function(x) {
#     data.frame('Cut' = x, 'Count' = sum(resultsChild$pvalue <= x, na.rm = TRUE))
# })
# pval_table <- do.call(rbind, pval_table)
if(outputIsHTML) {
    kable(pval_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(pval_table)
}

Cut	Count
0.0001	38
0.0010	111
0.0100	434
0.0250	833
0.0500	1424
0.1000	2633
0.2000	5669
0.3000	9560
0.4000	14390
0.5000	20686
0.6000	28606
0.7000	37974
0.8000	49060
0.9000	61476
1.0000	74814

Adult

## Split features by different p-value cutoffs
pval_table <- resultsAdultCA %>% 
  as.tibble() %>% 
  filter(!is.na(pvalue)) %>%
  mutate(Cut = as.numeric(
    as.character(
      cut(pvalue, include.lowest = TRUE, 
          breaks=c(0, 1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
          labels = c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))))) %>%
  group_by(Cut) %>%
  summarise(nCut=n()) %>%
  mutate(Count=cumsum(nCut)) %>%
  dplyr::select(c('Cut', 'Count'))
  
# pval_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
#     0.6, 0.7, 0.8, 0.9, 1), function(x) {
#     data.frame('Cut' = x, 'Count' = sum(resultsAdult$pvalue <= x, na.rm = TRUE))
# })
# pval_table <- do.call(rbind, pval_table)
if(outputIsHTML) {
    kable(pval_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(pval_table)
}

Cut	Count
0.0001	64
0.0010	153
0.0100	528
0.0250	964
0.0500	1614
0.1000	3003
0.2000	6070
0.3000	9871
0.4000	14834
0.5000	21090
0.6000	28650
0.7000	38085
0.8000	49087
0.9000	60974
1.0000	74814

Adjusted p-values distributions

Control - Fetal

ChildControl-Fetal

This plot shows a histogram of the BH adjusted p-values. It might be skewed right or left, or flat as shown in the Wikipedia examples.

## Adjusted p-values histogram plot
hdescription <- elementMetadata(resultsChild) %>%
  as.tibble() %>%
  filter(grepl('adjusted',description)) %>%
  dplyr::select('description') %>%
  as.character()
resultsChild %>% 
  as.tibble() %>%
  filter(!is.na(padj)) %>% 
  ggplot(aes(x = padj)) +
  geom_histogram(alpha=.5, position='identity', bins = 50) +
  labs(title=paste('Histogram of',hdescription)) +
  xlab('Adjusted p-values') +
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,10000))

This is the numerical summary of the distribution of the BH adjusted p-values.

## Adjusted p-values distribution summary
res <- resultsChild %>%
  as.tibble() %>%
  filter(!is.na(padj))
summary(res$padj)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.1439  0.5735  0.5129  0.8473  1.0000

AdultControl-Fetal

This plot shows a histogram of the BH adjusted p-values. It might be skewed right or left, or flat as shown in the Wikipedia examples.

## Adjusted p-values histogram plot
hdescription <- elementMetadata(resultsAdult) %>%
  as.tibble() %>%
  filter(grepl('adjusted',description)) %>%
  dplyr::select('description') %>%
  as.character()
resultsAdult %>% 
  as.tibble() %>% 
  filter(!is.na(padj)) %>% 
  ggplot(aes(x = padj)) + 
  geom_histogram(alpha=.5, position='identity', bins = 50) + 
  labs(title=paste('Histogram of',hdescription)) + 
  xlab('Adjusted p-values') + 
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,10000))

This is the numerical summary of the distribution of the BH adjusted p-values.

## Adjusted p-values distribution summary
res <- resultsAdult %>%
  as.tibble() %>%
  filter(!is.na(padj))
summary(res$padj)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.1035  0.5225  0.4851  0.8254  0.9999

Autism - Fetal

ChildAutism-Fetal

This plot shows a histogram of the BH adjusted p-values. It might be skewed right or left, or flat as shown in the Wikipedia examples.

## Adjusted p-values histogram plot
hdescription <- elementMetadata(resultsChildA) %>% 
  as.tibble() %>% 
  filter(grepl('adjusted',description)) %>% 
  dplyr::select('description') %>% 
  as.character()
resultsChildA %>% 
  as.tibble() %>% 
  filter(!is.na(padj)) %>%
  ggplot(aes(x = padj)) + 
  geom_histogram(alpha=.5, position='identity', bins = 50) + 
  labs(title=paste('Histogram of',hdescription)) + 
  xlab('Adjusted p-values') + 
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,10000))

This is the numerical summary of the distribution of the BH adjusted p-values.

## Adjusted p-values distribution summary
res <- resultsChildA %>%
  as.tibble() %>%
  filter(!is.na(padj))
summary(res$padj)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.08851 0.51096 0.47669 0.81749 0.99993

AdultAutism-Fetal

This plot shows a histogram of the BH adjusted p-values. It might be skewed right or left, or flat as shown in the Wikipedia examples.

## Adjusted p-values histogram plot
hdescription <- elementMetadata(resultsAdultA) %>%
  as.tibble() %>%
  filter(grepl('adjusted',description)) %>%
  dplyr::select('description') %>%
  as.character()
resultsAdultA %>% 
  as.tibble() %>%
  filter(!is.na(padj)) %>% 
  ggplot(aes(x = padj)) +
  geom_histogram(alpha=.5, position='identity', bins = 50) +
  labs(title=paste('Histogram of',hdescription)) +
  xlab('Adjusted p-values') +
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,10000))

This is the numerical summary of the distribution of the BH adjusted p-values.

## Adjusted p-values distribution summary
res <- resultsAdultA %>%
  as.tibble() %>%
  filter(!is.na(padj))
summary(res$padj)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.1047  0.5218  0.4852  0.8238  1.0000

Control - Autism

Child

This plot shows a histogram of the BH adjusted p-values. It might be skewed right or left, or flat as shown in the Wikipedia examples.

## Adjusted p-values histogram plot
hdescription <- elementMetadata(resultsChildCA) %>%
  as.tibble() %>%
  filter(grepl('adjusted',description)) %>%
  dplyr::select('description') %>%
  as.character()
resultsChildCA %>%  
  as.tibble() %>% 
  filter(!is.na(padj)) %>%  
  ggplot(aes(x = padj)) + 
  geom_histogram(alpha=.5, position='identity', bins = 50) + 
  labs(title=paste('Histogram of',hdescription)) + 
  xlab('Adjusted p-values') + 
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,100))

This is the numerical summary of the distribution of the BH adjusted p-values.

## Adjusted p-values distribution summary
res <- resultsChildCA %>%
  as.tibble() %>%
  filter(!is.na(padj))
summary(res$padj)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## 0.004091 0.999991 0.999991 0.998160 0.999991 0.999995

Adult

This plot shows a histogram of the BH adjusted p-values. It might be skewed right or left, or flat as shown in the Wikipedia examples.

## Adjusted p-values histogram plot
hdescription <- elementMetadata(resultsAdultCA) %>%
  as.tibble() %>%
  filter(grepl('adjusted',description)) %>%
  dplyr::select('description') %>%
  as.character()
resultsAdultCA %>% 
  as.tibble() %>%
  filter(!is.na(padj)) %>% 
  ggplot(aes(x = padj)) +
  geom_histogram(alpha=.5, position='identity', bins = 50) +
  labs(title=paste('Histogram of',hdescription)) +
  xlab('Adjusted p-values') +
  coord_cartesian(xlim=c(0, 1.0005), ylim=c(0,100))

This is the numerical summary of the distribution of the BH adjusted p-values.

## Adjusted p-values distribution summary
res <- resultsAdultCA %>%
  as.tibble() %>%
  filter(!is.na(padj))
summary(res$padj)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  1.0000  1.0000  0.9972  1.0000  1.0000

Adjusted p-values cummulative sums

Control - Fetal

ChildControl-Fetal

This table shows the number of features with BH adjusted p-values less or equal than some commonly used cutoff values.

## Split features by different adjusted p-value cutoffs
padj_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
    0.6, 0.7, 0.8, 0.9, 1), function(x) {
    data.frame('Cut' = x, 'Count' = sum(resultsChild$padj <= x, na.rm = TRUE))
})
padj_table <- do.call(rbind, padj_table)
if(outputIsHTML) {
    kable(padj_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(padj_table)
}

Cut	Count
0.0001	4393
0.0010	5971
0.0100	8630
0.0250	10432
0.0500	12258
0.1000	14711
0.2000	18678
0.3000	22319
0.4000	25890
0.5000	29779
0.6000	34297
0.7000	39411
0.8000	45655
0.9000	54467
1.0000	66040

AdultControl-Fetal

This table shows the number of features with BH adjusted p-values less or equal than some commonly used cutoff values.

## Split features by different adjusted p-value cutoffs
padj_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
    0.6, 0.7, 0.8, 0.9, 1), function(x) {
    data.frame('Cut' = x, 'Count' = sum(resultsAdult$padj <= x, na.rm = TRUE))
})
padj_table <- do.call(rbind, padj_table)
if(outputIsHTML) {
    kable(padj_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(padj_table)
}

Cut	Count
0.0001	5152
0.0010	6914
0.0100	10059
0.0250	12170
0.0500	14368
0.1000	17382
0.2000	21924
0.3000	26042
0.4000	29932
0.5000	34132
0.6000	38912
0.7000	44176
0.8000	50909
0.9000	59256
1.0000	70367

Autism - Fetal

ChildAutism-Fetal

This table shows the number of features with BH adjusted p-values less or equal than some commonly used cutoff values.

## Split features by different adjusted p-value cutoffs
padj_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
    0.6, 0.7, 0.8, 0.9, 1), function(x) {
    data.frame('Cut' = x, 'Count' = sum(resultsChildA$padj <= x, na.rm = TRUE))
})
padj_table <- do.call(rbind, padj_table)
if(outputIsHTML) {
    kable(padj_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(padj_table)
}

Cut	Count
0.0001	5751
0.0010	7529
0.0100	10630
0.0250	12590
0.0500	14570
0.1000	17461
0.2000	21770
0.3000	25566
0.4000	29311
0.5000	33243
0.6000	37775
0.7000	43146
0.8000	49387
0.9000	57068
1.0000	67474

AdultAutism-Fetal

This table shows the number of features with BH adjusted p-values less or equal than some commonly used cutoff values.

## Split features by different adjusted p-value cutoffs
padj_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
    0.6, 0.7, 0.8, 0.9, 1), function(x) {
    data.frame('Cut' = x, 'Count' = sum(resultsAdultA$padj <= x, na.rm = TRUE))
})
padj_table <- do.call(rbind, padj_table)
if(outputIsHTML) {
    kable(padj_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(padj_table)
}

Cut	Count
0.0001	4831
0.0010	6563
0.0100	9679
0.0250	11667
0.0500	13832
0.1000	16954
0.2000	21413
0.3000	25321
0.4000	29354
0.5000	33465
0.6000	38211
0.7000	43514
0.8000	49957
0.9000	57862
1.0000	68905

Control - Autism

Child

This table shows the number of features with BH adjusted p-values less or equal than some commonly used cutoff values.

## Split features by different adjusted p-value cutoffs
padj_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
    0.6, 0.7, 0.8, 0.9, 1), function(x) {
    data.frame('Cut' = x, 'Count' = sum(resultsChildCA$padj <= x, na.rm = TRUE))
})
padj_table <- do.call(rbind, padj_table)
if(outputIsHTML) {
    kable(padj_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(padj_table)
}

Cut	Count
0.0001	0
0.0010	0
0.0100	3
0.0250	11
0.0500	21
0.1000	29
0.2000	47
0.3000	62
0.4000	73
0.5000	98
0.6000	114
0.7000	150
0.8000	182
0.9000	209
1.0000	59149

Adult

This table shows the number of features with BH adjusted p-values less or equal than some commonly used cutoff values.

## Split features by different adjusted p-value cutoffs
padj_table <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
    0.6, 0.7, 0.8, 0.9, 1), function(x) {
    data.frame('Cut' = x, 'Count' = sum(resultsAdultCA$padj <= x, na.rm = TRUE))
})
padj_table <- do.call(rbind, padj_table)
if(outputIsHTML) {
    kable(padj_table, format = 'markdown', align = c('c', 'c'))
} else {
    kable(padj_table)
}

Cut	Count
0.0001	3
0.0010	4
0.0100	18
0.0250	24
0.0500	46
0.1000	63
0.2000	83
0.3000	105
0.4000	144
0.5000	175
0.6000	205
0.7000	239
0.8000	268
0.9000	315
1.0000	63261

Volcano plots

Control - Fetal

ChildControl - Fetal

resultsChild %>%
  as.tibble()  %>%
  rownames_to_column(var='target_id') %>%
  mutate(sig=ifelse(target_id %in% signifChild[['target_id']], "FDR<0.05", "Not Sig")) %>%
  left_join(ttg, by = 'target_id') %>% 
  ggplot(aes(log2FoldChange, -log10(padj))) + 
  geom_point(aes(col=sig)) + 
  scale_color_manual(values=c("red", "black"))

#  coord_cartesian(ylim=c(0,100))

AdultControl - Fetal

resultsAdult %>%
  as.tibble()  %>%
  rownames_to_column(var='target_id') %>%
  mutate(sig=ifelse(target_id %in% signifAdult[['target_id']], "FDR<0.05", "Not Sig")) %>%
  left_join(ttg, by = 'target_id') %>% 
  ggplot(aes(log2FoldChange, -log10(padj))) + 
  geom_point(aes(col=sig)) + 
  scale_color_manual(values=c("red", "black"))

  #coord_cartesian(ylim=c(0,100))

Autism - Fetal

ChildAutism - Fetal

resultsChildA %>%
  as.tibble()  %>%
  rownames_to_column(var='target_id') %>%
  mutate(sig=ifelse(target_id %in% signifChildA[['target_id']], "FDR<0.05", "Not Sig")) %>%
  left_join(ttg, by = 'target_id') %>% 
  ggplot(aes(log2FoldChange, -log10(padj))) + 
  geom_point(aes(col=sig)) + 
  scale_color_manual(values=c("red", "black"))

#  coord_cartesian(ylim=c(0,100))

AdultAutism - Fetal

resultsAdultA %>%
  as.tibble()  %>%
  rownames_to_column(var='target_id') %>%
  mutate(sig=ifelse(target_id %in% signifAdultA[['target_id']], "FDR<0.05", "Not Sig")) %>%
  left_join(ttg, by = 'target_id') %>% 
  ggplot(aes(log2FoldChange, -log10(padj))) + 
  geom_point(aes(col=sig)) + 
  scale_color_manual(values=c("red", "black"))

#  coord_cartesian(ylim=c(0,100))

Control - Autism

Child

r1 <- resultsChildCA %>%
  as.tibble()  %>%
  rownames_to_column(var='target_id') %>%
  mutate(sig=ifelse(target_id %in% signifChildCA[['target_id']], "FDR<0.05", "Not Sig")) %>%
  left_join(ttg, by = 'target_id')
r1 %>% ggplot(aes(log2FoldChange, -log10(padj))) + 
  geom_point(aes(col=sig)) + 
  scale_color_manual(values=c("red", "black")) +
#  coord_cartesian(ylim=c(0,10)) + 
  geom_text_repel(data=filter(r1, sig=='FDR<0.05'), aes(label=symbol))

Adult

r1 <- resultsAdultCA %>%
  as.tibble()  %>%
  rownames_to_column(var='target_id') %>%
  mutate(sig=ifelse(target_id %in% signifAdultCA[['target_id']], "FDR<0.05", "Not Sig")) %>%
  left_join(ttg, by = 'target_id')
r1 %>% ggplot(aes(log2FoldChange, -log10(padj))) + 
  geom_point(aes(col=sig)) + 
  scale_color_manual(values=c("red", "black")) +
#  coord_cartesian(ylim=c(0,10)) + 
  geom_text_repel(data=filter(r1, sig=='FDR<0.05'), aes(label=symbol))

Gene lists

Control - Fetal

ChildControl - Fetal

This table shows the 50 most significant genes (out of 9046) differentially expressed between control child samples and fetal samples.

genes_control_fetal_child <- signifChild %>%
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                                 color_tile("red", "white")(signif(log2FoldChange, 3)),
                                 color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type')) %>%
  head(n = 50)
genes_control_fetal_child %>%
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
SOX11	NM_003108.3	-7.340	0	SRY-box 11 (SOX11)	REVIEWED	mRNA
GPC2	NM_152742.2	-5.830	0	glypican 2 (GPC2)	VALIDATED	mRNA
MEX3A	NM_001093725.1	-6.220	0	mex-3 RNA binding family member A (MEX3A)	VALIDATED	mRNA
LMNB1	NM_005573.3	-5.960	0	lamin B1 (LMNB1), transcript variant 1	REVIEWED	mRNA
KIF11	NM_004523.3	-5.030	0	kinesin family member 11 (KIF11)	REVIEWED	mRNA
SOX4	NM_003107.2	-5.720	0	SRY-box 4 (SOX4)	REVIEWED	mRNA
TMSB15A	NM_021992.2	-7.640	0	thymosin beta 15a (TMSB15A)	VALIDATED	mRNA
GPR37L1	XM_011510158.2	6.760	0	PREDICTED: Homo sapiens G protein-coupled receptor 37 like 1 (GPR37L1), transcript variant X1	MODEL	mRNA
LMNB1	NM_001198557.1	-6.260	0	lamin B1 (LMNB1), transcript variant 2	REVIEWED	mRNA
IGIP	NM_001007189.1	3.810	0	IgA inducing protein (IGIP)	VALIDATED	mRNA
OMG	NM_002544.4	7.130	0	oligodendrocyte myelin glycoprotein (OMG)	VALIDATED	mRNA
DCHS1	NM_003737.3	-3.090	0	dachsous cadherin-related 1 (DCHS1)	REVIEWED	mRNA
SMC4	XM_011512312.2	-4.610	0	PREDICTED: Homo sapiens structural maintenance of chromosomes 4 (SMC4), transcript variant X2	MODEL	mRNA
CDK1	NM_001786.4	-7.430	0	cyclin dependent kinase 1 (CDK1), transcript variant 1	REVIEWED	mRNA
BCL2L2	NM_001199839.1	2.420	0	BCL2 like 2 (BCL2L2), transcript variant 2	REVIEWED	mRNA
KNL1	XM_017022432.1	-5.810	0	PREDICTED: Homo sapiens cancer susceptibility candidate 5 (CASC5), transcript variant X1	MODEL	mRNA
SPC25	NM_020675.3	-4.730	0	SPC25, NDC80 kinetochore complex component (SPC25)	REVIEWED	mRNA
PTGDS	NM_000954.5	12.700	0	prostaglandin D2 synthase (PTGDS)	REVIEWED	mRNA
ZBTB47	NM_145166.3	3.470	0	zinc finger and BTB domain containing 47 (ZBTB47)	VALIDATED	mRNA
ASPM	NM_018136.4	-8.930	0	abnormal spindle microtubule assembly (ASPM), transcript variant 1	REVIEWED	mRNA
GPR37L1	NM_004767.3	7.860	0	G protein-coupled receptor 37 like 1 (GPR37L1)	VALIDATED	mRNA
ZBED4	NM_014838.2	-2.540	0	zinc finger BED-type containing 4 (ZBED4)	VALIDATED	mRNA
PLEKHB1	NM_001130035.1	6.330	0	pleckstrin homology domain containing B1 (PLEKHB1), transcript variant 4	VALIDATED	mRNA
UBL3	NM_007106.3	2.270	0	ubiquitin like 3 (UBL3)	VALIDATED	mRNA
S100A1	NM_006271.1	8.070	0	S100 calcium binding protein A1 (S100A1)	REVIEWED	mRNA
TPPP	NM_007030.2	6.500	0	tubulin polymerization promoting protein (TPPP)	VALIDATED	mRNA
CCNB2	NM_004701.3	-7.540	0	cyclin B2 (CCNB2)	REVIEWED	mRNA
LGI3	NM_139278.2	11.100	0	leucine rich repeat LGI family member 3 (LGI3)	VALIDATED	mRNA
HNRNPA0	NM_006805.3	-2.150	0	heterogeneous nuclear ribonucleoprotein A0 (HNRNPA0)	REVIEWED	mRNA
HMGB3	NM_005342.3	-3.370	0	high mobility group box 3 (HMGB3), transcript variant 2	REVIEWED	mRNA
NDC80	NM_006101.2	-7.790	0	NDC80, kinetochore complex component (NDC80)	VALIDATED	mRNA
LMNB2	NM_032737.3	-2.650	0	lamin B2 (LMNB2)	REVIEWED	mRNA
NUSAP1	XM_005254430.4	-9.560	0	PREDICTED: Homo sapiens nucleolar and spindle associated protein 1 (NUSAP1), transcript variant X5	MODEL	mRNA
RRM2	NM_001034.3	-6.310	0	ribonucleotide reductase regulatory subunit M2 (RRM2), transcript variant 2	REVIEWED	mRNA
ZNF365	NM_014951.2	6.400	0	zinc finger protein 365 (ZNF365), transcript variant A	REVIEWED	mRNA
PRNP	NM_001080123.2	4.120	0	prion protein (PRNP), transcript variant 5	REVIEWED	mRNA
TACC3	NM_006342.2	-4.630	0	transforming acidic coiled-coil containing protein 3 (TACC3)	REVIEWED	mRNA
LDLRAD4	NM_001003674.3	8.660	0	low density lipoprotein receptor class A domain containing 4 (LDLRAD4), transcript variant c1	VALIDATED	mRNA
H1F0	NM_005318.3	-2.740	0	H1 histone family member 0 (H1F0)	REVIEWED	mRNA
ENDOD1	NM_015036.2	5.780	0	endonuclease domain containing 1 (ENDOD1)	VALIDATED	mRNA
ESCO2	NM_001017420.2	-6.640	0	establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2)	REVIEWED	mRNA
RCOR2	NM_173587.3	-4.500	0	REST corepressor 2 (RCOR2)	VALIDATED	mRNA
PTTG1	NM_004219.3	-5.350	0	pituitary tumor-transforming 1 (PTTG1), transcript variant 2	REVIEWED	mRNA
FOXO3B	NR_026718.1	-3.080	0	forkhead box O3B pseudogene (FOXO3B)	PROVISIONAL	non-coding RNA
GTSE1	NM_016426.6	-7.010	0	G2 and S-phase expressed 1 (GTSE1)	REVIEWED	mRNA
TF	NM_001063.3	9.490	0	transferrin (TF)	REVIEWED	mRNA
EOMES	NM_001278183.1	-11.800	0	eomesodermin (EOMES), transcript variant 3	REVIEWED	mRNA
QSER1	NM_001076786.2	-2.810	0	glutamine and serine rich 1 (QSER1)	VALIDATED	mRNA
MOBP	NR_003090.2	9.730	0	myelin-associated oligodendrocyte basic protein (MOBP), transcript variant 4	VALIDATED	non-coding RNA
HDAC2	XM_011535788.1	-2.460	0	PREDICTED: Homo sapiens histone deacetylase 2 (HDAC2), transcript variant X1	MODEL	mRNA

AdultControl - Fetal

This table shows the 50 most significant genes (out of 10052) differentially expressed between control adult samples and fetal samples.

genes_control_fetal_adult <- signifAdult %>%
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                                 color_tile("red", "white")(signif(log2FoldChange, 3)),
                                 color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type')) %>%
  head(n = 50)
genes_control_fetal_adult %>%
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
SOX11	NM_003108.3	-8.090	0	SRY-box 11 (SOX11)	REVIEWED	mRNA
MEX3A	NM_001093725.1	-6.570	0	mex-3 RNA binding family member A (MEX3A)	VALIDATED	mRNA
SOX4	NM_003107.2	-6.720	0	SRY-box 4 (SOX4)	REVIEWED	mRNA
GPC2	NM_152742.2	-5.070	0	glypican 2 (GPC2)	VALIDATED	mRNA
LMNB1	NM_005573.3	-6.480	0	lamin B1 (LMNB1), transcript variant 1	REVIEWED	mRNA
KIF11	NM_004523.3	-4.940	0	kinesin family member 11 (KIF11)	REVIEWED	mRNA
DCHS1	NM_003737.3	-3.860	0	dachsous cadherin-related 1 (DCHS1)	REVIEWED	mRNA
GPR37L1	XM_011510158.2	6.440	0	PREDICTED: Homo sapiens G protein-coupled receptor 37 like 1 (GPR37L1), transcript variant X1	MODEL	mRNA
IGIP	NM_001007189.1	4.090	0	IgA inducing protein (IGIP)	VALIDATED	mRNA
LMNB1	NM_001198557.1	-6.670	0	lamin B1 (LMNB1), transcript variant 2	REVIEWED	mRNA
OMG	NM_002544.4	7.970	0	oligodendrocyte myelin glycoprotein (OMG)	VALIDATED	mRNA
TMSB15A	NM_021992.2	-8.510	0	thymosin beta 15a (TMSB15A)	VALIDATED	mRNA
SMC4	XM_011512312.2	-5.010	0	PREDICTED: Homo sapiens structural maintenance of chromosomes 4 (SMC4), transcript variant X2	MODEL	mRNA
HNRNPA0	NM_006805.3	-2.570	0	heterogeneous nuclear ribonucleoprotein A0 (HNRNPA0)	REVIEWED	mRNA
BCL2L2	NM_001199839.1	2.550	0	BCL2 like 2 (BCL2L2), transcript variant 2	REVIEWED	mRNA
HMGB3	NM_005342.3	-4.070	0	high mobility group box 3 (HMGB3), transcript variant 2	REVIEWED	mRNA
UBL3	NM_007106.3	2.620	0	ubiquitin like 3 (UBL3)	VALIDATED	mRNA
S100A1	NM_006271.1	9.270	0	S100 calcium binding protein A1 (S100A1)	REVIEWED	mRNA
ENDOD1	NM_015036.2	7.060	0	endonuclease domain containing 1 (ENDOD1)	VALIDATED	mRNA
PTGDS	NM_000954.5	13.000	0	prostaglandin D2 synthase (PTGDS)	REVIEWED	mRNA
PLEKHB1	NM_001130035.1	6.890	0	pleckstrin homology domain containing B1 (PLEKHB1), transcript variant 4	VALIDATED	mRNA
TPPP	NM_007030.2	7.110	0	tubulin polymerization promoting protein (TPPP)	VALIDATED	mRNA
CDK1	NM_001786.4	-7.230	0	cyclin dependent kinase 1 (CDK1), transcript variant 1	REVIEWED	mRNA
LMNB2	NM_032737.3	-2.960	0	lamin B2 (LMNB2)	REVIEWED	mRNA
DBN1	NM_004395.3	-4.620	0	drebrin 1 (DBN1), transcript variant 1	REVIEWED	mRNA
GPR37L1	NM_004767.3	8.010	0	G protein-coupled receptor 37 like 1 (GPR37L1)	VALIDATED	mRNA
ASPM	NM_018136.4	-8.210	0	abnormal spindle microtubule assembly (ASPM), transcript variant 1	REVIEWED	mRNA
KNL1	XM_017022432.1	-5.530	0	PREDICTED: Homo sapiens cancer susceptibility candidate 5 (CASC5), transcript variant X1	MODEL	mRNA
LGI3	NM_139278.2	11.800	0	leucine rich repeat LGI family member 3 (LGI3)	VALIDATED	mRNA
TF	NM_001063.3	11.000	0	transferrin (TF)	REVIEWED	mRNA
ZBTB47	NM_145166.3	3.370	0	zinc finger and BTB domain containing 47 (ZBTB47)	VALIDATED	mRNA
LDLRAD4	NM_001003674.3	9.640	0	low density lipoprotein receptor class A domain containing 4 (LDLRAD4), transcript variant c1	VALIDATED	mRNA
MOBP	NR_003090.2	11.100	0	myelin-associated oligodendrocyte basic protein (MOBP), transcript variant 4	VALIDATED	non-coding RNA
DESI1	NM_015704.2	1.890	0	desumoylating isopeptidase 1 (DESI1)	VALIDATED	mRNA
TACC3	NM_006342.2	-5.510	0	transforming acidic coiled-coil containing protein 3 (TACC3)	REVIEWED	mRNA
NONO	NM_007363.4	-2.180	0	non-POU domain containing octamer binding (NONO), transcript variant 2	REVIEWED	mRNA
ENPP4	NM_014936.4	5.480	0	ectonucleotide pyrophosphatase/phosphodiesterase 4 (putative) (ENPP4)	VALIDATED	mRNA
RRM2	NM_001034.3	-7.040	0	ribonucleotide reductase regulatory subunit M2 (RRM2), transcript variant 2	REVIEWED	mRNA
H1F0	NM_005318.3	-2.950	0	H1 histone family member 0 (H1F0)	REVIEWED	mRNA
ZNF365	NM_014951.2	6.680	0	zinc finger protein 365 (ZNF365), transcript variant A	REVIEWED	mRNA
SEC14L5	NM_014692.1	8.960	0	SEC14 like lipid binding 5 (SEC14L5)	VALIDATED	mRNA
SPC25	NM_020675.3	-3.830	0	SPC25, NDC80 kinetochore complex component (SPC25)	REVIEWED	mRNA
PRNP	NM_001080123.2	4.250	0	prion protein (PRNP), transcript variant 5	REVIEWED	mRNA
CNP	NM_033133.4	6.920	0	2’,3’-cyclic nucleotide 3’ phosphodiesterase (CNP), transcript variant 1	VALIDATED	mRNA
LRRC3B	NM_001317808.1	-4.470	0	leucine rich repeat containing 3B (LRRC3B), transcript variant 2	REVIEWED	mRNA
RBM15B	NM_013286.4	-2.090	0	RNA binding motif protein 15B (RBM15B)	VALIDATED	mRNA
MEX3B	NM_032246.4	-5.070	0	mex-3 RNA binding family member B (MEX3B)	REVIEWED	mRNA
HNRNPA1	NM_002136.3	-2.510	0	heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1), transcript variant 1	REVIEWED	mRNA
TMEM151A	NM_153266.3	7.140	0	transmembrane protein 151A (TMEM151A)	VALIDATED	mRNA
EFNB1	NM_004429.4	-4.480	0	ephrin B1 (EFNB1)	REVIEWED	mRNA

Autism - Fetal

ChildAutism - Fetal

This table shows the 50 most significant genes (out of 10335) differentially expressed between autism child samples and fetal samples.

genes_autism_fetal_child <- signifChildA %>%
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                                 color_tile("red", "white")(signif(log2FoldChange, 3)),
                                 color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type')) %>%
  head(n = 50)
genes_autism_fetal_child %>%
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
SOX11	NM_003108.3	-6.800	0	SRY-box 11 (SOX11)	REVIEWED	mRNA
GPC2	NM_152742.2	-5.920	0	glypican 2 (GPC2)	VALIDATED	mRNA
MEX3A	NM_001093725.1	-6.190	0	mex-3 RNA binding family member A (MEX3A)	VALIDATED	mRNA
LMNB1	NM_005573.3	-6.230	0	lamin B1 (LMNB1), transcript variant 1	REVIEWED	mRNA
KIF11	NM_004523.3	-5.560	0	kinesin family member 11 (KIF11)	REVIEWED	mRNA
SOX4	NM_003107.2	-5.550	0	SRY-box 4 (SOX4)	REVIEWED	mRNA
GPR37L1	XM_011510158.2	6.930	0	PREDICTED: Homo sapiens G protein-coupled receptor 37 like 1 (GPR37L1), transcript variant X1	MODEL	mRNA
DCHS1	NM_003737.3	-3.590	0	dachsous cadherin-related 1 (DCHS1)	REVIEWED	mRNA
TMSB15A	NM_021992.2	-7.970	0	thymosin beta 15a (TMSB15A)	VALIDATED	mRNA
SMC4	XM_011512312.2	-5.330	0	PREDICTED: Homo sapiens structural maintenance of chromosomes 4 (SMC4), transcript variant X2	MODEL	mRNA
LMNB1	NM_001198557.1	-6.110	0	lamin B1 (LMNB1), transcript variant 2	REVIEWED	mRNA
IGIP	NM_001007189.1	3.850	0	IgA inducing protein (IGIP)	VALIDATED	mRNA
OMG	NM_002544.4	7.260	0	oligodendrocyte myelin glycoprotein (OMG)	VALIDATED	mRNA
NUSAP1	XM_005254430.4	-7.850	0	PREDICTED: Homo sapiens nucleolar and spindle associated protein 1 (NUSAP1), transcript variant X5	MODEL	mRNA
BCL2L2	NM_001199839.1	2.450	0	BCL2 like 2 (BCL2L2), transcript variant 2	REVIEWED	mRNA
KNL1	XM_017022432.1	-6.280	0	PREDICTED: Homo sapiens cancer susceptibility candidate 5 (CASC5), transcript variant X1	MODEL	mRNA
ZBED4	NM_014838.2	-2.640	0	zinc finger BED-type containing 4 (ZBED4)	VALIDATED	mRNA
CDK1	NM_001786.4	-7.540	0	cyclin dependent kinase 1 (CDK1), transcript variant 1	REVIEWED	mRNA
QSER1	NM_001076786.2	-3.340	0	glutamine and serine rich 1 (QSER1)	VALIDATED	mRNA
ZBTB47	NM_145166.3	3.440	0	zinc finger and BTB domain containing 47 (ZBTB47)	VALIDATED	mRNA
SPC25	NM_020675.3	-5.140	0	SPC25, NDC80 kinetochore complex component (SPC25)	REVIEWED	mRNA
ELAVL2	NM_001351477.1	-8.900	0	ELAV like RNA binding protein 2 (ELAVL2), transcript variant 26	REVIEWED	mRNA
FOXO3B	NR_026718.1	-3.680	0	forkhead box O3B pseudogene (FOXO3B)	PROVISIONAL	non-coding RNA
S100A1	NM_006271.1	8.730	0	S100 calcium binding protein A1 (S100A1)	REVIEWED	mRNA
GPR37L1	NM_004767.3	7.990	0	G protein-coupled receptor 37 like 1 (GPR37L1)	VALIDATED	mRNA
HNRNPA0	NM_006805.3	-2.190	0	heterogeneous nuclear ribonucleoprotein A0 (HNRNPA0)	REVIEWED	mRNA
PTGDS	NM_000954.5	12.300	0	prostaglandin D2 synthase (PTGDS)	REVIEWED	mRNA
ASPM	NM_018136.4	-9.240	0	abnormal spindle microtubule assembly (ASPM), transcript variant 1	REVIEWED	mRNA
PLEKHB1	NM_001130035.1	6.220	0	pleckstrin homology domain containing B1 (PLEKHB1), transcript variant 4	VALIDATED	mRNA
ATP1B1	NM_001677.3	4.440	0	ATPase Na+/K+ transporting subunit beta 1 (ATP1B1)	REVIEWED	mRNA
RRM2	NM_001034.3	-6.710	0	ribonucleotide reductase regulatory subunit M2 (RRM2), transcript variant 2	REVIEWED	mRNA
HNRNPA1	NM_002136.3	-2.530	0	heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1), transcript variant 1	REVIEWED	mRNA
ZNF365	NM_014951.2	6.630	0	zinc finger protein 365 (ZNF365), transcript variant A	REVIEWED	mRNA
CCNB2	NM_004701.3	-6.730	0	cyclin B2 (CCNB2)	REVIEWED	mRNA
PINK1	NM_032409.2	3.110	0	PTEN induced putative kinase 1 (PINK1)	REVIEWED	mRNA
PEBP1	NM_002567.3	2.430	0	phosphatidylethanolamine binding protein 1 (PEBP1)	REVIEWED	mRNA
TPPP	NM_007030.2	6.210	0	tubulin polymerization promoting protein (TPPP)	VALIDATED	mRNA
EOMES	NM_001278183.1	-10.600	0	eomesodermin (EOMES), transcript variant 3	REVIEWED	mRNA
TACC3	NM_006342.2	-4.870	0	transforming acidic coiled-coil containing protein 3 (TACC3)	REVIEWED	mRNA
PRNP	NM_001080123.2	4.100	0	prion protein (PRNP), transcript variant 5	REVIEWED	mRNA
H1F0	NM_005318.3	-2.780	0	H1 histone family member 0 (H1F0)	REVIEWED	mRNA
LMNB2	NM_032737.3	-2.530	0	lamin B2 (LMNB2)	REVIEWED	mRNA
SELENOT	NM_016275.4	1.940	0	selenoprotein T (SELENOT)	REVIEWED	mRNA
NDC80	NM_006101.2	-7.550	0	NDC80, kinetochore complex component (NDC80)	VALIDATED	mRNA
LGI3	NM_139278.2	11.100	0	leucine rich repeat LGI family member 3 (LGI3)	VALIDATED	mRNA
UBL3	NM_007106.3	2.080	0	ubiquitin like 3 (UBL3)	VALIDATED	mRNA
DESI1	NM_015704.2	1.740	0	desumoylating isopeptidase 1 (DESI1)	VALIDATED	mRNA
FBXO2	NM_012168.5	9.860	0	F-box protein 2 (FBXO2)	REVIEWED	mRNA
HCN2	NM_001194.3	7.630	0	hyperpolarization activated cyclic nucleotide gated potassium and sodium channel 2 (HCN2)	REVIEWED	mRNA
TF	NM_001063.3	9.540	0	transferrin (TF)	REVIEWED	mRNA

AdultAutism - Fetal

This table shows the 50 most significant genes (out of 9870) differentially expressed between autism adult samples and fetal samples.

genes_autism_fetal_adult <- signifAdultA %>%
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                                 color_tile("red", "white")(signif(log2FoldChange, 3)),
                                 color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type')) %>%
  head(n = 50)
genes_autism_fetal_adult %>%
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
SOX11	NM_003108.3	-7.260	0	SRY-box 11 (SOX11)	REVIEWED	mRNA
GPC2	NM_152742.2	-5.410	0	glypican 2 (GPC2)	VALIDATED	mRNA
MEX3A	NM_001093725.1	-6.540	0	mex-3 RNA binding family member A (MEX3A)	VALIDATED	mRNA
SOX4	NM_003107.2	-6.700	0	SRY-box 4 (SOX4)	REVIEWED	mRNA
LMNB1	NM_005573.3	-6.380	0	lamin B1 (LMNB1), transcript variant 1	REVIEWED	mRNA
KIF11	NM_004523.3	-5.130	0	kinesin family member 11 (KIF11)	REVIEWED	mRNA
IGIP	NM_001007189.1	4.080	0	IgA inducing protein (IGIP)	VALIDATED	mRNA
GPR37L1	XM_011510158.2	6.390	0	PREDICTED: Homo sapiens G protein-coupled receptor 37 like 1 (GPR37L1), transcript variant X1	MODEL	mRNA
LMNB1	NM_001198557.1	-6.580	0	lamin B1 (LMNB1), transcript variant 2	REVIEWED	mRNA
SMC4	XM_011512312.2	-5.260	0	PREDICTED: Homo sapiens structural maintenance of chromosomes 4 (SMC4), transcript variant X2	MODEL	mRNA
DCHS1	NM_003737.3	-3.400	0	dachsous cadherin-related 1 (DCHS1)	REVIEWED	mRNA
OMG	NM_002544.4	7.530	0	oligodendrocyte myelin glycoprotein (OMG)	VALIDATED	mRNA
HNRNPA0	NM_006805.3	-2.700	0	heterogeneous nuclear ribonucleoprotein A0 (HNRNPA0)	REVIEWED	mRNA
HMGB3	NM_005342.3	-4.080	0	high mobility group box 3 (HMGB3), transcript variant 2	REVIEWED	mRNA
S100A1	NM_006271.1	8.930	0	S100 calcium binding protein A1 (S100A1)	REVIEWED	mRNA
BCL2L2	NM_001199839.1	2.410	0	BCL2 like 2 (BCL2L2), transcript variant 2	REVIEWED	mRNA
PTGDS	NM_000954.5	12.800	0	prostaglandin D2 synthase (PTGDS)	REVIEWED	mRNA
CDK1	NM_001786.4	-7.640	0	cyclin dependent kinase 1 (CDK1), transcript variant 1	REVIEWED	mRNA
TPPP	NM_007030.2	7.020	0	tubulin polymerization promoting protein (TPPP)	VALIDATED	mRNA
TMSB15A	NM_021992.2	-9.470	0	thymosin beta 15a (TMSB15A)	VALIDATED	mRNA
ASPM	NM_018136.4	-8.280	0	abnormal spindle microtubule assembly (ASPM), transcript variant 1	REVIEWED	mRNA
PLEKHB1	NM_001130035.1	6.690	0	pleckstrin homology domain containing B1 (PLEKHB1), transcript variant 4	VALIDATED	mRNA
KNL1	XM_017022432.1	-5.440	0	PREDICTED: Homo sapiens cancer susceptibility candidate 5 (CASC5), transcript variant X1	MODEL	mRNA
H1F0	NM_005318.3	-3.110	0	H1 histone family member 0 (H1F0)	REVIEWED	mRNA
LMNB2	NM_032737.3	-2.830	0	lamin B2 (LMNB2)	REVIEWED	mRNA
ZBTB47	NM_145166.3	3.370	0	zinc finger and BTB domain containing 47 (ZBTB47)	VALIDATED	mRNA
GPR37L1	NM_004767.3	7.780	0	G protein-coupled receptor 37 like 1 (GPR37L1)	VALIDATED	mRNA
LGI3	NM_139278.2	11.600	0	leucine rich repeat LGI family member 3 (LGI3)	VALIDATED	mRNA
SPC25	NM_020675.3	-4.110	0	SPC25, NDC80 kinetochore complex component (SPC25)	REVIEWED	mRNA
ENDOD1	NM_015036.2	6.340	0	endonuclease domain containing 1 (ENDOD1)	VALIDATED	mRNA
HNRNPA1	NM_002136.3	-2.630	0	heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1), transcript variant 1	REVIEWED	mRNA
UBL3	NM_007106.3	2.250	0	ubiquitin like 3 (UBL3)	VALIDATED	mRNA
TF	NM_001063.3	10.400	0	transferrin (TF)	REVIEWED	mRNA
NONO	NM_007363.4	-2.120	0	non-POU domain containing octamer binding (NONO), transcript variant 2	REVIEWED	mRNA
TACC3	NM_006342.2	-5.050	0	transforming acidic coiled-coil containing protein 3 (TACC3)	REVIEWED	mRNA
MOBP	NR_003090.2	10.500	0	myelin-associated oligodendrocyte basic protein (MOBP), transcript variant 4	VALIDATED	non-coding RNA
LDLRAD4	NM_001003674.3	8.970	0	low density lipoprotein receptor class A domain containing 4 (LDLRAD4), transcript variant c1	VALIDATED	mRNA
PRNP	NM_001080123.2	4.180	0	prion protein (PRNP), transcript variant 5	REVIEWED	mRNA
DESI1	NM_015704.2	1.800	0	desumoylating isopeptidase 1 (DESI1)	VALIDATED	mRNA
ZNF365	NM_014951.2	6.350	0	zinc finger protein 365 (ZNF365), transcript variant A	REVIEWED	mRNA
PTTG1	NM_004219.3	-6.200	0	pituitary tumor-transforming 1 (PTTG1), transcript variant 2	REVIEWED	mRNA
ZBED4	NM_014838.2	-2.290	0	zinc finger BED-type containing 4 (ZBED4)	VALIDATED	mRNA
DCAF7	NM_005828.4	-1.530	0	DDB1 and CUL4 associated factor 7 (DCAF7), transcript variant 1	REVIEWED	mRNA
TMEM151A	NM_153266.3	6.940	0	transmembrane protein 151A (TMEM151A)	VALIDATED	mRNA
RRM2	NM_001034.3	-6.070	0	ribonucleotide reductase regulatory subunit M2 (RRM2), transcript variant 2	REVIEWED	mRNA
MEX3B	NM_032246.4	-4.780	0	mex-3 RNA binding family member B (MEX3B)	REVIEWED	mRNA
SEC14L5	NM_014692.1	8.400	0	SEC14 like lipid binding 5 (SEC14L5)	VALIDATED	mRNA
HBG2	NM_000184.2	-10.500	0	hemoglobin subunit gamma 2 (HBG2)	REVIEWED	mRNA
RBM15B	NM_013286.4	-1.980	0	RNA binding motif protein 15B (RBM15B)	VALIDATED	mRNA
CCNB2	NM_004701.3	-5.630	0	cyclin B2 (CCNB2)	REVIEWED	mRNA

Control - Autism

Child

This table shows the 21 most significant genes differentially expressed between control child samples and autism child samples.

genes_control_autism_child <- signifChildCA %>%
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                  color_tile("red", "white")(signif(log2FoldChange, 3)),
                  color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type')) %>%
  head(n = 50)
genes_control_autism_child %>% 
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
ZSCAN25	NM_001350984.1	4.94	0.0040905	zinc finger and SCAN domain containing 25 (ZSCAN25), transcript variant 7	VALIDATED	mRNA
PIK3R3	NM_003629.3	6.77	0.0041154	phosphoinositide-3-kinase regulatory subunit 3 (PIK3R3), transcript variant 1	REVIEWED	mRNA
BUB1B	NM_001211.5	3.27	0.0056204	BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B)	REVIEWED	mRNA
ZFP64	XM_005260449.1	-6.20	0.0165635	PREDICTED: Homo sapiens ZFP64 zinc finger protein (ZFP64), transcript variant X6	MODEL	mRNA
TTR	NM_000371.3	6.99	0.0229338	transthyretin (TTR)	REVIEWED	mRNA
ZNF160	NM_001322136.1	-5.58	0.0229338	zinc finger protein 160 (ZNF160), transcript variant 12	REVIEWED	mRNA
EXOC1	NM_018261.3	-5.82	0.0229338	exocyst complex component 1 (EXOC1), transcript variant 1	REVIEWED	mRNA
RGL1	XM_017000756.1	5.76	0.0229338	PREDICTED: Homo sapiens ral guanine nucleotide dissociation stimulator like 1 (RGL1), transcript variant X3	MODEL	mRNA
PLPP5	XM_017013905.1	4.74	0.0229338	PREDICTED: Homo sapiens phospholipid phosphatase 5 (PLPP5), transcript variant X10	MODEL	mRNA
TCF4	XM_017025937.1	6.29	0.0229338	PREDICTED: Homo sapiens transcription factor 4 (TCF4), transcript variant X5	MODEL	mRNA
LRRC75A-AS1	NR_027159.1	-5.32	0.0233528	LRRC75A antisense RNA 1 (LRRC75A-AS1), transcript variant 3	PREDICTED	long non-coding RNA
RAB11FIP1	NM_025151.4	2.15	0.0261247	RAB11 family interacting protein 1 (RAB11FIP1), transcript variant 1	VALIDATED	mRNA
TAGAP	NM_152133.2	5.47	0.0261247	T-cell activation RhoGTPase activating protein (TAGAP), transcript variant 1	REVIEWED	mRNA
YPEL2	XM_017024621.1	5.93	0.0261247	PREDICTED: Homo sapiens yippee like 2 (YPEL2), transcript variant X1	MODEL	mRNA
OSBPL2	XM_017028170.1	5.90	0.0261247	PREDICTED: Homo sapiens oxysterol binding protein like 2 (OSBPL2), transcript variant X8	MODEL	mRNA
SFRP1	NM_003012.4	2.10	0.0317043	secreted frizzled related protein 1 (SFRP1)	REVIEWED	mRNA
MCM7	NM_001278595.1	-5.36	0.0430600	minichromosome maintenance complex component 7 (MCM7), transcript variant 3	REVIEWED	mRNA
A4GALT	XM_005261647.2	-6.00	0.0490829	PREDICTED: Homo sapiens alpha 1,4-galactosyltransferase (A4GALT), transcript variant X4	MODEL	mRNA
SLC5A5	XM_011528194.2	4.25	0.0490829	PREDICTED: Homo sapiens solute carrier family 5 member 5 (SLC5A5), transcript variant X4	MODEL	mRNA
LOC105377135	XM_011529809.2	-3.62	0.0490829	PREDICTED: Homo sapiens transcription initiation factor TFIID subunit 4-like (LOC105377135), transcript variant X2	MODEL	mRNA
ADAM28	XM_011544371.2	2.25	0.0490829	PREDICTED: Homo sapiens ADAM metallopeptidase domain 28 (ADAM28), transcript variant X14	MODEL	mRNA

Adult

This table shows the 44 most significant genes differentially expressed between control adult samples and autism adult samples.

genes_control_autism_adult <- signifAdultCA %>%
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                  color_tile("red", "white")(signif(log2FoldChange, 3)),
                  color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type')) %>%
  head(n = 50)
genes_control_autism_adult %>%
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
FHOD3	XM_005258355.1	-7.62	0.0000000	PREDICTED: Homo sapiens formin homology 2 domain containing 3 (FHOD3), transcript variant X13	MODEL	mRNA
LOC107986119	XR_001750192.1	7.26	0.0000019	PREDICTED: Homo sapiens uncharacterized LOC107986119 (LOC107986119), transcript variant X1	MODEL	ncRNA
DLGAP1	NM_001242764.1	-6.86	0.0000096	DLG associated protein 1 (DLGAP1), transcript variant 6	VALIDATED	mRNA
AFF2	NM_001170628.1	-5.07	0.0002494	AF4/FMR2 family member 2 (AFF2), transcript variant 6	REVIEWED	mRNA
CSE1L	NR_045796.1	-6.60	0.0012660	chromosome segregation 1 like (CSE1L), transcript variant 3	REVIEWED	non-coding RNA
PRKCE	XM_006712050.3	-6.74	0.0012660	PREDICTED: Homo sapiens protein kinase C epsilon (PRKCE), transcript variant X17	MODEL	mRNA
LOC107985374	XR_001735766.1	6.06	0.0012660	PREDICTED: Homo sapiens uncharacterized LOC107985374 (LOC107985374)	MODEL	ncRNA
ZNF821	XM_017023411.1	-5.92	0.0019919	PREDICTED: Homo sapiens zinc finger protein 821 (ZNF821), transcript variant X4	MODEL	mRNA
GRIA1	XM_017009393.1	-6.30	0.0031578	PREDICTED: Homo sapiens glutamate ionotropic receptor AMPA type subunit 1 (GRIA1), transcript variant X3	MODEL	mRNA
EIF3C	XM_017023814.1	6.78	0.0031578	PREDICTED: Homo sapiens eukaryotic translation initiation factor 3 subunit C (EIF3C), transcript variant X1	MODEL	mRNA
CHD3	XM_017024066.1	6.18	0.0031578	PREDICTED: Homo sapiens chromodomain helicase DNA binding protein 3 (CHD3), transcript variant X11	MODEL	mRNA
SCAF11	XM_017020221.1	6.10	0.0039586	PREDICTED: Homo sapiens SR-related CTD associated factor 11 (SCAF11), transcript variant X8	MODEL	mRNA
ATAD2	XM_011516996.2	-2.83	0.0040110	PREDICTED: Homo sapiens ATPase family, AAA domain containing 2 (ATAD2), transcript variant X3	MODEL	mRNA
LSM6	XR_001741100.1	5.61	0.0040110	PREDICTED: Homo sapiens LSM6 homolog, U6 small nuclear RNA and mRNA degradation associated (LSM6), transcript variant X3	MODEL	misc_RNA
PASK	XM_011510835.1	-6.48	0.0047899	PREDICTED: Homo sapiens PAS domain containing serine/threonine kinase (PASK), transcript variant X14	MODEL	mRNA
CGN	XM_005245365.4	7.10	0.0082430	PREDICTED: Homo sapiens cingulin (CGN), transcript variant X1	MODEL	mRNA
WNK2	XM_011518936.2	-5.69	0.0083023	PREDICTED: Homo sapiens WNK lysine deficient protein kinase 2 (WNK2), transcript variant X22	MODEL	mRNA
OGT	XM_017029907.1	-6.15	0.0099568	PREDICTED: Homo sapiens O-linked N-acetylglucosamine (GlcNAc) transferase (OGT), transcript variant X1	MODEL	mRNA
NCAPG2	NM_017760.6	6.04	0.0107996	non-SMC condensin II complex subunit G2 (NCAPG2), transcript variant 1	REVIEWED	mRNA
LIN52	NM_001024674.2	5.67	0.0142319	lin-52 DREAM MuvB core complex component (LIN52)	VALIDATED	mRNA
TNFRSF10D	NM_003840.4	-2.24	0.0219061	TNF receptor superfamily member 10d (TNFRSF10D)	REVIEWED	mRNA
STARD8	XM_011531069.2	6.23	0.0219061	PREDICTED: Homo sapiens StAR related lipid transfer domain containing 8 (STARD8), transcript variant X2	MODEL	mRNA
UBA1	XM_017029780.1	-5.18	0.0219061	PREDICTED: Homo sapiens ubiquitin like modifier activating enzyme 1 (UBA1), transcript variant X6	MODEL	mRNA
LOC105372462	XR_919753.2	-5.10	0.0229630	PREDICTED: Homo sapiens uncharacterized LOC105372462 (LOC105372462), transcript variant X2	MODEL	ncRNA
ACVR1C	NM_001111033.1	-6.01	0.0264439	activin A receptor type 1C (ACVR1C), transcript variant 4	VALIDATED	mRNA
LOC105369351	XR_950214.2	-5.71	0.0268767	PREDICTED: Homo sapiens uncharacterized LOC105369351 (LOC105369351), transcript variant X2	MODEL	ncRNA
SPAST	NM_014946.3	-4.08	0.0313178	spastin (SPAST), transcript variant 1	REVIEWED	mRNA
CEP170	XM_011544343.2	5.88	0.0313178	PREDICTED: Homo sapiens centrosomal protein 170 (CEP170), transcript variant X23	MODEL	mRNA
TREM2	XM_006715116.3	5.06	0.0355194	PREDICTED: Homo sapiens triggering receptor expressed on myeloid cells 2 (TREM2), transcript variant X1	MODEL	mRNA
SELE	NM_000450.2	-4.48	0.0357277	selectin E (SELE)	REVIEWED	mRNA
GADD45B	NM_015675.3	-3.43	0.0357277	growth arrest and DNA damage inducible beta (GADD45B)	REVIEWED	mRNA
TPX2	XM_011528697.2	4.54	0.0381066	PREDICTED: Homo sapiens TPX2, microtubule nucleation factor (TPX2), transcript variant X1	MODEL	mRNA
PITPNM3	NM_001165966.1	-6.13	0.0399315	PITPNM family member 3 (PITPNM3), transcript variant 2	REVIEWED	mRNA
C12orf57	NM_001301834.1	-5.14	0.0399315	chromosome 12 open reading frame 57 (C12orf57), transcript variant 2	REVIEWED	mRNA
LINC01896	XR_935681.2	-5.38	0.0399315	PREDICTED: Homo sapiens uncharacterized LOC645321 (LOC645321), transcript variant X3	MODEL	ncRNA
LINC01896	XR_952162.2	-5.38	0.0399315	PREDICTED: Homo sapiens uncharacterized LOC645321 (LOC645321), transcript variant X3	MODEL	ncRNA
LINC01896	XR_952517.2	-5.38	0.0399315	PREDICTED: Homo sapiens uncharacterized LOC645321 (LOC645321), transcript variant X3	MODEL	ncRNA
NFX1	XR_001746314.1	-5.34	0.0408559	PREDICTED: Homo sapiens nuclear transcription factor, X-box binding 1 (NFX1), transcript variant X5	MODEL	misc_RNA
FHL2	NM_001318896.1	-4.86	0.0432861	four and a half LIM domains 2 (FHL2), transcript variant 8	REVIEWED	mRNA
MIR100HG	NR_137195.1	-5.62	0.0432861	mir-100-let-7a-2-mir-125b-1 cluster host gene (MIR100HG), transcript variant 22	REVIEWED	long non-coding RNA
KIAA1324	XM_011541825.1	-5.33	0.0432861	PREDICTED: Homo sapiens KIAA1324 (KIAA1324), transcript variant X1	MODEL	mRNA
NCKAP5	XM_017003980.1	-5.45	0.0432861	PREDICTED: Homo sapiens NCK associated protein 5 (NCKAP5), transcript variant X18	MODEL	mRNA
LOC105370848	XR_001733412.1	6.09	0.0432861	PREDICTED: Homo sapiens uncharacterized LOC105370848 (LOC105370848)	MODEL	ncRNA
ANO2	XM_011520978.2	-4.42	0.0457225	PREDICTED: Homo sapiens anoctamin 2 (ANO2), transcript variant X5	MODEL	mRNA
LOC101929097	XM_005259695.2	-6.17	0.0491767	PREDICTED: Homo sapiens uncharacterized LOC101929097 (LOC101929097)	MODEL	mRNA
SOCS3	NM_003955.4	-3.89	0.0496000	suppressor of cytokine signaling 3 (SOCS3)	REVIEWED	mRNA

Developmental Control - Autism

Child

This table shows the 16 most significant genes differentially expressed between control child samples and autism child samples AND different between control child samples and fetal samples.

genes_dev_control_autism_child <- signifChildCA %>%
  filter(target_id %in% devChildCA) %>%
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                  color_tile("red", "white")(signif(log2FoldChange, 3)),
                  color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type'))
genes_dev_control_autism_child %>%
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
BUB1B	NM_001211.5	3.27	0.0056204	BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B)	REVIEWED	mRNA
ZFP64	XM_005260449.1	-6.20	0.0165635	PREDICTED: Homo sapiens ZFP64 zinc finger protein (ZFP64), transcript variant X6	MODEL	mRNA
TTR	NM_000371.3	6.99	0.0229338	transthyretin (TTR)	REVIEWED	mRNA
ZNF160	NM_001322136.1	-5.58	0.0229338	zinc finger protein 160 (ZNF160), transcript variant 12	REVIEWED	mRNA
EXOC1	NM_018261.3	-5.82	0.0229338	exocyst complex component 1 (EXOC1), transcript variant 1	REVIEWED	mRNA
RGL1	XM_017000756.1	5.76	0.0229338	PREDICTED: Homo sapiens ral guanine nucleotide dissociation stimulator like 1 (RGL1), transcript variant X3	MODEL	mRNA
PLPP5	XM_017013905.1	4.74	0.0229338	PREDICTED: Homo sapiens phospholipid phosphatase 5 (PLPP5), transcript variant X10	MODEL	mRNA
LRRC75A-AS1	NR_027159.1	-5.32	0.0233528	LRRC75A antisense RNA 1 (LRRC75A-AS1), transcript variant 3	PREDICTED	long non-coding RNA
RAB11FIP1	NM_025151.4	2.15	0.0261247	RAB11 family interacting protein 1 (RAB11FIP1), transcript variant 1	VALIDATED	mRNA
TAGAP	NM_152133.2	5.47	0.0261247	T-cell activation RhoGTPase activating protein (TAGAP), transcript variant 1	REVIEWED	mRNA
SFRP1	NM_003012.4	2.10	0.0317043	secreted frizzled related protein 1 (SFRP1)	REVIEWED	mRNA
MCM7	NM_001278595.1	-5.36	0.0430600	minichromosome maintenance complex component 7 (MCM7), transcript variant 3	REVIEWED	mRNA
A4GALT	XM_005261647.2	-6.00	0.0490829	PREDICTED: Homo sapiens alpha 1,4-galactosyltransferase (A4GALT), transcript variant X4	MODEL	mRNA
SLC5A5	XM_011528194.2	4.25	0.0490829	PREDICTED: Homo sapiens solute carrier family 5 member 5 (SLC5A5), transcript variant X4	MODEL	mRNA
LOC105377135	XM_011529809.2	-3.62	0.0490829	PREDICTED: Homo sapiens transcription initiation factor TFIID subunit 4-like (LOC105377135), transcript variant X2	MODEL	mRNA
ADAM28	XM_011544371.2	2.25	0.0490829	PREDICTED: Homo sapiens ADAM metallopeptidase domain 28 (ADAM28), transcript variant X14	MODEL	mRNA

Adult

This table shows the 35 most significant genes differentially expressed between control adult samples and autism adult samples AND different between control adult samples and fetal samples.

genes_dev_control_autism_adult <- signifAdultCA %>%
  filter(target_id %in% devAdultCA) %>%
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                  color_tile("red", "white")(signif(log2FoldChange, 3)),
                  color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type'))
genes_dev_control_autism_adult %>%
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
FHOD3	XM_005258355.1	-7.62	0.0000000	PREDICTED: Homo sapiens formin homology 2 domain containing 3 (FHOD3), transcript variant X13	MODEL	mRNA
LOC107986119	XR_001750192.1	7.26	0.0000019	PREDICTED: Homo sapiens uncharacterized LOC107986119 (LOC107986119), transcript variant X1	MODEL	ncRNA
DLGAP1	NM_001242764.1	-6.86	0.0000096	DLG associated protein 1 (DLGAP1), transcript variant 6	VALIDATED	mRNA
AFF2	NM_001170628.1	-5.07	0.0002494	AF4/FMR2 family member 2 (AFF2), transcript variant 6	REVIEWED	mRNA
CSE1L	NR_045796.1	-6.60	0.0012660	chromosome segregation 1 like (CSE1L), transcript variant 3	REVIEWED	non-coding RNA
PRKCE	XM_006712050.3	-6.74	0.0012660	PREDICTED: Homo sapiens protein kinase C epsilon (PRKCE), transcript variant X17	MODEL	mRNA
LOC107985374	XR_001735766.1	6.06	0.0012660	PREDICTED: Homo sapiens uncharacterized LOC107985374 (LOC107985374)	MODEL	ncRNA
ZNF821	XM_017023411.1	-5.92	0.0019919	PREDICTED: Homo sapiens zinc finger protein 821 (ZNF821), transcript variant X4	MODEL	mRNA
GRIA1	XM_017009393.1	-6.30	0.0031578	PREDICTED: Homo sapiens glutamate ionotropic receptor AMPA type subunit 1 (GRIA1), transcript variant X3	MODEL	mRNA
EIF3C	XM_017023814.1	6.78	0.0031578	PREDICTED: Homo sapiens eukaryotic translation initiation factor 3 subunit C (EIF3C), transcript variant X1	MODEL	mRNA
CHD3	XM_017024066.1	6.18	0.0031578	PREDICTED: Homo sapiens chromodomain helicase DNA binding protein 3 (CHD3), transcript variant X11	MODEL	mRNA
SCAF11	XM_017020221.1	6.10	0.0039586	PREDICTED: Homo sapiens SR-related CTD associated factor 11 (SCAF11), transcript variant X8	MODEL	mRNA
ATAD2	XM_011516996.2	-2.83	0.0040110	PREDICTED: Homo sapiens ATPase family, AAA domain containing 2 (ATAD2), transcript variant X3	MODEL	mRNA
LSM6	XR_001741100.1	5.61	0.0040110	PREDICTED: Homo sapiens LSM6 homolog, U6 small nuclear RNA and mRNA degradation associated (LSM6), transcript variant X3	MODEL	misc_RNA
PASK	XM_011510835.1	-6.48	0.0047899	PREDICTED: Homo sapiens PAS domain containing serine/threonine kinase (PASK), transcript variant X14	MODEL	mRNA
CGN	XM_005245365.4	7.10	0.0082430	PREDICTED: Homo sapiens cingulin (CGN), transcript variant X1	MODEL	mRNA
WNK2	XM_011518936.2	-5.69	0.0083023	PREDICTED: Homo sapiens WNK lysine deficient protein kinase 2 (WNK2), transcript variant X22	MODEL	mRNA
OGT	XM_017029907.1	-6.15	0.0099568	PREDICTED: Homo sapiens O-linked N-acetylglucosamine (GlcNAc) transferase (OGT), transcript variant X1	MODEL	mRNA
TNFRSF10D	NM_003840.4	-2.24	0.0219061	TNF receptor superfamily member 10d (TNFRSF10D)	REVIEWED	mRNA
UBA1	XM_017029780.1	-5.18	0.0219061	PREDICTED: Homo sapiens ubiquitin like modifier activating enzyme 1 (UBA1), transcript variant X6	MODEL	mRNA
LOC105372462	XR_919753.2	-5.10	0.0229630	PREDICTED: Homo sapiens uncharacterized LOC105372462 (LOC105372462), transcript variant X2	MODEL	ncRNA
ACVR1C	NM_001111033.1	-6.01	0.0264439	activin A receptor type 1C (ACVR1C), transcript variant 4	VALIDATED	mRNA
SPAST	NM_014946.3	-4.08	0.0313178	spastin (SPAST), transcript variant 1	REVIEWED	mRNA
TREM2	XM_006715116.3	5.06	0.0355194	PREDICTED: Homo sapiens triggering receptor expressed on myeloid cells 2 (TREM2), transcript variant X1	MODEL	mRNA
GADD45B	NM_015675.3	-3.43	0.0357277	growth arrest and DNA damage inducible beta (GADD45B)	REVIEWED	mRNA
TPX2	XM_011528697.2	4.54	0.0381066	PREDICTED: Homo sapiens TPX2, microtubule nucleation factor (TPX2), transcript variant X1	MODEL	mRNA
PITPNM3	NM_001165966.1	-6.13	0.0399315	PITPNM family member 3 (PITPNM3), transcript variant 2	REVIEWED	mRNA
C12orf57	NM_001301834.1	-5.14	0.0399315	chromosome 12 open reading frame 57 (C12orf57), transcript variant 2	REVIEWED	mRNA
LINC01896	XR_935681.2	-5.38	0.0399315	PREDICTED: Homo sapiens uncharacterized LOC645321 (LOC645321), transcript variant X3	MODEL	ncRNA
LINC01896	XR_952162.2	-5.38	0.0399315	PREDICTED: Homo sapiens uncharacterized LOC645321 (LOC645321), transcript variant X3	MODEL	ncRNA
LINC01896	XR_952517.2	-5.38	0.0399315	PREDICTED: Homo sapiens uncharacterized LOC645321 (LOC645321), transcript variant X3	MODEL	ncRNA
NFX1	XR_001746314.1	-5.34	0.0408559	PREDICTED: Homo sapiens nuclear transcription factor, X-box binding 1 (NFX1), transcript variant X5	MODEL	misc_RNA
FHL2	NM_001318896.1	-4.86	0.0432861	four and a half LIM domains 2 (FHL2), transcript variant 8	REVIEWED	mRNA
MIR100HG	NR_137195.1	-5.62	0.0432861	mir-100-let-7a-2-mir-125b-1 cluster host gene (MIR100HG), transcript variant 22	REVIEWED	long non-coding RNA
KIAA1324	XM_011541825.1	-5.33	0.0432861	PREDICTED: Homo sapiens KIAA1324 (KIAA1324), transcript variant X1	MODEL	mRNA
NCKAP5	XM_017003980.1	-5.45	0.0432861	PREDICTED: Homo sapiens NCK associated protein 5 (NCKAP5), transcript variant X18	MODEL	mRNA
ANO2	XM_011520978.2	-4.42	0.0457225	PREDICTED: Homo sapiens anoctamin 2 (ANO2), transcript variant X5	MODEL	mRNA

The list of “developmental” genes that are differentially expressed in Autism vs Control are saved in DESeq2_glist_child.txt and DESeq2_glist_adult.txt (Entrez GeneIDs), in DESeq2_slist_child.txt and DESeq2_slist_adult.txt (gene symbols) while the list of “developmental” transcripts are saved in DESeq2_tlist_child.txt and DESeq2_tlist_adult.txt.

Save gene lists

unlink('DESeq2_tlist_child.txt')
unlink('DESeq2_tlist_adult.txt')
unlink('DESeq2_glist_child.txt')
unlink('DESeq2_glist_adult.txt')
unlink('DESeq2_slist_child.txt')
unlink('DESeq2_slist_adult.txt')
lapply(devChildCA[order(devChildCA)], write, 'DESeq2_tlist_child.txt', append=TRUE)
lapply(devAdultCA[order(devAdultCA)], write, 'DESeq2_tlist_adult.txt', append=TRUE)
lapply(devChildCAg[order(devChildCAg)], write, 'DESeq2_glist_child.txt', append=TRUE)
lapply(devAdultCAg[order(devAdultCAg)], write, 'DESeq2_glist_adult.txt', append=TRUE)
lapply(devChildCAs[order(devChildCAs)], write, 'DESeq2_slist_child.txt', append=TRUE)
lapply(devAdultCAs[order(devAdultCAs)], write, 'DESeq2_slist_adult.txt', append=TRUE)
save(file='DESeq2_tlist.RData', devChildCA, devAdultCA)
save(file='DESeq2_glist.RData', devChildCAg, devAdultCAg, devChildCAs, devAdultCAs)

Gene trajectories

Up-regulated

devChildCAup <- intersect((signifChild %>% filter(log2FoldChange > 0))[['target_id']], signifChildCA$target_id)
for (i in devChildCAup) {
  obs1 <- assay(rld) %>% 
    as.data.frame() %>% 
    rownames_to_column('target_id') %>% 
    filter(target_id == i) %>% 
    gather(colnames(assay(rld)), key = 'sample', value = 'est_counts') %>%
    left_join(metadata1, by = 'sample') %>% 
    left_join(ttg, by = 'target_id')
  symbol <- obs1$symbol[1]
  cat('####', symbol, '\n\n')
  g <- ggplot(obs1, aes(x=grp, y=exp(est_counts), color=grp)) +
    geom_point(shape = 1, size = 3) +
    geom_boxplot() +
    labs(title = paste0(obs1$symbol[1],' (',obs1$target_id[1],')'), 
         subtitle = str_c(str_wrap(obs1$description[1], width=55), collapse="\n"),
         x = 'group',
         y = 'estimated counts',
         fill = "Group") +
    theme(legend.position = c(0.9, 0.85), 
          legend.title = element_text(face = "bold.italic"), 
          legend.box.background = element_rect(colour = "black", fill = alpha('white', alpha = 0.6)), 
          axis.text.x = element_text(angle = 45, hjust = 1), 
          plot.title = element_text(hjust = 0.4), 
          plot.subtitle = element_text(hjust = 0.4) 
    ) 
  print(g)
  cat('\n\n')
}

ADAM28

RAB11FIP1

TTR

SLC5A5

TAGAP

Down-regulated

devChildCAdown <- intersect((signifChild %>% filter(log2FoldChange < 0))[['target_id']], signifChildCA$target_id)
for (x in devChildCAdown) {
  obs1 <- assay(rld) %>% 
    as.data.frame() %>% 
    rownames_to_column('target_id') %>% 
    filter(target_id == x) %>% 
    gather(colnames(assay(rld)), key = 'sample', value = 'est_counts') %>% 
    left_join(metadata1, by = 'sample') %>% 
    left_join(ttg, by = 'target_id')
  symbol <- obs1$symbol[1]
  cat('####', symbol, '\n\n')
  g <- ggplot(obs1, aes(x=grp, y=exp(est_counts), color=grp)) + 
    geom_point(shape = 1, size = 3) + 
    geom_boxplot() + 
    labs(title=paste0(obs1$symbol[1],' (',obs1$target_id[1],')'), 
         subtitle=str_c(str_wrap(obs1$description[1], width=55), collapse="\n"), 
         x = 'group', 
         y = 'estimated counts', 
         fill = "Group") +
    scale_fill_discrete(name = "Group") + 
    theme(legend.position = c(0.9, 0.85), 
          legend.title = element_text(face = "bold.italic"), 
          legend.box.background = element_rect(colour = "black", fill = alpha('white', alpha = 0.6)), 
          axis.text.x = element_text(angle = 45, hjust = 1), 
          plot.title = element_text(hjust = 0.4), 
          plot.subtitle = element_text(hjust = 0.4) 
    )
  print(g)
  cat('\n\n')
}

MCM7

ZNF160

LRRC75A-AS1

EXOC1

ZFP64

BUB1B

SFRP1

PLPP5

RGL1

A4GALT

LOC105377135

GO analysis

Perform GO analysis using the goseq package, which normalizes GO term counts by transcript length.

refseq.lengths <- read_tsv('/share/db/refseq/human/refseq.108.rna.lengths', col_names = c('target_id', 'length'))
ttg <- ttg %>% full_join(refseq.lengths, by = 'target_id')
gene_lengths <- ttg %>%
  dplyr::select(gene_id,length) %>%
  filter(!is.na(gene_id)) %>%
  group_by(gene_id) %>%
  summarise(median_length = median(length)) %>%
  dplyr::select(gene_id, median_length) %>%
  spread(gene_id, median_length) %>%
  as.list() %>%
  unlist()

genes <- rep(0, length(unique(na.omit(ttg$gene_id))))
names(genes) <- na.omit(unique(ttg$gene_id))
genes[as.character(unique(signifChildCA$gene_id))] <- 1

pwf <- nullp(DEgenes = genes, genome = NULL, id = NULL, bias.data = gene_lengths)

GO.wall <- goseq(pwf, "hg19", "knownGene")
t1 <- GO.wall %>% filter(over_represented_pvalue < 0.05) %>% group_by(ontology) %>% summarise(n=n())
t2 <- GO.wall %>% filter(ontology == 'BP' & over_represented_pvalue < 0.05 & grepl('(neur[oa])|nerv', term)) %>%
  mutate(category = paste0('[',category,'](http://amigo.geneontology.org/amigo/term/',category,')')) %>%
  dplyr::select(c('category', 'term', 'numDEInCat', 'numInCat', 'over_represented_pvalue'))
t3 <- GO.wall %>% filter(ontology == 'MF' & over_represented_pvalue < 0.05) %>% head(n=50) %>% 
  mutate(category = paste0('[',category,'](http://amigo.geneontology.org/amigo/term/',category,')')) %>%
  dplyr::select(c('category', 'term', 'numDEInCat', 'numInCat', 'over_represented_pvalue'))
t4 <- GO.wall %>% filter(ontology == 'CC' & over_represented_pvalue < 0.05) %>% head(n=50) %>% 
  mutate(category = paste0('[',category,'](http://amigo.geneontology.org/amigo/term/',category,')')) %>%
  dplyr::select(c('category', 'term', 'numDEInCat', 'numInCat', 'over_represented_pvalue'))

Summary

Number of significant categories in different ontologies

kable(t1, format = 'markdown')

ontology	n
BP	190
CC	15
MF	35

Biological process

Summary of biological process terms related to neuronal development

kable(t2, format = 'markdown')

category	term	numDEInCat	numInCat	over_represented_pvalue
GO:0014034	neural crest cell fate commitment	1	3	0.0033456
GO:1904956	regulation of midbrain dopaminergic neuron differentiation	1	6	0.0064146
GO:1904338	regulation of dopaminergic neuron differentiation	1	11	0.0119593
GO:0014029	neural crest formation	1	12	0.0132207
GO:0090179	planar cell polarity pathway involved in neural tube closure	1	13	0.0142758
GO:0090178	regulation of establishment of planar polarity involved in neural tube closure	1	14	0.0152185
GO:0090177	establishment of planar polarity involved in neural tube closure	1	15	0.0162200
GO:1904948	midbrain dopaminergic neuron differentiation	1	19	0.0206566
GO:0071542	dopaminergic neuron differentiation	1	39	0.0416974

Molecular function

Summary of the 50th most significant “molecular functions” terms

kable(t3, format = 'markdown')

category	term	numDEInCat	numInCat	over_represented_pvalue
GO:0008507	sodium:iodide symporter activity	1	1	0.0009594
GO:0015373	monovalent anion:sodium symporter activity	1	1	0.0009594
GO:0050512	lactosylceramide 4-alpha-galactosyltransferase activity	1	1	0.0009651
GO:0001011	transcription factor activity, sequence-specific DNA binding, RNA polymerase recruiting	1	1	0.0010870
GO:0001087	transcription factor activity, TFIIB-class binding	1	1	0.0010870
GO:0015111	iodide transmembrane transporter activity	1	3	0.0029095
GO:0001093	TFIIB-class transcription factor binding	1	3	0.0033086
GO:0008321	Ral guanyl-nucleotide exchange factor activity	1	3	0.0033163
GO:0001083	transcription factor activity, RNA polymerase II basal transcription factor binding	1	5	0.0053085
GO:0070324	thyroid hormone binding	1	6	0.0065222
GO:0046935	1-phosphatidylinositol-3-kinase regulator activity	1	6	0.0065940
GO:0035014	phosphatidylinositol 3-kinase regulator activity	1	7	0.0077892
GO:0015643	toxic substance binding	1	11	0.0116777
GO:0001091	RNA polymerase II basal transcription factor binding	1	11	0.0118417
GO:0008195	phosphatidate phosphatase activity	1	13	0.0146204
GO:0017049	GTP-Rho binding	1	17	0.0173075
GO:0043425	bHLH transcription factor binding	1	27	0.0291582
GO:0015248	sterol transporter activity	1	28	0.0291726
GO:0035250	UDP-galactosyltransferase activity	1	30	0.0320839
GO:0017147	Wnt-protein binding	1	30	0.0323582
GO:0017016	Ras GTPase binding	2	281	0.0361649
GO:0001784	phosphotyrosine binding	1	35	0.0369065
GO:0008378	galactosyltransferase activity	1	35	0.0369817
GO:0004003	ATP-dependent DNA helicase activity	1	35	0.0371952
GO:0001098	basal transcription machinery binding	1	36	0.0381469
GO:0001099	basal RNA polymerase II transcription machinery binding	1	36	0.0381469
GO:0070888	E-box binding	1	37	0.0392161
GO:0005109	frizzled binding	1	36	0.0396567
GO:0031267	small GTPase binding	2	302	0.0411958
GO:0016303	1-phosphatidylinositol-3-kinase activity	1	43	0.0444385
GO:0015485	cholesterol binding	1	43	0.0448928
GO:0005085	guanyl-nucleotide exchange factor activity	2	322	0.0461261
GO:0045309	protein phosphorylated amino acid binding	1	44	0.0465393
GO:0051020	GTPase binding	2	330	0.0484330
GO:0032934	sterol binding	1	48	0.0498251

Cellular component

Summary of the 50th most significant “cellular component” terms

kable(t4, format = 'markdown')

category	term	numDEInCat	numInCat	over_represented_pvalue
GO:0044424	intracellular part	20	13845	0.0027052
GO:0098592	cytoplasmic side of apical plasma membrane	1	3	0.0034805
GO:0005622	intracellular	20	14144	0.0041462
GO:0000778	condensed nuclear chromosome kinetochore	1	9	0.0093613
GO:0042555	MCM complex	1	11	0.0118843
GO:0000940	condensed chromosome outer kinetochore	1	12	0.0133876
GO:0044454	nuclear chromosome part	3	493	0.0149696
GO:0000228	nuclear chromosome	3	527	0.0179541
GO:0000780	condensed nuclear chromosome, centromeric region	1	19	0.0197618
GO:0000145	exocyst	1	19	0.0199270
GO:0005942	phosphatidylinositol 3-kinase complex	1	20	0.0209235
GO:0005680	anaphase-promoting complex	1	21	0.0224898
GO:0051233	spindle midzone	1	28	0.0298132
GO:0000152	nuclear ubiquitin ligase complex	1	38	0.0402079
GO:0005829	cytosol	9	4749	0.0468865

Heatmap of DE genes

Child

myttg <- ttg %>% filter(target_id %in% devChildCA) %>% dplyr::select(c('target_id', 'symbol'))
symbols <- myttg$symbol
names(symbols) <- myttg$target_id
sel_vars <- metadata1 %>%
  filter(age == 'Child' | grepl('SVZ', sample)) %>%
  dplyr::select('sample') %>%
  as.list() %>% unlist() %>% as.vector()
obs <-  
  counts(ddsWald, normalized=TRUE) %>%
  # assay(rld) %>%
  as.data.frame() %>%
  rownames_to_column('target_id') %>%
  filter(target_id %in% devChildCA) %>%
  dplyr::select(target_id, sel_vars) %>%
  gather(sel_vars, key = 'sample', value = 'est_counts') %>%
  spread(target_id, est_counts) %>%
  remove_rownames() %>%
  column_to_rownames(var="sample") %>%
  as.matrix()
colnames(obs) <- symbols[colnames(obs)]
labels <- metadata1 %>%
  filter(age == 'Child' | grepl('SVZ', sample)) %>%
  dplyr::select(c('sample','age')) %>%
  arrange(sample) %>%
  remove_rownames() %>%
  column_to_rownames(var='sample')
Rowv <- obs %>%
  dist(method="euclidean") %>%
  hclust(method = "complete") %>%
  as.dendrogram %>%
  # rotate(c(6:7,1:5)) %>%
  color_branches(k = 3) %>%
  set("branches_lwd", 4)
obs[which(obs == 0)] <- 1
par(mar=c(5,4,4,2)+0.1)
heatmap.2(obs, Rowv = Rowv, trace="none", margins=c(5,9), srtCol = 45,
          symm = F, symkey = F, symbreaks = F, scale="none")

Adult

myttg <- ttg %>% filter(target_id %in% devAdultCA) %>% dplyr::select(c('target_id', 'symbol'))
symbols <- myttg$symbol
names(symbols) <- myttg$target_id
sel_vars <- metadata1 %>%
  filter(age == 'Adult' | grepl('SVZ', sample)) %>%
  dplyr::select('sample') %>%
  as.list() %>% unlist() %>% as.vector()
obs <-
  counts(ddsWald, normalized=TRUE) %>%
  # assay(rld) %>%
  as.data.frame() %>%
  rownames_to_column('target_id') %>%
  filter(target_id %in% devAdultCA) %>%
  dplyr::select(target_id, sel_vars) %>%
  gather(sel_vars, key = 'sample', value = 'est_counts') %>%
  spread(target_id, est_counts) %>%
  remove_rownames() %>%
  column_to_rownames(var="sample") %>%
  as.matrix()
colnames(obs) <- symbols[colnames(obs)]
labels <- metadata1 %>%
  filter(age == 'Adult' | grepl('SVZ', sample)) %>%
  dplyr::select(c('sample','age')) %>%
  arrange(sample) %>%
  remove_rownames() %>%
  column_to_rownames(var='sample')
Rowv <- obs %>%
  dist(method="euclidean") %>%
  hclust(method = "complete") %>%
  as.dendrogram %>%
  # rotate(c(6:7,1:5)) %>%
  color_branches(k = 3) %>%
  set("branches_lwd", 4)
obs[which(obs == 0)] <- 1
par(mar=c(5,4,4,2)+0.1)
heatmap.2(obs, Rowv = Rowv, trace="none", margins=c(5,9), srtCol = 45,
          symm = F, symkey = F, symbreaks = F, scale="none")

Correlation plot for all genes

This correlation plot uses all the transcripts annotated as PROVISIONAL, REVIEWED or VALIDATED in RefSeq (the high confidence categories)

cor_tlist <- ttg %>% filter(status %in% c('PROVISIONAL', 'REVIEWED', 'VALIDATED')) %>% .[['target_id']]
obs <- 
  counts(ddsWald, normalized = TRUE) %>%
  # assay(vst) %>%
  as.data.frame() %>%
  rownames_to_column('target_id') %>%
  filter(target_id %in% cor_tlist) %>%
  gather(colnames(ddsWald), key = 'sample', value = 'est_counts') %>% 
  full_join(metadata1,  by = 'sample') %>%
  dplyr::select(c('grp','target_id','est_counts')) %>%
  group_by(grp, target_id) %>%
  summarise(est_counts = median(est_counts)) %>%
  spread(target_id, est_counts) %>%
  remove_rownames() %>%
  column_to_rownames(var="grp") %>%
  t() %>%
  as.tibble() %>%
  filter_at(vars(matches('Child|Adult')), all_vars(. >= 1))
colnames(obs) <- metadata1 %>% 
  dplyr::select(grp) %>% 
  group_by(grp) %>% 
  summarise(group_count=n()) %>% 
  mutate(group = paste0(grp,'(',group_count,')')) %>%
  .[['group']]
colpal=brewer.pal(n=8, name="RdBu")
corrplot(cor(obs),
         method="circle",
         type="lower",
         col=colpal,
         cl.lim=c(0,1),
         number.cex = 0.6,
         addCoef.col = "black",
         tl.col="black",
         tl.cex = 0.75,
         tl.srt=45,
         diag=FALSE)

Correlation plot for “developmental” genes

This correlation plot uses the ‘significant’ transcripts, which are different between the control child samples and the fetal samples.

All transcripts

obs <- 
  counts(ddsWald, normalized = TRUE) %>%
  as.data.frame() %>%
  rownames_to_column('target_id') %>%
  filter(target_id %in% signifChild$target_id) %>%
  gather(colnames(ddsWald), key = 'sample', value = 'est_counts') %>% 
  full_join(metadata1,  by = 'sample') %>%
  dplyr::select(c('grp','target_id','est_counts')) %>%
  group_by(grp, target_id) %>%
  summarise(est_counts = median(est_counts)) %>%
  spread(target_id, est_counts) %>%
  remove_rownames() %>%
  column_to_rownames(var="grp") %>%
  t() %>%
  as.tibble() %>%
  filter_at(vars(matches('Child|Adult')), all_vars(. >= 1))
colnames(obs) <- metadata1 %>% 
  dplyr::select(grp) %>% 
  group_by(grp) %>% 
  summarise(group_count=n()) %>% 
  mutate(group = paste0(grp,'(',group_count,')')) %>%
  .[['group']]
colpal=brewer.pal(n=8, name="RdBu")
corrplot(cor(obs),
         method="circle",
         type="lower",
         col=colpal,
         #cl.lim=c(0,1),
         number.cex = 0.6,
         addCoef.col = "black",
         tl.col="black",
         tl.cex = 0.75,
         tl.srt=45,
         diag=FALSE)

Up-regulated

obs <- 
  counts(ddsWald, normalized = TRUE) %>%
  # assay(rld) %>%
  as.data.frame() %>%
  rownames_to_column('target_id') %>%
  filter(target_id %in% (signifChild %>% filter(log2FoldChange > 0))[['target_id']]) %>%
  gather(colnames(assay(ddsWald)), key = 'sample', value = 'est_counts') %>% 
  full_join(metadata1,  by = 'sample') %>%
  dplyr::select(c('grp','target_id','est_counts')) %>%
  group_by(grp, target_id) %>%
  summarise(est_counts = median(est_counts)) %>%
  spread(target_id, est_counts) %>%
  remove_rownames() %>%
  column_to_rownames(var="grp") %>%
  t() %>%
  as.tibble() %>%
  filter_at(vars(matches('Child|Adult')), all_vars(. >= 1))
colnames(obs) <- metadata1 %>% 
  dplyr::select(grp) %>% 
  group_by(grp) %>% 
  summarise(group_count=n()) %>% 
  mutate(group = paste0(grp,'(',group_count,')')) %>%
  .[['group']]
colpal=brewer.pal(n=8, name="RdBu")
corrplot(cor(obs),
         method="circle",
         type="lower",
         col=colpal,
         #cl.lim=c(0,1),
         number.cex = 0.6,
         addCoef.col = "black",
         tl.col="black",
         tl.cex = 0.75,
         tl.srt=45,
         diag=FALSE)

Down-regulated

obs <- 
  counts(ddsWald, normalized=TRUE) %>%
  # assay(rld) %>% 
  as.data.frame() %>%
  rownames_to_column('target_id') %>%
  filter(target_id %in% (signifChild %>% filter(log2FoldChange < 0))[['target_id']]) %>%
  gather(colnames(assay(ddsWald)), key = 'sample', value = 'est_counts') %>% 
  full_join(metadata1,  by = 'sample') %>%
  dplyr::select(c('grp','target_id','est_counts')) %>%
  group_by(grp, target_id) %>%
  summarise(est_counts = median(est_counts)) %>%
  spread(target_id, est_counts) %>%
  remove_rownames() %>%
  column_to_rownames(var="grp") %>%
  t() %>%
  as.tibble() %>%
  filter_at(vars(matches('Child|Adult')), all_vars(. >= 1))
colnames(obs) <- metadata1 %>% 
  dplyr::select(grp) %>% 
  group_by(grp) %>% 
  summarise(group_count=n()) %>% 
  mutate(group = paste0(grp,'(',group_count,')')) %>%
  .[['group']]
colpal=brewer.pal(n=8, name="RdBu")
corrplot(cor(obs),
         method="circle",
         type="lower",
         col=colpal,
         #cl.lim=c(0,1),
         number.cex = 0.6,
         addCoef.col = "black",
         tl.col="black",
         tl.cex = 0.75,
         tl.srt=45,
         diag=FALSE)

Cortical tissue analysis

DESeq2 analysis

Select cortical metadata

Select the metadata corresponding to the SVZ tissue samples:

metadata2 <- metadata %>%
  filter(grepl('cortex|CP',tissue)) %>%
  mutate(condition = factor(condition, levels=c('Control', 'Autism'))) %>%
  mutate(grp = factor(paste0(age,condition), levels=c('FetalControl', 'ChildControl', 'ChildAutism', 'AdultControl', 'AdultAutism'))) %>%
  arrange(sample) %>%
  dplyr::select( c('sample', 'condition', 'library', 'readlength', 'tissue', 'age', 'grp', 'path'))
metadata2

Read kallisto output files

Read the files with the kallisto estimated counts for each of the transcripts in RefSeq version 108 (http://www.ncbi.nlm.nih.gov/genome/annotation_euk/Homo_sapiens/108/).

files <- metadata2 %>% dplyr::select('sample', 'path') %>% spread('sample', 'path') %>% as.list() %>% unlist()
txi.kallisto.cp <- tximport(files, type = "kallisto", txOut = TRUE)
metadata2.df <- metadata2 %>%  column_to_rownames(var="sample") %>% as.data.frame()
rownames(txi.kallisto.cp$counts) <- as.character(rownames(txi.kallisto.cp$counts))

Perform Wald test

• Create a DESeq dataset from the imported estimated counts object with a model design based on a factor made by combining the age group and the condition (grp) with a baseline level corresponding to the Control condition and Fetal age group.
• Filter out all the rows that have at least 1 estimated read in the fetal samples, at least 1 in the control samples and at least 1 in the autism samples .
• Perform a Wald test on this model.
• List the available contrasts

dds2 <- DESeqDataSetFromTximport(txi = txi.kallisto.cp, colData = metadata2.df, design = ~grp)
c <- counts(dds2) %>% as.tibble()
c[['target_id']] <- rownames(counts(dds2))
c <- c %>% mutate(sum1 = rowSums(dplyr::select(., starts_with("CP_"))),
                  sum2 = rowSums(dplyr::select(., starts_with("A_"))),
                  sum3 = rowSums(dplyr::select(., starts_with("C_"))))

# n1 <- colnames(c) %>% as.tibble() %>% filter(grepl('SVZ', value)) %>% summarise(n()) %>% as.numeric()
# n2 <- colnames(c) %>% as.tibble() %>% filter(grepl('A_', value)) %>% summarise(n()) %>% as.numeric()
# n3 <- colnames(c) %>% as.tibble() %>% filter(grepl('C_', value)) %>% summarise(n()) %>% as.numeric()

c <- c %>% filter((sum1 > 1) & (sum2 > 1) & (sum3 > 1))
dds2 <- dds2[c$target_id,]
rm('c')
ddsWald2 <- DESeq(dds2, test = "Wald", betaPrior = TRUE, parallel = TRUE)
resultsNames(ddsWald2)

## [1] "Intercept"       "grpFetalControl" "grpChildAutism"  "grpAdultControl"
## [5] "grpAdultAutism"

DESeq results

Extract the results from the model.

# resultsChild2   <- results(ddsWald2, alpha = 0.05, 
#                           contrast = c('grp', 'ChildControl', 'FetalControl'))
resultsAdult2   <- results(ddsWald2, alpha = 0.05, 
                          contrast = c('grp', 'AdultControl', 'FetalControl'))
resultsChildA2  <- results(ddsWald2, alpha = 0.05, 
                          contrast = c('grp', 'ChildAutism', 'FetalControl'))
resultsAdultA2  <- results(ddsWald2, alpha = 0.05, 
                          contrast = c('grp', 'AdultAutism', 'FetalControl'))
# resultsChildCA2 <- results(ddsWald2, alpha = 0.05, 
#                           contrast = c('grp','ChildControl','ChildAutism'))
resultsAdultCA2 <- results(ddsWald2, alpha = 0.05, 
                          contrast = c('grp','AdultControl','AdultAutism'))


# From the DESeq2 vignette: 
#
# "Regarding multiple test correction, if a user is planning to contrast all pairs of many levels
#  and then selectively reporting the results of only a subset of those pairs, one needs to perform multiple testing across contrasts 
#  as well as genes to control for this additional form of multiple testing. 
#  This can be done by using the p.adjust function across a long vector of p values from all pairs of contrasts, 

#  then re-assigning these adjusted p values to the appropriate results table."
# pChild <- resultsChild %>% as.tibble() %>% rownames_to_column(var='target_id') %>%
#   dplyr::select(c('target_id', 'pvalue')) %>% spread('target_id', 'pvalue') %>%
#   as.list() %>% unlist()
# pAdult <- resultsAdult %>% as.tibble() %>% rownames_to_column(var='target_id') %>%
#   dplyr::select(c('target_id', 'pvalue')) %>% spread('target_id', 'pvalue') %>%
#   as.list() %>% unlist()

# signifChild2 <- resultsChild2 %>%
#   as.tibble(.) %>% rownames_to_column(var='target_id') %>%
#   filter(padj<0.05) %>% arrange(padj) %>%
#   left_join(ttg, by = 'target_id') %>%
#   dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

signifAdult2 <- resultsAdult2 %>%
  as.tibble(.) %>% rownames_to_column(var='target_id') %>%
  filter(padj<0.05) %>% arrange(padj) %>%
  left_join(ttg, by = 'target_id') %>%
  dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

signifChildA2 <- resultsChildA2 %>%
  as.tibble(.) %>% rownames_to_column(var='target_id') %>%
  filter(padj<0.05) %>% arrange(padj) %>%
  left_join(ttg, by = 'target_id') %>%
  dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

signifAdultA2 <- resultsAdultA2 %>%
  as.tibble(.) %>% rownames_to_column(var='target_id') %>%
  filter(padj<0.05) %>% arrange(padj) %>%
  left_join(ttg, by = 'target_id') %>%
  dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

# signifChildCA2 <- resultsChildCA2 %>%
#   as.tibble(.) %>% rownames_to_column(var='target_id') %>%
#   filter(padj<0.05) %>% arrange(padj) %>%
#   left_join(ttg, by = 'target_id') %>%
#   dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

signifAdultCA2 <- resultsAdultCA2 %>%
  as.tibble(.) %>% rownames_to_column(var='target_id') %>%
  filter(padj<0.05) %>% arrange(padj) %>%
  left_join(ttg, by = 'target_id') %>%
  dplyr::select(c('target_id', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj', 'gene_id', 'symbol', 'description', 'status', 'molecule_type'))

# devChildCA2  <- intersect(signifChild2$target_id, signifChildCA2$target_id)
devAdultCA2  <- intersect(signifAdult2$target_id, signifAdultCA2$target_id)
# devChildCAg2 <- tibble(target_id=devChildCA2) %>% left_join(ttg, by='target_id') %>% 
#   dplyr::select('gene_id') %>% as.list() %>% unlist() %>% unique()
devAdultCAg2 <- tibble(target_id=devAdultCA2) %>% left_join(ttg, by='target_id') %>% 
  dplyr::select('gene_id') %>% as.list() %>% unlist() %>% unique()
# devChildCAs2 <- tibble(target_id=devChildCA2) %>% left_join(ttg, by='target_id') %>% 
#   dplyr::select('symbol') %>% as.list() %>% unlist() %>% unique()
devAdultCAs2 <- tibble(target_id=devAdultCA2) %>% left_join(ttg, by='target_id') %>% 
  dplyr::select('symbol') %>% as.list() %>% unlist() %>% unique()

Control - Fetal

Summary of the number of transcripts and genes differentially expressed in control samples of different age groups vs. the fetal samples (“developmental genes”):

rsum_control_fetal2 <- tribble( ~Counts, ~Adult,
                               'Transcripts', 
                               length(signifAdult2$target_id), 
                               'Genes', 
                               length(unique(signifAdult2$gene_id))
                               )
kable(rsum_control_fetal2, format = 'markdown')

Counts	Adult
Transcripts	16686
Genes	11588

Venn diagram:

grid.draw(venn.diagram(list(Adult=signifAdult2$target_id), 
             filename = NULL,
             alpha = 0.5,
             fill = c("darkorchid1"),
             cat.col = c("darkorchid4")
             )
)

                   fill = c("cornflowerblue", "darkorchid1"),
                   cat.col = c("darkblue", "darkorchid4")
                   fill = c("cornflowerblue", "darkorchid1"),
                   cat.col = c("darkblue", "darkorchid4")

Autism - Fetal

Summary of the number of transcripts and genes differentially expressed in autism samples of different age groups vs. the fetal samples:

rsum_autism_fetal2 <- tribble( ~Counts, ~Child, ~Adult, 
                               'Transcripts', 
                               length(signifChildA2$target_id), 
                               length(signifAdultA2$target_id), 
                               'Genes', 
                               length(unique(signifChildA2$gene_id)), 
                               length(unique(signifAdultA2$gene_id)) 
                               )
kable(rsum_autism_fetal2, format = 'markdown')

Counts	Child	Adult
Transcripts	13313	15374
Genes	10014	10990

Venn diagram:

grid.draw(venn.diagram(list(Child=signifChildA2$target_id, Adult=signifAdultA2$target_id),
             filename = NULL,
             alpha = 0.5,
                       fill = c("cornflowerblue", "darkorchid1"),
                       cat.col = c("darkblue", "darkorchid4")
             )
)

Control - Autism

Summary of the number of transcripts and genes differentially expressed in autism samples of different age groups vs. the same age control samples:

rsum_autism_control2 <- tribble( ~Counts, ~Adult,
                               'Transcripts', 
                               length(signifAdultCA2$target_id), 
                               'Genes', 
                               length(unique(signifAdultCA2$gene_id))
                               )
kable(rsum_autism_control2, format = 'markdown')

Counts	Adult
Transcripts	10334
Genes	7992

Venn diagram:

grid.draw(venn.diagram(list(Adult=signifAdultCA2$target_id), 
                       filename = NULL, 
                       alpha = 0.5, 
                       fill = c("darkorchid1"), 
                       cat.col = c("darkorchid4") 
                       ) 
          )

Developmental Control - Autism

Summary of the number of transcripts and genes differentially expressed in autism samples of different age groups vs. the same age control samples AND also different in the control age group vs. the fetal samples (“developmental”):

rsum_autism_control_fetal2 <- tribble( ~Counts, ~Adult,
                               'Transcripts', 
                               length(devAdultCA2), 
                               'Genes', 
                               length(devAdultCAg2)
                               )
kable(rsum_autism_control_fetal2, format = 'markdown')

Counts	Adult
Transcripts	6182
Genes	5251

Venn diagram:

grid.draw(venn.diagram(list(Adult=devAdultCA2), 
                       filename = NULL, 
                       alpha = 0.5, 
                       fill = c("darkorchid1"),
                       cat.col = c("darkorchid4")
                       )
          )

QC

PCA

## Transform count data
intgroup = "grp"
rld2 <- rlog(ddsWald2, blind = FALSE)
# vst2 <- varianceStabilizingTransformation(ddsWald2, blind=FALSE)

## Perform PCA analysis and get percent of variance explained
data_pca2 <- plotPCA(rld2, intgroup = intgroup, ntop = 1000, returnData = TRUE)
percentVar2 <- round(100 * attr(data_pca, "percentVar"))

## Make plot
data_pca2 %>%
ggplot(aes_string(x = "PC1", y = "PC2", color = "grp")) +
 geom_point(size = 3) +
 xlab(paste0("PC1: ", percentVar2[1], "% variance")) +
 ylab(paste0("PC2: ", percentVar2[2], "% variance")) +
 coord_fixed() +
 geom_text(aes(label=name), nudge_x = 1, hjust = 0, size = 3, check_overlap = FALSE)

The above plot shows the first two principal components that explain the variability in the data using the regularized log count data. If you are unfamiliar with principal component analysis, you might want to check the Wikipedia entry or this interactive explanation. In this case, the first and second principal component explain 51 and 9 percent of the variance respectively.

Sample-to-sample distances

## Obtain the sample euclidean distances
sampleDists2 <- dist(t(assay(rld2)))
sampleDistMatrix2 <- as.matrix(sampleDists2)

## Add names based on intgroup
rownames(sampleDistMatrix2) <- rownames(colData(rld2))
colnames(sampleDistMatrix2) <- NULL

## Define colors to use for the heatmap if none were supplied
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)

## Make the heatmap
pheatmap(sampleDistMatrix2, clustering_distance_rows = sampleDists2,
         clustering_distance_cols = sampleDists2, color = colors)

This plot shows how samples are clustered based on their euclidean distance using the regularized log transformed count data. This figure gives an overview of how the samples are hierarchically clustered. It is a complementary figure to the PCA plot.

MA Plots

Control - Fetal

This section contains the MA plots (see Wikipedia) that compare the mean of the normalized counts against the log fold change. They show one point per feature. The points are shown in red if the feature has an adjusted p-value less than alpha, that is, the statistically significant features are shown in red.

AdultControl-Fetal

## MA plot with alpha used in DESeq2::results()
plotMA(resultsAdult2, alpha = 0.05, main = 'MA plot with alpha = 0.05', ylim = c(-10, 10))
abline(h=c(-1,1),col="dodgerblue",lwd=2)

This plot uses alpha = 0.05, which is the alpha value used to determine which resulting features were significant when running the function DESeq2::results().

Autism - Fetal

This section contains the MA plots (see Wikipedia) that compare the mean of the normalized counts against the log fold change. They show one point per feature. The points are shown in red if the feature has an adjusted p-value less than alpha, that is, the statistically significant features are shown in red.

ChildAutism-Fetal

## MA plot with alpha used in DESeq2::results()
plotMA(resultsChildA2, alpha = 0.05, main = 'MA plot with alpha = 0.05', ylim = c(-10, 10))
abline(h=c(-1,1),col="dodgerblue",lwd=2)

This plot uses alpha = 0.05, which is the alpha value used to determine which resulting features were significant when running the function DESeq2::results().

AdultAutism-Fetal

## MA plot with alpha used in DESeq2::results()
plotMA(resultsAdultA2, alpha = 0.05, main = 'MA plot with alpha = 0.05', ylim = c(-10, 10))
abline(h=c(-1,1),col="dodgerblue",lwd=2)

This plot uses alpha = 0.05, which is the alpha value used to determine which resulting features were significant when running the function DESeq2::results().

Control - Autism

This section contains the MA plots (see Wikipedia) that compare the mean of the normalized counts against the log fold change. They show one point per feature. The points are shown in red if the feature has an adjusted p-value less than alpha, that is, the statistically significant features are shown in red.

Adult

## MA plot with alpha used in DESeq2::results()
plotMA(resultsAdultCA2, alpha = 0.05, main = 'MA plot with alpha = 0.05', ylim = c(-10, 10))
abline(h=c(-1,1),col="dodgerblue",lwd=2)

This plot uses alpha = 0.05, which is the alpha value used to determine which resulting features were significant when running the function DESeq2::results().

Volcano plots

Control - Fetal

AdultControl - Fetal

resultsAdult2 %>%
  as.tibble()  %>%
  rownames_to_column(var='target_id') %>%
  mutate(sig=ifelse(target_id %in% signifAdult2[['target_id']], "FDR<0.05", "Not Sig")) %>%
  left_join(ttg, by = 'target_id') %>% 
  ggplot(aes(log2FoldChange, -log10(padj))) + 
  geom_point(aes(col=sig)) + 
  scale_color_manual(values=c("red", "black"))

  #coord_cartesian(ylim=c(0,100))

Autism - Fetal

ChildAutism - Fetal

resultsChildA2 %>%
  as.tibble()  %>%
  rownames_to_column(var='target_id') %>%
  mutate(sig=ifelse(target_id %in% signifChildA2[['target_id']], "FDR<0.05", "Not Sig")) %>%
  left_join(ttg, by = 'target_id') %>% 
  ggplot(aes(log2FoldChange, -log10(padj))) + 
  geom_point(aes(col=sig)) + 
  scale_color_manual(values=c("red", "black"))

#  coord_cartesian(ylim=c(0,100))

AdultAutism - Fetal

resultsAdultA2 %>%
  as.tibble()  %>%
  rownames_to_column(var='target_id') %>%
  mutate(sig=ifelse(target_id %in% signifAdultA2[['target_id']], "FDR<0.05", "Not Sig")) %>%
  left_join(ttg, by = 'target_id') %>% 
  ggplot(aes(log2FoldChange, -log10(padj))) + 
  geom_point(aes(col=sig)) + 
  scale_color_manual(values=c("red", "black"))

#  coord_cartesian(ylim=c(0,100))

Control - Autism

Adult

r1 <- resultsAdultCA2 %>%
  as.tibble()  %>%
  rownames_to_column(var='target_id') %>%
  mutate(sig=ifelse(target_id %in% signifAdultCA2[['target_id']], "FDR<0.05", "Not Sig")) %>%
  left_join(ttg, by = 'target_id')
r1 %>% ggplot(aes(log2FoldChange, -log10(padj))) + 
  geom_point(aes(col=sig)) + 
  scale_color_manual(values=c("red", "black"))

#  coord_cartesian(ylim=c(0,10)) + 
#  geom_text_repel(data=filter(r1, sig=='FDR<0.05'), aes(label=symbol))

Gene lists

Control - Fetal

AdultControl - Fetal

This table shows the 50 most significant genes (out of 11588) differentially expressed between control adult samples and fetal samples.

genes_control_fetal_adult2 <- signifAdult2 %>%
  head(n = 50) %>% 
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                                 color_tile("red", "white")(signif(log2FoldChange, 3)),
                                 color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type')) 
genes_control_fetal_adult2 %>%
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
MEX3A	NM_001093725.1	-7.90	0	mex-3 RNA binding family member A (MEX3A)	VALIDATED	mRNA
ODC1	NM_001287190.1	-2.77	0	ornithine decarboxylase 1 (ODC1), transcript variant 4	REVIEWED	mRNA
PABPC1	NM_002568.3	-3.37	0	poly(A) binding protein cytoplasmic 1 (PABPC1)	REVIEWED	mRNA
SOX11	NM_003108.3	-8.73	0	SRY-box 11 (SOX11)	REVIEWED	mRNA
FOXG1	NM_005249.4	-3.39	0	forkhead box G1 (FOXG1)	REVIEWED	mRNA
SPOCK2	NM_014767.2	6.18	0	SPARC/osteonectin, cwcv and kazal like domains proteoglycan 2 (SPOCK2), transcript variant 2	REVIEWED	mRNA
HDAC2	XM_011535788.1	-3.59	0	PREDICTED: Homo sapiens histone deacetylase 2 (HDAC2), transcript variant X1	MODEL	mRNA
GNG2	XM_011536846.2	-3.18	0	PREDICTED: Homo sapiens G protein subunit gamma 2 (GNG2), transcript variant X2	MODEL	mRNA
SLA	NM_006748.3	-6.65	0	Src like adaptor (SLA), transcript variant 3	VALIDATED	mRNA
RBMX	NM_001164803.1	-2.74	0	RNA binding motif protein, X-linked (RBMX), transcript variant 2	REVIEWED	mRNA
RIMS3	XM_011542479.2	4.03	0	PREDICTED: Homo sapiens regulating synaptic membrane exocytosis 3 (RIMS3), transcript variant X1	MODEL	mRNA
SSBP2	XM_017009307.1	-3.71	0	PREDICTED: Homo sapiens single stranded DNA binding protein 2 (SSBP2), transcript variant X5	MODEL	mRNA
SOX4	NM_003107.2	-7.40	0	SRY-box 4 (SOX4)	REVIEWED	mRNA
EEF1A1	NM_001402.5	-3.00	0	eukaryotic translation elongation factor 1 alpha 1 (EEF1A1)	REVIEWED	mRNA
WBP2	NM_012478.3	2.40	0	WW domain binding protein 2 (WBP2), transcript variant 1	REVIEWED	mRNA
ALDOC	NM_005165.2	6.82	0	aldolase, fructose-bisphosphate C (ALDOC)	REVIEWED	mRNA
BACH2	NM_001170794.1	-6.52	0	BTB domain and CNC homolog 2 (BACH2), transcript variant 2	VALIDATED	mRNA
RBMX	NM_002139.3	-2.20	0	RNA binding motif protein, X-linked (RBMX), transcript variant 1	REVIEWED	mRNA
C11orf95	NM_001144936.1	-2.98	0	chromosome 11 open reading frame 95 (C11orf95)	PREDICTED	mRNA
MLF2	NM_005439.2	1.98	0	myeloid leukemia factor 2 (MLF2), transcript variant 1	VALIDATED	mRNA
DDN	NM_015086.1	6.67	0	dendrin (DDN)	VALIDATED	mRNA
FAM117B	NM_173511.3	-3.87	0	family with sequence similarity 117 member B (FAM117B)	VALIDATED	mRNA
GNB1	XM_017001060.1	-6.84	0	PREDICTED: Homo sapiens G protein subunit beta 1 (GNB1), transcript variant X2	MODEL	mRNA
CBX3	NM_016587.3	-2.21	0	chromobox 3 (CBX3), transcript variant 2	REVIEWED	mRNA
ACO2	NM_001098.2	1.99	0	aconitase 2 (ACO2)	REVIEWED	mRNA
CAMK2A	NM_171825.2	7.47	0	calcium/calmodulin dependent protein kinase II alpha (CAMK2A), transcript variant 2	REVIEWED	mRNA
ITM2B	NM_021999.4	1.63	0	integral membrane protein 2B (ITM2B)	REVIEWED	mRNA
TOP2B	NM_001068.3	-2.67	0	topoisomerase (DNA) II beta (TOP2B), transcript variant 2	REVIEWED	mRNA
BCL2L2	NM_001199839.1	3.50	0	BCL2 like 2 (BCL2L2), transcript variant 2	REVIEWED	mRNA
JUND	NM_001286968.1	2.62	0	JunD proto-oncogene, AP-1 transcription factor subunit (JUND), transcript variant 1	REVIEWED	mRNA
JUND	NM_005354.5	2.62	0	JunD proto-oncogene, AP-1 transcription factor subunit (JUND), transcript variant 1	VALIDATED	mRNA
BCL11A	XM_017004333.1	-5.49	0	PREDICTED: Homo sapiens B-cell CLL/lymphoma 11A (BCL11A), transcript variant X2	MODEL	mRNA
SETBP1	NM_015559.2	-3.44	0	SET binding protein 1 (SETBP1), transcript variant 1	REVIEWED	mRNA
IPO7	NM_006391.2	-1.90	0	importin 7 (IPO7)	REVIEWED	mRNA
COX5B	NM_001862.2	3.10	0	cytochrome c oxidase subunit 5B (COX5B)	REVIEWED	mRNA
BCL7A	NM_001024808.2	-2.19	0	BCL tumor suppressor 7A (BCL7A), transcript variant 2	REVIEWED	mRNA
EEF1G	NM_001404.4	-2.48	0	eukaryotic translation elongation factor 1 gamma (EEF1G)	REVIEWED	mRNA
ALDOA	NM_184041.2	4.10	0	aldolase, fructose-bisphosphate A (ALDOA), transcript variant 2	REVIEWED	mRNA
CKB	NM_001823.4	2.48	0	creatine kinase B (CKB)	REVIEWED	mRNA
MTRNR2L6	NM_001190487.2	3.88	0	MT-RNR2-like 6 (MTRNR2L6)	VALIDATED	mRNA
LMNB1	NM_001198557.1	-4.70	0	lamin B1 (LMNB1), transcript variant 2	REVIEWED	mRNA
CA11	NM_001217.4	5.56	0	carbonic anhydrase 11 (CA11), transcript variant 1	REVIEWED	mRNA
LINC00461	NR_024383.1	-3.80	0	long intergenic non-protein coding RNA 461 (LINC00461), transcript variant 2	VALIDATED	long non-coding RNA
THY1	NM_001311162.1	3.57	0	Thy-1 cell surface antigen (THY1), transcript variant 3	REVIEWED	mRNA
MT3	NM_005954.2	8.33	0	metallothionein 3 (MT3)	VALIDATED	mRNA
MTRNR2L1	NM_001190452.1	3.94	0	MT-RNR2-like 1 (MTRNR2L1)	VALIDATED	mRNA
TSPYL2	NM_022117.3	5.17	0	TSPY like 2 (TSPYL2)	REVIEWED	mRNA
RPLP0	NM_001002.3	-1.91	0	ribosomal protein lateral stalk subunit P0 (RPLP0), transcript variant 1	REVIEWED	mRNA
NEAT1	NR_028272.1	5.18	0	nuclear paraspeckle assembly transcript 1 (non-protein coding) (NEAT1), transcript variant MENepsilon	REVIEWED	long non-coding RNA
SBK1	NM_001024401.2	-3.35	0	SH3 domain binding kinase 1 (SBK1)	VALIDATED	mRNA

Autism - Fetal

AdultAutism - Fetal

This table shows the 50 most significant genes (out of 10990) differentially expressed between autism adult samples and fetal samples.

genes_autism_fetal_adult2 <- signifAdultA2 %>% 
  head(n = 50) %>% 
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)), 
         log2FoldChange = ifelse(log2FoldChange < 0, 
                                 color_tile("red", "white")(signif(log2FoldChange, 3)), 
                                 color_tile("white", "darkgreen")(signif(log2FoldChange, 3))), 
         transcript = target_id) %>% 
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type')) 
genes_autism_fetal_adult2 %>% 
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
MTRNR2L6	NM_001190487.2	7.05	0	MT-RNR2-like 6 (MTRNR2L6)	VALIDATED	mRNA
GNG2	XM_011536846.2	-4.27	0	PREDICTED: Homo sapiens G protein subunit gamma 2 (GNG2), transcript variant X2	MODEL	mRNA
NEAT1	NR_028272.1	7.60	0	nuclear paraspeckle assembly transcript 1 (non-protein coding) (NEAT1), transcript variant MENepsilon	REVIEWED	long non-coding RNA
MTRNR2L1	NM_001190452.1	7.42	0	MT-RNR2-like 1 (MTRNR2L1)	VALIDATED	mRNA
MTRNR2L4	NM_001190476.1	7.56	0	MT-RNR2-like 4 (MTRNR2L4)	VALIDATED	mRNA
ITM2B	NM_021999.4	2.26	0	integral membrane protein 2B (ITM2B)	REVIEWED	mRNA
FOXG1	NM_005249.4	-3.82	0	forkhead box G1 (FOXG1)	REVIEWED	mRNA
MTRNR2L8	NM_001190702.1	7.37	0	MT-RNR2-like 8 (MTRNR2L8)	VALIDATED	mRNA
IGIP	NM_001007189.1	5.10	0	IgA inducing protein (IGIP)	VALIDATED	mRNA
SEPT3	NM_019106.5	-3.50	0	septin 3 (SEPT3), transcript variant B	REVIEWED	mRNA
JPT1	NM_016185.3	-4.90	0	Jupiter microtubule associated homolog 1 (JPT1), transcript variant 1	VALIDATED	mRNA
MBP	NM_001025092.1	15.60	0	myelin basic protein (MBP), transcript variant 4	REVIEWED	mRNA
ALDOC	NM_005165.2	7.42	0	aldolase, fructose-bisphosphate C (ALDOC)	REVIEWED	mRNA
TUBB	NM_178014.3	-4.55	0	tubulin beta class I (TUBB), transcript variant 2	REVIEWED	mRNA
JUND	NM_001286968.1	2.95	0	JunD proto-oncogene, AP-1 transcription factor subunit (JUND), transcript variant 1	REVIEWED	mRNA
JUND	NM_005354.5	2.95	0	JunD proto-oncogene, AP-1 transcription factor subunit (JUND), transcript variant 1	VALIDATED	mRNA
SOX11	NM_003108.3	-6.06	0	SRY-box 11 (SOX11)	REVIEWED	mRNA
B2M	NM_004048.2	5.43	0	beta-2-microglobulin (B2M)	REVIEWED	mRNA
MT3	NM_005954.2	8.95	0	metallothionein 3 (MT3)	VALIDATED	mRNA
BCL2L2	NM_001199839.1	3.81	0	BCL2 like 2 (BCL2L2), transcript variant 2	REVIEWED	mRNA
ZBTB47	NM_145166.3	4.00	0	zinc finger and BTB domain containing 47 (ZBTB47)	VALIDATED	mRNA
SPOCK2	NM_014767.2	4.48	0	SPARC/osteonectin, cwcv and kazal like domains proteoglycan 2 (SPOCK2), transcript variant 2	REVIEWED	mRNA
MTRNR2L2	NM_001190470.1	7.18	0	MT-RNR2-like 2 (MTRNR2L2)	VALIDATED	mRNA
C11orf95	NM_001144936.1	-3.77	0	chromosome 11 open reading frame 95 (C11orf95)	PREDICTED	mRNA
PLP1	NM_001305004.1	13.10	0	proteolipid protein 1 (PLP1), transcript variant 4	REVIEWED	mRNA
CRTAP	NM_006371.4	4.09	0	cartilage associated protein (CRTAP)	REVIEWED	mRNA
CRMP1	NM_001313.4	-4.08	0	collapsin response mediator protein 1 (CRMP1), transcript variant 2	REVIEWED	mRNA
PTGDS	NM_000954.5	11.30	0	prostaglandin D2 synthase (PTGDS)	REVIEWED	mRNA
CELSR3	NM_001407.2	-3.56	0	cadherin EGF LAG seven-pass G-type receptor 3 (CELSR3)	REVIEWED	mRNA
SLC6A8	NM_001142806.1	2.82	0	solute carrier family 6 member 8 (SLC6A8), transcript variant 3	REVIEWED	mRNA
NEUROD6	NM_022728.3	-6.98	0	neuronal differentiation 6 (NEUROD6)	REVIEWED	mRNA
MTRNR2L10	NM_001190708.1	6.47	0	MT-RNR2-like 10 (MTRNR2L10)	VALIDATED	mRNA
BCL7A	NM_001024808.2	-2.93	0	BCL tumor suppressor 7A (BCL7A), transcript variant 2	REVIEWED	mRNA
PLEKHB1	NM_001130035.1	10.90	0	pleckstrin homology domain containing B1 (PLEKHB1), transcript variant 4	VALIDATED	mRNA
FAM102A	NM_203305.2	6.21	0	family with sequence similarity 102 member A (FAM102A), transcript variant 2	VALIDATED	mRNA
SPTLC3	XM_011529280.2	7.71	0	PREDICTED: Homo sapiens serine palmitoyltransferase long chain base subunit 3 (SPTLC3), transcript variant X2	MODEL	mRNA
SBK1	NM_001024401.2	-4.28	0	SH3 domain binding kinase 1 (SBK1)	VALIDATED	mRNA
SPARCL1	NM_004684.5	11.40	0	SPARC like 1 (SPARCL1), transcript variant 2	VALIDATED	mRNA
HDAC2	XM_011535788.1	-2.91	0	PREDICTED: Homo sapiens histone deacetylase 2 (HDAC2), transcript variant X1	MODEL	mRNA
MOBP	NR_003090.2	12.80	0	myelin-associated oligodendrocyte basic protein (MOBP), transcript variant 4	VALIDATED	non-coding RNA
BCYRN1	NR_001568.1	12.60	0	brain cytoplasmic RNA 1 (BCYRN1)	REVIEWED	long non-coding RNA
SHISA4	NR_030775.1	4.61	0	shisa family member 4 (SHISA4), transcript variant 2	VALIDATED	non-coding RNA
FAM117B	NM_173511.3	-4.40	0	family with sequence similarity 117 member B (FAM117B)	VALIDATED	mRNA
MARCH4	NM_020814.2	-4.83	0	membrane associated ring-CH-type finger 4 (MARCH4)	VALIDATED	mRNA
CERCAM	XM_011518763.2	6.84	0	PREDICTED: Homo sapiens cerebral endothelial cell adhesion molecule (CERCAM), transcript variant X4	MODEL	mRNA
MEX3A	NM_001093725.1	-5.85	0	mex-3 RNA binding family member A (MEX3A)	VALIDATED	mRNA
SSBP2	XM_017009307.1	-3.46	0	PREDICTED: Homo sapiens single stranded DNA binding protein 2 (SSBP2), transcript variant X5	MODEL	mRNA
HSPB1	NM_001540.3	6.07	0	heat shock protein family B (small) member 1 (HSPB1)	REVIEWED	mRNA
ODC1	NM_001287190.1	-2.21	0	ornithine decarboxylase 1 (ODC1), transcript variant 4	REVIEWED	mRNA
EEF1G	NM_001404.4	-2.48	0	eukaryotic translation elongation factor 1 gamma (EEF1G)	REVIEWED	mRNA

Control - Autism

Adult

This table shows the 50 most significant genes (out of 7992) differentially expressed between control adult samples and autism adult samples.

genes_control_autism_adult2 <- signifAdultCA2 %>% 
  head(n = 50) %>%
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                  color_tile("red", "white")(signif(log2FoldChange, 3)),
                  color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type'))
genes_control_autism_adult2 %>%
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
EFHD2	NM_024329.5	2.190	0	EF-hand domain family member D2 (EFHD2)	VALIDATED	mRNA
NEAT1	NR_028272.1	-2.420	0	nuclear paraspeckle assembly transcript 1 (non-protein coding) (NEAT1), transcript variant MENepsilon	REVIEWED	long non-coding RNA
LPAR1	NM_001351403.1	-4.210	0	lysophosphatidic acid receptor 1 (LPAR1), transcript variant 9	REVIEWED	mRNA
STMN3	NM_015894.3	4.460	0	stathmin 3 (STMN3), transcript variant 1	REVIEWED	mRNA
RIMS3	XM_011542479.2	3.070	0	PREDICTED: Homo sapiens regulating synaptic membrane exocytosis 3 (RIMS3), transcript variant X1	MODEL	mRNA
JPH4	NM_001146028.1	2.730	0	junctophilin 4 (JPH4), transcript variant 2	REVIEWED	mRNA
RAB3A	NM_002866.4	4.110	0	RAB3A, member RAS oncogene family (RAB3A)	VALIDATED	mRNA
SHTN1	XM_017016462.1	-2.890	0	PREDICTED: Homo sapiens shootin 1 (SHTN1), transcript variant X3	MODEL	mRNA
CLSTN3	NM_014718.3	4.560	0	calsyntenin 3 (CLSTN3)	VALIDATED	mRNA
TUBA4A	NM_006000.2	3.860	0	tubulin alpha 4a (TUBA4A), transcript variant 1	REVIEWED	mRNA
PHYHIP	NM_014759.3	5.890	0	phytanoyl-CoA 2-hydroxylase interacting protein (PHYHIP), transcript variant 2	VALIDATED	mRNA
AP2M1	NM_004068.3	2.030	0	adaptor related protein complex 2 mu 1 subunit (AP2M1), transcript variant 1	REVIEWED	mRNA
MTRNR2L6	NM_001190487.2	-3.170	0	MT-RNR2-like 6 (MTRNR2L6)	VALIDATED	mRNA
MTRNR2L1	NM_001190452.1	-3.480	0	MT-RNR2-like 1 (MTRNR2L1)	VALIDATED	mRNA
STXBP1	NM_001032221.3	3.160	0	syntaxin binding protein 1 (STXBP1), transcript variant 2	REVIEWED	mRNA
UCHL1	NM_004181.4	3.350	0	ubiquitin C-terminal hydrolase L1 (UCHL1)	REVIEWED	mRNA
ARGLU1	NM_018011.3	-1.640	0	arginine and glutamate rich 1 (ARGLU1)	VALIDATED	mRNA
SYT5	XM_006723339.3	4.280	0	PREDICTED: Homo sapiens synaptotagmin 5 (SYT5), transcript variant X1	MODEL	mRNA
ATP2A2	XR_243009.2	5.920	0	PREDICTED: Homo sapiens ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 2 (ATP2A2), transcript variant X2	MODEL	misc_RNA
RTN1	NM_206852.2	4.140	0	reticulon 1 (RTN1), transcript variant 3	REVIEWED	mRNA
GLS	NM_014905.4	4.700	0	glutaminase (GLS), transcript variant 1	REVIEWED	mRNA
YWHAH	NM_003405.3	3.230	0	tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein eta (YWHAH)	REVIEWED	mRNA
ANKRD34A	NM_001039888.3	2.700	0	ankyrin repeat domain 34A (ANKRD34A)	VALIDATED	mRNA
SCRT1	NM_031309.5	3.720	0	scratch family transcriptional repressor 1 (SCRT1)	REVIEWED	mRNA
PKM	XM_017022313.1	10.200	0	PREDICTED: Homo sapiens pyruvate kinase, muscle (PKM), transcript variant X5	MODEL	mRNA
BASP1	NM_006317.4	5.350	0	brain abundant membrane attached signal protein 1 (BASP1), transcript variant 1	REVIEWED	mRNA
MBP	NM_001025092.1	-3.410	0	myelin basic protein (MBP), transcript variant 4	REVIEWED	mRNA
CALY	NM_015722.3	6.410	0	calcyon neuron specific vesicular protein (CALY), transcript variant 1	VALIDATED	mRNA
RNF141	NM_016422.3	-1.840	0	ring finger protein 141 (RNF141)	REVIEWED	mRNA
KIF5B	XM_017016224.1	-3.080	0	PREDICTED: Homo sapiens kinesin family member 5B (KIF5B), transcript variant X1	MODEL	mRNA
GNAO1	XM_011523003.2	7.740	0	PREDICTED: Homo sapiens G protein subunit alpha o1 (GNAO1), transcript variant X1	MODEL	mRNA
GOLGA7	NM_016099.2	-2.620	0	golgin A7 (GOLGA7), transcript variant 1	VALIDATED	mRNA
IGIP	NM_001007189.1	-0.953	0	IgA inducing protein (IGIP)	VALIDATED	mRNA
RBMX	NM_001164803.1	-1.660	0	RNA binding motif protein, X-linked (RBMX), transcript variant 2	REVIEWED	mRNA
PDXP	NM_020315.4	2.630	0	pyridoxal phosphatase (PDXP)	VALIDATED	mRNA
THY1	NM_001311162.1	2.750	0	Thy-1 cell surface antigen (THY1), transcript variant 3	REVIEWED	mRNA
SYT13	NM_020826.2	3.820	0	synaptotagmin 13 (SYT13), transcript variant 1	REVIEWED	mRNA
SCN3B	NM_001040151.1	3.010	0	sodium voltage-gated channel beta subunit 3 (SCN3B), transcript variant 2	REVIEWED	mRNA
NCDN	NM_001014841.1	3.290	0	neurochondrin (NCDN), transcript variant 2	REVIEWED	mRNA
TMEM59L	NM_012109.2	2.430	0	transmembrane protein 59 like (TMEM59L)	REVIEWED	mRNA
SEPT7	XM_017012862.1	-1.630	0	PREDICTED: Homo sapiens septin 7 (SEPT7), transcript variant X11	MODEL	mRNA
SYNJ2BP	NM_018373.2	-1.350	0	synaptojanin 2 binding protein (SYNJ2BP)	VALIDATED	mRNA
DOCK1	XM_017015817.1	-2.880	0	PREDICTED: Homo sapiens dedicator of cytokinesis 1 (DOCK1), transcript variant X7	MODEL	mRNA
ATP1A3	NM_001256213.1	3.870	0	ATPase Na+/K+ transporting subunit alpha 3 (ATP1A3), transcript variant 2	REVIEWED	mRNA
SPINT2	NM_021102.3	4.400	0	serine peptidase inhibitor, Kunitz type 2 (SPINT2), transcript variant a	REVIEWED	mRNA
LOC101930059	XR_431133.3	-2.270	0	PREDICTED: Homo sapiens uncharacterized LOC101930059 (LOC101930059), transcript variant X4	MODEL	ncRNA
SEPT3	NM_019106.5	1.770	0	septin 3 (SEPT3), transcript variant B	REVIEWED	mRNA
MTRNR2L8	NM_001190702.1	-3.560	0	MT-RNR2-like 8 (MTRNR2L8)	VALIDATED	mRNA
HPCAL1	NM_002149.3	5.100	0	hippocalcin like 1 (HPCAL1), transcript variant 1	REVIEWED	mRNA
HOOK3	NM_032410.3	-1.330	0	hook microtubule tethering protein 3 (HOOK3)	VALIDATED	mRNA

Developmental Control - Autism

Adult

This table shows the 50 most significant genes (out of 5251) differentially expressed between control adult samples and autism adult samples AND different between control adult samples and fetal samples.

genes_dev_control_autism_adult2 <- signifAdultCA2 %>%
  head(n = 50) %>%
  filter(target_id %in% devAdultCA2) %>%
  mutate(gene = text_spec(symbol, link = paste0('http://www.genecards.org/cgi-bin/carddisp.pl?gene=', symbol)),
         log2FoldChange = ifelse(log2FoldChange < 0,
                  color_tile("red", "white")(signif(log2FoldChange, 3)),
                  color_tile("white", "darkgreen")(signif(log2FoldChange, 3))),
         transcript = target_id) %>%
  dplyr::select(c('gene', 'transcript', 'log2FoldChange', 'padj', 'description', 'status', 'molecule_type'))
genes_dev_control_autism_adult2 %>%
  kable("html", escape = F) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),  font_size = 8, full_width = F)

gene	transcript	log2FoldChange	padj	description	status	molecule_type
EFHD2	NM_024329.5	2.190	0	EF-hand domain family member D2 (EFHD2)	VALIDATED	mRNA
NEAT1	NR_028272.1	-2.420	0	nuclear paraspeckle assembly transcript 1 (non-protein coding) (NEAT1), transcript variant MENepsilon	REVIEWED	long non-coding RNA
LPAR1	NM_001351403.1	-4.210	0	lysophosphatidic acid receptor 1 (LPAR1), transcript variant 9	REVIEWED	mRNA
STMN3	NM_015894.3	4.460	0	stathmin 3 (STMN3), transcript variant 1	REVIEWED	mRNA
RIMS3	XM_011542479.2	3.070	0	PREDICTED: Homo sapiens regulating synaptic membrane exocytosis 3 (RIMS3), transcript variant X1	MODEL	mRNA
JPH4	NM_001146028.1	2.730	0	junctophilin 4 (JPH4), transcript variant 2	REVIEWED	mRNA
RAB3A	NM_002866.4	4.110	0	RAB3A, member RAS oncogene family (RAB3A)	VALIDATED	mRNA
SHTN1	XM_017016462.1	-2.890	0	PREDICTED: Homo sapiens shootin 1 (SHTN1), transcript variant X3	MODEL	mRNA
CLSTN3	NM_014718.3	4.560	0	calsyntenin 3 (CLSTN3)	VALIDATED	mRNA
TUBA4A	NM_006000.2	3.860	0	tubulin alpha 4a (TUBA4A), transcript variant 1	REVIEWED	mRNA
PHYHIP	NM_014759.3	5.890	0	phytanoyl-CoA 2-hydroxylase interacting protein (PHYHIP), transcript variant 2	VALIDATED	mRNA
AP2M1	NM_004068.3	2.030	0	adaptor related protein complex 2 mu 1 subunit (AP2M1), transcript variant 1	REVIEWED	mRNA
MTRNR2L6	NM_001190487.2	-3.170	0	MT-RNR2-like 6 (MTRNR2L6)	VALIDATED	mRNA
MTRNR2L1	NM_001190452.1	-3.480	0	MT-RNR2-like 1 (MTRNR2L1)	VALIDATED	mRNA
STXBP1	NM_001032221.3	3.160	0	syntaxin binding protein 1 (STXBP1), transcript variant 2	REVIEWED	mRNA
UCHL1	NM_004181.4	3.350	0	ubiquitin C-terminal hydrolase L1 (UCHL1)	REVIEWED	mRNA
ARGLU1	NM_018011.3	-1.640	0	arginine and glutamate rich 1 (ARGLU1)	VALIDATED	mRNA
SYT5	XM_006723339.3	4.280	0	PREDICTED: Homo sapiens synaptotagmin 5 (SYT5), transcript variant X1	MODEL	mRNA
ATP2A2	XR_243009.2	5.920	0	PREDICTED: Homo sapiens ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 2 (ATP2A2), transcript variant X2	MODEL	misc_RNA
GLS	NM_014905.4	4.700	0	glutaminase (GLS), transcript variant 1	REVIEWED	mRNA
YWHAH	NM_003405.3	3.230	0	tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein eta (YWHAH)	REVIEWED	mRNA
ANKRD34A	NM_001039888.3	2.700	0	ankyrin repeat domain 34A (ANKRD34A)	VALIDATED	mRNA
SCRT1	NM_031309.5	3.720	0	scratch family transcriptional repressor 1 (SCRT1)	REVIEWED	mRNA
PKM	XM_017022313.1	10.200	0	PREDICTED: Homo sapiens pyruvate kinase, muscle (PKM), transcript variant X5	MODEL	mRNA
MBP	NM_001025092.1	-3.410	0	myelin basic protein (MBP), transcript variant 4	REVIEWED	mRNA
CALY	NM_015722.3	6.410	0	calcyon neuron specific vesicular protein (CALY), transcript variant 1	VALIDATED	mRNA
KIF5B	XM_017016224.1	-3.080	0	PREDICTED: Homo sapiens kinesin family member 5B (KIF5B), transcript variant X1	MODEL	mRNA
GOLGA7	NM_016099.2	-2.620	0	golgin A7 (GOLGA7), transcript variant 1	VALIDATED	mRNA
IGIP	NM_001007189.1	-0.953	0	IgA inducing protein (IGIP)	VALIDATED	mRNA
RBMX	NM_001164803.1	-1.660	0	RNA binding motif protein, X-linked (RBMX), transcript variant 2	REVIEWED	mRNA
PDXP	NM_020315.4	2.630	0	pyridoxal phosphatase (PDXP)	VALIDATED	mRNA
THY1	NM_001311162.1	2.750	0	Thy-1 cell surface antigen (THY1), transcript variant 3	REVIEWED	mRNA
SYT13	NM_020826.2	3.820	0	synaptotagmin 13 (SYT13), transcript variant 1	REVIEWED	mRNA
NCDN	NM_001014841.1	3.290	0	neurochondrin (NCDN), transcript variant 2	REVIEWED	mRNA
TMEM59L	NM_012109.2	2.430	0	transmembrane protein 59 like (TMEM59L)	REVIEWED	mRNA
SEPT7	XM_017012862.1	-1.630	0	PREDICTED: Homo sapiens septin 7 (SEPT7), transcript variant X11	MODEL	mRNA
ATP1A3	NM_001256213.1	3.870	0	ATPase Na+/K+ transporting subunit alpha 3 (ATP1A3), transcript variant 2	REVIEWED	mRNA
SPINT2	NM_021102.3	4.400	0	serine peptidase inhibitor, Kunitz type 2 (SPINT2), transcript variant a	REVIEWED	mRNA
LOC101930059	XR_431133.3	-2.270	0	PREDICTED: Homo sapiens uncharacterized LOC101930059 (LOC101930059), transcript variant X4	MODEL	ncRNA
SEPT3	NM_019106.5	1.770	0	septin 3 (SEPT3), transcript variant B	REVIEWED	mRNA
MTRNR2L8	NM_001190702.1	-3.560	0	MT-RNR2-like 8 (MTRNR2L8)	VALIDATED	mRNA
HPCAL1	NM_002149.3	5.100	0	hippocalcin like 1 (HPCAL1), transcript variant 1	REVIEWED	mRNA
HOOK3	NM_032410.3	-1.330	0	hook microtubule tethering protein 3 (HOOK3)	VALIDATED	mRNA

The list of “developmental” genes that are differentially expressed in Autism vs Control are saved in DESeq2_glist_adult.cortex.txt (Entrez GeneIDs), in DESeq2_slist_adult.cortex.txt (gene symbols) while the list of “developmental” transcripts are saved in DESeq2_tlist_adult.cortex.txt.

Save gene lists

unlink('DESeq2_tlist_adult.txt')
unlink('DESeq2_glist_adult.txt')
unlink('DESeq2_slist_adult.txt')
lapply(devAdultCA[order(devAdultCA)], write, 'DESeq2_tlist_adult.cortex.txt', append=TRUE)
lapply(devAdultCAg[order(devAdultCAg)], write, 'DESeq2_glist_adult.cortex.txt', append=TRUE)
lapply(devAdultCAs[order(devAdultCAs)], write, 'DESeq2_slist_adult.cortex.txt', append=TRUE)
save(file='DESeq2_tlist.cortex.RData', devAdultCA2)
save(file='DESeq2_glist.cortex.RData', devAdultCAg, devAdultCAs)

Gene trajectories

Up-regulated

devAdultCAup2 <- intersect((signifAdult2 %>% filter(log2FoldChange > 0))[['target_id']], signifAdultCA2$target_id)
for (i in devAdultCAup2[1:20]) {
  obs1 <- assay(rld2) %>% 
    as.data.frame() %>% 
    rownames_to_column('target_id') %>% 
    filter(target_id == i) %>% 
    gather(colnames(assay(rld2)), key = 'sample', value = 'est_counts') %>%
    left_join(metadata2, by = 'sample') %>% 
    left_join(ttg, by = 'target_id')
  symbol <- obs1$symbol[1]
  cat('####', symbol, '\n\n')
  g <- ggplot(obs1, aes(x=grp, y=exp(est_counts), color=grp)) +
    geom_point(shape = 1, size = 3) +
    geom_boxplot() +
    labs(title = paste0(obs1$symbol[1],' (',obs1$target_id[1],')'), 
         subtitle = str_c(str_wrap(obs1$description[1], width=55), collapse="\n"),
         x = 'group',
         y = 'estimated counts',
         fill = "Group") +
    theme(legend.position = c(0.9, 0.85), 
          legend.title = element_text(face = "bold.italic"), 
          legend.box.background = element_rect(colour = "black", fill = alpha('white', alpha = 0.6)), 
          axis.text.x = element_text(angle = 45, hjust = 1), 
          plot.title = element_text(hjust = 0.4), 
          plot.subtitle = element_text(hjust = 0.4) 
    ) 
  print(g)
  cat('\n\n')
}

SPOCK2

RIMS3

WBP2

MLF2

DDN

ACO2

CAMK2A

ITM2B

JUND

JUND

COX5B

ALDOA

CKB

MTRNR2L6

CA11

THY1

MTRNR2L1

TSPYL2

NEAT1

NAT8L

Down-regulated

devAdultCAdown2 <- intersect((signifAdult2 %>% filter(log2FoldChange < 0))[['target_id']], signifAdultCA$target_id)
for (x in devAdultCAdown2) {
  obs1 <- assay(rld2) %>% 
    as.data.frame() %>% 
    rownames_to_column('target_id') %>% 
    filter(target_id == x) %>% 
    gather(colnames(assay(rld2)), key = 'sample', value = 'est_counts') %>% 
    left_join(metadata2, by = 'sample') %>% 
    left_join(ttg, by = 'target_id')
  symbol <- obs1$symbol[1]
  cat('####', symbol, '\n\n')
  g <- ggplot(obs1, aes(x=grp, y=exp(est_counts), color=grp)) + 
    geom_point(shape = 1, size = 3) + 
    geom_boxplot() + 
    labs(title=paste0(obs1$symbol[1],' (',obs1$target_id[1],')'), 
         subtitle=str_c(str_wrap(obs1$description[1], width=55), collapse="\n"), 
         x = 'group', 
         y = 'estimated counts', 
         fill = "Group") +
    scale_fill_discrete(name = "Group") + 
    theme(legend.position = c(0.9, 0.85), 
          legend.title = element_text(face = "bold.italic"), 
          legend.box.background = element_rect(colour = "black", fill = alpha('white', alpha = 0.6)), 
          axis.text.x = element_text(angle = 45, hjust = 1), 
          plot.title = element_text(hjust = 0.4), 
          plot.subtitle = element_text(hjust = 0.4) 
    )
  print(g)
  cat('\n\n')
}

CHD3

SPAST

AFF2

EIF3C

CEP170

GRIA1

NCKAP5

SELE

GO analysis

Perform GO analysis using the goseq package, which normalizes GO term counts by transcript length.

ttgfile <- '/share/db/refseq/human/refseq.108.rna.t2g.txt'
ttg <- read_tsv(ttgfile)

refseq.lengths <- read_tsv('/share/db/refseq/human/refseq.108.rna.lengths', col_names = c('target_id', 'length'))
ttg <- ttg %>% full_join(refseq.lengths, by = 'target_id')
gene_lengths <- ttg %>%
  dplyr::select(gene_id,length) %>%
  filter(!is.na(gene_id)) %>%
  group_by(gene_id) %>%
  summarise(median_length = median(length)) %>%
  dplyr::select(gene_id, median_length) %>%
  spread(gene_id, median_length) %>%
  as.list() %>%
  unlist()

genes <- rep(0, length(unique(na.omit(ttg$gene_id))))
names(genes) <- unique(na.omit(ttg$gene_id))
genes[as.character(unique(na.omit(signifAdultCA2$gene_id)))] <- 1

pwf <- nullp(DEgenes = genes, genome = NULL, id = NULL, bias.data = gene_lengths)

GO.wall <- goseq(pwf, "hg19", "knownGene")
t1 <- GO.wall %>% filter(over_represented_pvalue < 0.05) %>% group_by(ontology) %>% summarise(n=n())
t2 <- GO.wall %>% filter(ontology == 'BP' & over_represented_pvalue < 0.05 & grepl('(neur[oa])|nerv', term)) %>%
  mutate(category = paste0('[',category,'](http://amigo.geneontology.org/amigo/term/',category,')')) %>%
  dplyr::select(c('category', 'term', 'numDEInCat', 'numInCat', 'over_represented_pvalue'))
t3 <- GO.wall %>% filter(ontology == 'MF' & over_represented_pvalue < 0.05) %>% head(n=50) %>% 
  mutate(category = paste0('[',category,'](http://amigo.geneontology.org/amigo/term/',category,')')) %>%
  dplyr::select(c('category', 'term', 'numDEInCat', 'numInCat', 'over_represented_pvalue'))
t4 <- GO.wall %>% filter(ontology == 'CC' & over_represented_pvalue < 0.05) %>% head(n=50) %>% 
  mutate(category = paste0('[',category,'](http://amigo.geneontology.org/amigo/term/',category,')')) %>%
  dplyr::select(c('category', 'term', 'numDEInCat', 'numInCat', 'over_represented_pvalue'))

Summary

Number of significant categories in different ontologies

kable(t1, format = 'markdown')

ontology	n
BP	1914
CC	397
MF	388

Biological process

Summary of biological process terms related to neuronal development

kable(t2, format = 'markdown')

category	term	numDEInCat	numInCat	over_represented_pvalue
GO:0007399	nervous system development	1081	2195	0.0000000
GO:0031175	neuron projection development	478	829	0.0000000
GO:0048666	neuron development	526	974	0.0000000
GO:0048812	neuron projection morphogenesis	338	570	0.0000000
GO:0022008	neurogenesis	726	1443	0.0000000
GO:0048699	generation of neurons	678	1346	0.0000000
GO:0010975	regulation of neuron projection development	247	402	0.0000000
GO:0030182	neuron differentiation	619	1225	0.0000000
GO:0048667	cell morphogenesis involved in neuron differentiation	301	519	0.0000000
GO:0051960	regulation of nervous system development	400	755	0.0000000
GO:0050767	regulation of neurogenesis	356	667	0.0000000
GO:0045664	regulation of neuron differentiation	301	547	0.0000000
GO:0010976	positive regulation of neuron projection development	152	236	0.0000000
GO:0007269	neurotransmitter secretion	98	138	0.0000000
GO:0051962	positive regulation of nervous system development	251	453	0.0000000
GO:0001505	regulation of neurotransmitter levels	120	184	0.0000000
GO:0006836	neurotransmitter transport	121	186	0.0000000
GO:0050769	positive regulation of neurogenesis	217	392	0.0000000
GO:0045666	positive regulation of neuron differentiation	181	318	0.0000000
GO:0007417	central nervous system development	434	898	0.0000000
GO:1990138	neuron projection extension	92	140	0.0000000
GO:0051961	negative regulation of nervous system development	135	256	0.0000001
GO:0050768	negative regulation of neurogenesis	124	237	0.0000004
GO:0048168	regulation of neuronal synaptic plasticity	36	50	0.0000010
GO:0097485	neuron projection guidance	118	223	0.0000010
GO:0007270	neuron-neuron synaptic transmission	76	134	0.0000021
GO:0046928	regulation of neurotransmitter secretion	36	53	0.0000024
GO:0010977	negative regulation of neuron projection development	68	119	0.0000075
GO:0048857	neural nucleus development	40	62	0.0000085
GO:0048169	regulation of long-term neuronal synaptic plasticity	19	23	0.0000095
GO:0051588	regulation of neurotransmitter transport	40	64	0.0000132
GO:0045665	negative regulation of neuron differentiation	96	184	0.0000156
GO:0070997	neuron death	147	303	0.0000199
GO:0021895	cerebral cortex neuron differentiation	18	22	0.0000367
GO:0051402	neuron apoptotic process	109	218	0.0000438
GO:0048791	calcium ion-regulated exocytosis of neurotransmitter	21	30	0.0000513
GO:0043523	regulation of neuron apoptotic process	98	194	0.0000669
GO:1901214	regulation of neuron death	130	269	0.0000713
GO:0060052	neurofilament cytoskeleton organization	9	9	0.0000756
GO:0008038	neuron recognition	24	34	0.0000823
GO:0007272	ensheathment of neurons	60	112	0.0002505
GO:1990089	response to nerve growth factor	29	46	0.0003811
GO:0001764	neuron migration	70	136	0.0004050
GO:0098877	neurotransmitter receptor transport to plasma membrane	10	11	0.0005700
GO:0021954	central nervous system neuron development	40	70	0.0005733
GO:1901216	positive regulation of neuron death	38	68	0.0009069
GO:0031102	neuron projection regeneration	29	49	0.0010986
GO:0098969	neurotransmitter receptor transport to postsynaptic membrane	9	10	0.0012879
GO:1990090	cellular response to nerve growth factor stimulus	26	43	0.0020896
GO:0021626	central nervous system maturation	6	6	0.0023247
GO:0099637	neurotransmitter receptor transport	11	14	0.0028902
GO:0014041	regulation of neuron maturation	9	11	0.0033223
GO:0021952	central nervous system projection neuron axonogenesis	15	22	0.0034720
GO:0072578	neurotransmitter-gated ion channel clustering	7	8	0.0036446
GO:0099639	neurotransmitter receptor transport, endosome to plasma membrane	6	6	0.0038856
GO:0042551	neuron maturation	25	43	0.0044646
GO:0050905	neuromuscular process	50	100	0.0045270
GO:0070570	regulation of neuron projection regeneration	16	25	0.0049648
GO:0043524	negative regulation of neuron apoptotic process	65	136	0.0057711
GO:1901215	negative regulation of neuron death	85	183	0.0066481
GO:0043525	positive regulation of neuron apoptotic process	27	49	0.0073235
GO:0099601	regulation of neurotransmitter receptor activity	20	34	0.0073346
GO:0050885	neuromuscular process controlling balance	28	52	0.0083336
GO:0048170	positive regulation of long-term neuronal synaptic plasticity	5	5	0.0084868
GO:0098887	neurotransmitter receptor transport, endosome to postsynaptic membrane	5	5	0.0095453
GO:0021892	cerebral cortex GABAergic interneuron differentiation	9	12	0.0103254
GO:0021955	central nervous system neuron axonogenesis	17	28	0.0111151
GO:0060384	innervation	16	25	0.0114348
GO:0014042	positive regulation of neuron maturation	4	4	0.0156775
GO:0097118	neuroligin clustering involved in postsynaptic membrane assembly	4	4	0.0162502
GO:0021953	central nervous system neuron differentiation	77	173	0.0204099
GO:0051581	negative regulation of neurotransmitter uptake	3	3	0.0211798
GO:1902284	neuron projection extension involved in neuron projection guidance	13	21	0.0213159
GO:0048172	regulation of short-term neuronal synaptic plasticity	10	15	0.0262543
GO:0021884	forebrain neuron development	17	31	0.0272162
GO:2000178	negative regulation of neural precursor cell proliferation	11	18	0.0278641
GO:0001504	neurotransmitter uptake	13	23	0.0288950
GO:0038179	neurotrophin signaling pathway	19	34	0.0293080
GO:0098881	exocytic insertion of neurotransmitter receptor to plasma membrane	4	4	0.0300200
GO:0098967	exocytic insertion of neurotransmitter receptor to postsynaptic membrane	4	4	0.0300200
GO:0021843	substrate-independent telencephalic tangential interneuron migration	7	10	0.0319943
GO:0021894	cerebral cortex GABAergic interneuron development	6	8	0.0339987
GO:0007422	peripheral nervous system development	35	72	0.0353098
GO:2001224	positive regulation of neuron migration	7	10	0.0362737
GO:0038180	nerve growth factor signaling pathway	6	8	0.0397597
GO:0036480	neuron intrinsic apoptotic signaling pathway in response to oxidative stress	6	8	0.0400716
GO:1903376	regulation of oxidative stress-induced neuron intrinsic apoptotic signaling pathway	6	8	0.0400716
GO:0099072	regulation of postsynaptic specialization membrane neurotransmitter receptor levels	5	6	0.0416252
GO:0001956	positive regulation of neurotransmitter secretion	8	12	0.0463670
GO:0036475	neuron death in response to oxidative stress	12	20	0.0486823

Molecular function

Summary of the 50th most significant “molecular functions” terms

kable(t3, format = 'markdown')

category	term	numDEInCat	numInCat	over_represented_pvalue
GO:0005515	protein binding	4443	10836	0e+00
GO:0005488	binding	5463	14089	0e+00
GO:0003723	RNA binding	767	1596	0e+00
GO:0019899	enzyme binding	846	1816	0e+00
GO:0003824	catalytic activity	2354	5720	0e+00
GO:0008092	cytoskeletal protein binding	419	844	0e+00
GO:0016740	transferase activity	1017	2366	0e+00
GO:1901265	nucleoside phosphate binding	910	2077	0e+00
GO:0000166	nucleotide binding	909	2076	0e+00
GO:0032553	ribonucleotide binding	816	1855	0e+00
GO:0035639	purine ribonucleoside triphosphate binding	790	1795	0e+00
GO:0017076	purine nucleotide binding	812	1851	0e+00
GO:0032555	purine ribonucleotide binding	806	1838	0e+00
GO:0036094	small molecule binding	1019	2378	0e+00
GO:0043168	anion binding	1120	2650	0e+00
GO:0016772	transferase activity, transferring phosphorus-containing groups	451	983	0e+00
GO:0016301	kinase activity	389	842	0e+00
GO:0043167	ion binding	2377	6030	0e+00
GO:0016773	phosphotransferase activity, alcohol group as acceptor	358	774	0e+00
GO:0019901	protein kinase binding	262	540	0e+00
GO:0019900	kinase binding	292	611	0e+00
GO:0044877	macromolecular complex binding	417	911	0e+00
GO:0015631	tubulin binding	152	285	0e+00
GO:0097367	carbohydrate derivative binding	922	2188	0e+00
GO:0019904	protein domain specific binding	312	660	0e+00
GO:0030554	adenyl nucleotide binding	654	1511	0e+00
GO:0016817	hydrolase activity, acting on acid anhydrides	384	833	0e+00
GO:0016818	hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides	383	831	0e+00
GO:0004674	protein serine/threonine kinase activity	218	445	0e+00
GO:0032559	adenyl ribonucleotide binding	648	1500	0e+00
GO:0004672	protein kinase activity	299	640	0e+00
GO:0016462	pyrophosphatase activity	381	829	0e+00
GO:0005524	ATP binding	632	1464	0e+00
GO:0060589	nucleoside-triphosphatase regulator activity	172	348	1e-07
GO:0019905	syntaxin binding	53	83	1e-07
GO:0008047	enzyme activator activity	238	502	1e-07
GO:0003924	GTPase activity	144	273	1e-07
GO:1901363	heterocyclic compound binding	2278	5812	1e-07
GO:0017111	nucleoside-triphosphatase activity	360	789	1e-07
GO:0044769	ATPase activity, coupled to transmembrane movement of ions, rotational mechanism	26	31	2e-07
GO:0045296	cadherin binding	154	304	3e-07
GO:0097159	organic cyclic compound binding	2306	5896	4e-07
GO:0003779	actin binding	193	399	5e-07
GO:0003954	NADH dehydrogenase activity	30	38	5e-07
GO:0008137	NADH dehydrogenase (ubiquinone) activity	30	38	5e-07
GO:0050136	NADH dehydrogenase (quinone) activity	30	38	5e-07
GO:0030695	GTPase regulator activity	151	310	7e-07
GO:0050839	cell adhesion molecule binding	214	451	7e-07
GO:0017075	syntaxin-1 binding	17	19	8e-07
GO:0008022	protein C-terminus binding	98	178	1e-06

Cellular component

Summary of the 50th most significant “cellular component” terms

kable(t4, format = 'markdown')

category	term	numDEInCat	numInCat	over_represented_pvalue
GO:0044424	intracellular part	5573	13845	0
GO:0005737	cytoplasm	4598	10995	0
GO:0005622	intracellular	5658	14144	0
GO:0044444	cytoplasmic part	3881	9072	0
GO:0097458	neuron part	755	1307	0
GO:0043005	neuron projection	576	949	0
GO:0044446	intracellular organelle part	3636	8609	0
GO:0043229	intracellular organelle	4875	12081	0
GO:0044422	organelle part	3703	8817	0
GO:0045202	synapse	494	810	0
GO:0043231	intracellular membrane-bounded organelle	4257	10390	0
GO:0044456	synapse part	419	675	0
GO:0043227	membrane-bounded organelle	4853	12109	0
GO:0043226	organelle	5179	13051	0
GO:0005829	cytosol	2133	4749	0
GO:0030424	axon	255	385	0
GO:0036477	somatodendritic compartment	387	656	0
GO:0032991	macromolecular complex	2071	4717	0
GO:0031090	organelle membrane	1298	2800	0
GO:0120025	plasma membrane bounded cell projection	858	1729	0
GO:0042995	cell projection	899	1837	0
GO:0044464	cell part	6133	16128	0
GO:0098794	postsynapse	255	407	0
GO:0005623	cell	6138	16154	0
GO:0030425	dendrite	280	462	0
GO:0031975	envelope	573	1121	0
GO:0031967	organelle envelope	572	1120	0
GO:0044428	nuclear part	1826	4222	0
GO:1902494	catalytic complex	644	1284	0
GO:0031981	nuclear lumen	1676	3866	0
GO:0043209	myelin sheath	123	167	0
GO:0098793	presynapse	213	343	0
GO:0005739	mitochondrion	787	1657	0
GO:0031974	membrane-enclosed lumen	2066	4889	0
GO:0043233	organelle lumen	2066	4889	0
GO:0070013	intracellular organelle lumen	2066	4889	0
GO:0005654	nucleoplasm	1433	3281	0
GO:0044429	mitochondrial part	488	970	0
GO:0005740	mitochondrial envelope	372	703	0
GO:0030054	cell junction	585	1192	0
GO:0098800	inner mitochondrial membrane protein complex	89	112	0
GO:0033267	axon part	123	175	0
GO:0019866	organelle inner membrane	292	531	0
GO:0005743	mitochondrial inner membrane	265	472	0
GO:0044297	cell body	271	483	0
GO:0098798	mitochondrial protein complex	99	134	0
GO:0098796	membrane protein complex	345	641	0
GO:0098984	neuron to neuron synapse	134	201	0
GO:0044463	cell projection part	499	1002	0
GO:0044455	mitochondrial membrane part	127	188	0

Heatmap of DE genes

Adult

myttg <- ttg %>% filter(target_id %in% devAdultCA2) %>% dplyr::select(c('target_id', 'symbol'))
symbols <- myttg$symbol
names(symbols) <- myttg$target_id
sel_vars <- metadata2 %>%
  filter(age == 'Adult' | grepl('CP', sample)) %>%
  dplyr::select('sample') %>%
  as.list() %>% unlist() %>% as.vector()
obs <-
  counts(ddsWald2, normalized=TRUE) %>%
  # assay(rld2) %>%
  as.data.frame() %>%
  rownames_to_column('target_id') %>%
  filter(target_id %in% devAdultCA2) %>%
  dplyr::select(target_id, sel_vars) %>%
  gather(sel_vars, key = 'sample', value = 'est_counts') %>%
  spread(target_id, est_counts) %>%
  remove_rownames() %>%
  column_to_rownames(var="sample") %>%
  as.matrix()
colnames(obs) <- symbols[colnames(obs)]
labels <- metadata2 %>%
  filter(age == 'Adult' | grepl('CP', sample)) %>%
  dplyr::select(c('sample','age')) %>%
  arrange(sample) %>%
  remove_rownames() %>%
  column_to_rownames(var='sample')
Rowv <- obs %>%
  dist(method="euclidean") %>%
  hclust(method = "complete") %>%
  as.dendrogram %>%
  # rotate(c(6:7,1:5)) %>%
  color_branches(k = 3) %>%
  set("branches_lwd", 4)
obs[which(obs == 0)] <- 1
par(mar=c(5,4,4,2)+0.1)
heatmap.2(obs, Rowv = Rowv, trace="none", margins=c(5,9), srtCol = 45,
          symm = F, symkey = F, symbreaks = F, scale="none")

Correlation plot for all genes

This correlation plot uses all the transcripts annotated as PROVISIONAL, REVIEWED or VALIDATED in RefSeq (the high confidence categories)

cor_tlist <- ttg %>% filter(status %in% c('PROVISIONAL', 'REVIEWED', 'VALIDATED')) %>% .[['target_id']]
obs <- 
  counts(ddsWald2, normalized = TRUE) %>%
  # assay(rld2) %>%
  as.data.frame() %>%
  rownames_to_column('target_id') %>%
  filter(target_id %in% cor_tlist) %>%
  gather(colnames(assay(rld2)), key = 'sample', value = 'est_counts') %>% 
  full_join(metadata2,  by = 'sample') %>%
  dplyr::select(c('grp','target_id','est_counts')) %>%
  group_by(grp, target_id) %>%
  summarise(est_counts = mean(est_counts)) %>%
  spread(target_id, est_counts) %>%
  remove_rownames() %>%
  column_to_rownames(var="grp") %>%
  t() %>%
  as.tibble() %>%
  filter_at(vars(matches('Child|Adult')), any_vars(. >= 0.5))
colnames(obs) <- metadata2 %>% 
  dplyr::select(grp) %>% 
  group_by(grp) %>% 
  summarise(group_count=n()) %>% 
  mutate(group = paste0(grp,'(',group_count,')')) %>%
  .[['group']]
colpal=brewer.pal(n=8, name="RdBu")
corrplot(cor(obs),
         method="circle",
         type="lower",
         col=colpal,
         cl.lim=c(0,1),
         number.cex = 0.6,
         addCoef.col = "black",
         tl.col="black",
         tl.cex = 0.75,
         tl.srt=45,
         diag=FALSE)

Correlation plot for DE genes

This correlation plot uses the ‘significant’ transcripts, which are DE between Fetal and Control samples and also between Autism and Control samples.

Up-regulated

obs <- 
  counts(ddsWald2, normalized = TRUE) %>%
  # assay(rld2) %>%
  as.data.frame() %>%
  rownames_to_column('target_id') %>%
  filter(target_id %in% devAdultCAup2) %>%
  gather(colnames(assay(ddsWald2)), key = 'sample', value = 'est_counts') %>% 
  full_join(metadata2,  by = 'sample') %>%
  dplyr::select(c('grp','target_id','est_counts')) %>%
  group_by(grp, target_id) %>%
  summarise(est_counts = mean(est_counts)) %>%
  spread(target_id, est_counts) %>%
  remove_rownames() %>%
  column_to_rownames(var="grp") %>%
  t() %>%
  as.tibble() %>%
  filter_at(vars(matches('Adult')), any_vars(. >= 0.5))
colnames(obs) <- metadata2 %>% 
  dplyr::select(grp) %>% 
  group_by(grp) %>% 
  summarise(group_count=n()) %>% 
  mutate(group = paste0(grp,'(',group_count,')')) %>%
  .[['group']]
colpal=brewer.pal(n=8, name="RdBu")
corrplot(cor(obs),
         method="circle",
         type="lower",
         col=colpal,
         #cl.lim=c(0,1),
         number.cex = 0.6,
         addCoef.col = "black",
         tl.col="black",
         tl.cex = 0.75,
         tl.srt=45,
         diag=FALSE)

Down-regulated

obs <- 
  counts(ddsWald2, normalized=TRUE) %>%
  # assay(rld2) %>% 
  as.data.frame() %>%
  rownames_to_column('target_id') %>%
  filter(target_id %in% devAdultCAdown2) %>%
  gather(colnames(assay(ddsWald2)), key = 'sample', value = 'est_counts') %>% 
  full_join(metadata2,  by = 'sample') %>%
  dplyr::select(c('grp','target_id','est_counts')) %>%
  group_by(grp, target_id) %>%
  summarise(est_counts = mean(est_counts)) %>%
  spread(target_id, est_counts) %>%
  remove_rownames() %>%
  column_to_rownames(var="grp") %>%
  t() %>%
  as.tibble() %>%
  filter_at(vars(matches('Child|Adult')), any_vars(. >= 0.5))
colnames(obs) <- metadata2 %>% 
  dplyr::select(grp) %>% 
  group_by(grp) %>% 
  summarise(group_count=n()) %>% 
  mutate(group = paste0(grp,'(',group_count,')')) %>%
  .[['group']]
colpal=brewer.pal(n=8, name="RdBu")
corrplot(cor(obs),
         method="circle",
         type="lower",
         col=colpal,
         #cl.lim=c(0,1),
         number.cex = 0.6,
         addCoef.col = "black",
         tl.col="black",
         tl.cex = 0.75,
         tl.srt=45,
         diag=FALSE)