LONDON blood dataset
Lanyu Zhang, Tiago C. Silva, Lily Wang
2020-04-26
1 Data retrival
library(dplyr)cohort <- "LONDON_blood"
data.dir <- file.path("DATASETS/",cohort,"/")
data.dir.table <- "DATASETS/Summary_Table/"
data.dir.raw <- file.path(data.dir,"/step1_download/")
data.dir.clinical.filter <- file.path(data.dir,"/step2_clinical_available_filtering/")
data.dir.probes.qc <- file.path(data.dir,"/step3_probesQC_filtering/")
data.dir.probes.normalization <- file.path(data.dir,"/step4_normalization/")
data.dir.pca <- file.path(data.dir,"/step5_pca_filtering/")
data.dir.neuron <- file.path(data.dir,"/step6_neuron_comp/")
# data.dir.single.cpg.pval <- file.path(data.dir,"/step7_single_cpg_pval/")
data.dir.residuals <- file.path(data.dir,"/step7_residuals/")
data.dir.median <- file.path(data.dir,"/step8_median/")
data.dir.validation <- file.path(data.dir,"/step9_validation/")
for(p in grep("dir",ls(),value = T)) dir.create(get(p),recursive = TRUE,showWarnings = FALSE)Required R library GEOquery can be installed as following:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("GEOquery")Source: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE59685
library(GEOquery)
library(SummarizedExperiment)
test <- getGEO(GEO = "GSE59685",destdir = data.dir,GSEMatrix = TRUE,)
GSE59685 <- test$GSE59685_series_matrix.txt.gz %>% makeSummarizedExperimentFromExpressionSet()
metadata <- colData(GSE59685)
## Failed to create assayData with pacakage GEOquery, so we use the beta matrix
## directly downloaded online instead
library(data.table)
assayData <- fread(
"ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE59nnn/GSE59685/suppl/GSE59685_betas.csv.gz",
skip = 5,
header = TRUE,
sep =','
)
## Turn assayData from "data.table" and "data.frame" to "data.frame" only
assayData <- data.frame(assayData)
## Exclude row 1 as it's old sample id
assayData <- assayData[-1, ]
## Create row names based on V1
row.names(assayData) <- assayData$V1
## Delete column V1 after creating rownames
assayData <- assayData[, 2:ncol(assayData)]
assayData <- data.matrix(assayData)
## Save RDS file
saveRDS(assayData, paste0(data.dir.raw, "GSE59685_assay.RDS"))
### Extract and save phenotype dataset
phenoData <- colData(GSE59685)
phenoData <- phenoData[match(colnames(assayData),rownames(phenoData)),]
saveRDS(phenoData, paste0(data.dir.raw, "GSE59685_pheno.RDS"))
write.csv(phenoData, paste0(data.dir.raw, "GSE59685_pheno.csv"))2 Data Pre-processing
Description:
Subset Files
- subset pheno data to
- whole blood tissue
- variables – sample, subject_id, sentrix_id, slide, age, sex, stage
- subset methylation data to match subjects in pheno data
Input: GSE59685_pheno.csv, GSE59685_assay.RDS
Output: pheno_BLOOD_df.RDS, beta110_BLOOD_mat.RDS
##### 1. Subset pheno data #####################################################
### Read in phenotype data
phenoRaw_df <- readr::read_csv(
paste0(data.dir.raw, "GSE59685_pheno.csv"),
col_types = readr::cols()
)
dim(phenoRaw_df)## [1] 531 49### Subset rows and columns
### Subset rows and columns
phenoBLOOD_df <- phenoRaw_df[
(phenoRaw_df$source_name_ch1 == "whole blood"),
c("geo_accession", "subjectid.ch1", "barcode.ch1",
"age.blood.ch1", "Sex.ch1", "ad.disease.status.ch1", "braak.stage.ch1")
]
### Rename vars
colnames(phenoBLOOD_df) <- c(
"sample", "subject.id", "sentrix_id", "age.blood", "sex", "status", "stage"
)
### Turn factors into characters
phenoBLOOD_df$sample <- as.character(phenoBLOOD_df$sample)
phenoBLOOD_df$subject.id <- as.character(phenoBLOOD_df$subject.id)
phenoBLOOD_df$sentrix_id <- as.character(phenoBLOOD_df$sentrix_id)
phenoBLOOD_df$sex <- as.character(phenoBLOOD_df$sex)
phenoBLOOD_df$status <- as.character(phenoBLOOD_df$status)
phenoBLOOD_df$stage <- as.integer(as.character(phenoBLOOD_df$stage))
### Get slide from sentrix_id
# e.g. "6042316048_R05C01"(sentrix_id) -- "6042316048"(slide) and "R05C01"(array)
sentrixID_mat <- do.call(rbind, strsplit(phenoBLOOD_df$sentrix_id, "_"))
phenoBLOOD_df$slide <- sentrixID_mat[, 1]
### Order final pheno_df
pheno_df <- phenoBLOOD_df[, c(
"sample", "subject.id", "sentrix_id", "slide",
"age.blood", "sex", "status", "stage"
)]
dim(pheno_df)## [1] 80 8##### 2. Subset methylation data ###############################################
### Read in methylation data
beta_mat <- readRDS(paste0(data.dir.raw, "GSE59685_assay.RDS")) #dim: 485577 531
### Subset methylation data based on pheno data
beta_mat <- beta_mat[, match(pheno_df$sample,colnames(beta_mat))] # dim: 485577 110##### 3. Output datasets #######################################################
## phenotype dataset
saveRDS(pheno_df, paste0(data.dir.clinical.filter, "pheno_BLOOD_withStatusExclude_df.RDS"))
## methylation beta values dataset
saveRDS(beta_mat, paste0(data.dir.clinical.filter, "beta_BLOOD_withStatusExclude_mat.RDS")) 2.1 Probes QC
- keep only probes that start with “cg”
- drop probes that are on X/Y
- drop probes where SNP with MAF >= 0.01 was present in the last 5 bp of the probe.
Input: beta110_PFC_mat.RDS
Output: beta110_PFC_CG_XY_SNPfiltered_mat.RDS
##### 1. keep on probes with start with "cg" ###################################
beta_mat <- readRDS(paste0(data.dir.clinical.filter, "beta_BLOOD_withStatusExclude_mat.RDS"))
nb.probes <- nrow(beta_mat)
nb.samples <- ncol(beta_mat)
nb.samples.with.clinical <- ncol(beta_mat)
beta_mat <- beta_mat[grep("cg",rownames(beta_mat)),]
dim(beta_mat)## [1] 482421 80nb.probes.cg <- nrow(beta_mat)##### 2. drop probes that are on X/Y ###########################################
##### 3. drop probes where SNP with MAF >= 0.01 in the last 5 bp of the probe ##
library(DMRcate)beta_CG_XY_SNPfiltered_mat <- rmSNPandCH(
object = beta_mat,
dist = 5,
mafcut = 0.01,
and = TRUE,
rmcrosshyb = FALSE,
rmXY = TRUE
)
dim(beta_CG_XY_SNPfiltered_mat)## [1] 450793 80nb.probes.cg.dmrcate <- nrow(beta_CG_XY_SNPfiltered_mat)
##### 4. Output datasets #######################################################
saveRDS(
beta_CG_XY_SNPfiltered_mat,
paste0(data.dir.probes.qc, "beta_CG_XY_SNPfiltered_withStatusExclude_mat.RDS")
)2.2 Samples QC
- Quantile normalization and BMIQ normalization
Input:
- beta_CG_XY_SNPfiltered_mat.RDS
- pheno_PFC_df.RDS
- full.annot.RDS
Output:
- London_PFC_QNBMIQ.RDS
2.2.1 Quantile normalization
library(lumi)
betaQN <- lumiN(x.lumi = beta_mat, method = "quantile")
dim(betaQN)##### 5. BMIQ ##################################################################
library(wateRmelon)
library(RPMM)
library(sesame)
library(sesameData)### Order annotation in the same order as beta matrix
annotType <- sesameDataGet("HM450.hg19.manifest")
annotType$designTypeNumeric <- ifelse(annotType$designType == "I",1,2)
### Density plot for type I and type II probes
library(sm)
betaQNCompleteCol1 <- betaQN[complete.cases(betaQN[,1]), ]
annotTypeCompleteCol1 <- annotType[row.names(betaQNCompleteCol1), ]
sm.density.compare(
betaQNCompleteCol1[,1],
annotTypeCompleteCol1$designTypeNumeric
)
type12 <- annotType$designTypeNumeric[match(rownames(betaQN),names(annotType))]### BMIQ
set.seed (946)
doParallel::registerDoParallel(cores = 8)
betaQN_BMIQ <- plyr::aaply(
betaQN, 2,
function(x){
norm_ls <- BMIQ(x, design.v = type12, plots = FALSE)
return (norm_ls$nbeta)
},.progress = "time",.parallel = TRUE
) %>% t()
saveRDS(betaQN_BMIQ, paste0(data.dir.probes.normalization, "London_BLOOD_QNBMIQ_withStatusExclude.RDS"))3 Outliers detection - PCA analysis
Description:
- estimate standard deviation for each probe
- select most variable probes (e.g. n = 50,000)
- pca plot
- Filter outliers
Input:
- QNBMIQ.rds
- pheno_df.RDS
Output:
- PCs_usingBetas.csv
- PCA plots
- QNBMIQ_PCfiltered.RDS
- pheno_df.RDS
# plotPCA and OrderDataBySd functions
devtools::source_gist("https://gist.github.com/tiagochst/d3a7b1639acf603916c315d23b1efb3e")## Sourcing https://gist.githubusercontent.com/tiagochst/d3a7b1639acf603916c315d23b1efb3e/raw/a14424662da343c1301b7b2f03210d28d16ae05c/functions.R## SHA-1 hash of file is ef6f39dc4e5eddb5ca1c6e5af321e75ff06e9362beta_mat <- readRDS(paste0(data.dir.probes.normalization, "London_BLOOD_QNBMIQ_withStatusExclude.RDS")) #dim: 437713 110
pheno_df <- readRDS(paste0(data.dir.clinical.filter, "pheno_BLOOD_withStatusExclude_df.RDS")) #dim: 110 7
identical(colnames(beta_mat), pheno_df$sample)## [1] TRUE### transform to m values
mvalue_mat <- log2(beta_mat/(1 - beta_mat)) #dim: 437713 110
pheno_df <- subset(pheno_df, pheno_df$sample %in% colnames(beta_mat)) #dim: 110 7
##### 1.Order matrix by most variable probes on top ############################
betaOrd_mat <- OrderDataBySd(beta_mat) #dim: 437713 110
mOrd_mat <- OrderDataBySd(mvalue_mat) #dim: 437713 110
betaOrd_matPlot <- betaOrd_mat[, pheno_df$sample] #dim: 437713 110
mOrd_matPlot <- mOrd_mat[, pheno_df$sample] #dim: 437713 110
identical(pheno_df$sample, colnames(betaOrd_matPlot))## [1] TRUEidentical(pheno_df$sample, colnames(mOrd_matPlot))## [1] TRUEexpSorted_mat = betaOrd_mat #dim: 437713 110
pca <- prcomp(
t(expSorted_mat[1:50000,]),
center = TRUE,
scale = TRUE
)
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2])
meanPC1 <- mean (d$PC1)
sdPC1 <- sd (d$PC1)
meanPC2 <- mean (d$PC2)
sdPC2 <- sd (d$PC2)
out3sdPC1_1 <- meanPC1 - 3*sdPC1
out3sdPC1_2 <- meanPC1 + 3*sdPC1
out3sdPC2_1 <- meanPC2 - 3*sdPC2
out3sdPC2_2 <- meanPC2 + 3*sdPC2
d$outlier_PC1[d$PC1 >= out3sdPC1_1 & d$PC1 <= out3sdPC1_2] <- 0
d$outlier_PC1[d$PC1 < out3sdPC1_1 | d$PC1 > out3sdPC1_2] <- 1
d$outlier_PC2[d$PC2 >= out3sdPC2_1 & d$PC2 <= out3sdPC2_2] <- 0
d$outlier_PC2[d$PC2 < out3sdPC2_1 | d$PC2 > out3sdPC2_2] <- 1
write.csv(d, paste0(data.dir.pca, "London_Blood_PCs_usingBetas_withStatusExclude.csv"))
##### 2.PCA plot ###############################################################
library(ggplot2)
library(ggrepel)
### beta values
byStatus <- plotPCA(
dataset = "London Blood: beta values",
expSorted_mat = betaOrd_mat,
pheno = pheno_df,
group_char = "status",
ntop = 50000,
center = TRUE,
scale = TRUE
)
bySex <- plotPCA(
dataset = "London Blood: beta values",
expSorted_mat = betaOrd_mat,
pheno = pheno_df,
group_char = "sex",
ntop = 50000,
center = TRUE,
scale = TRUE
)
### M values
byStatus <- plotPCA(
dataset = "London Blood: M values",
expSorted_mat = mOrd_mat,
pheno = pheno_df,
group_char = "status",
ntop = 50000,
center = TRUE,
scale = TRUE
)
bySex <- plotPCA(
dataset = "London Blood: M values",
expSorted_mat = mOrd_mat,
pheno = pheno_df,
group_char = "sex",
ntop = 50000,
center = TRUE,
scale = TRUE
)
3.1 Filter samples by PCA
noOutliers <- d[which(d$outlier_PC1 == 0 & d$outlier_PC2 == 0), ]
betaQN_BMIQ_PCfiltered <- beta_mat[, rownames(noOutliers)]
saveRDS(betaQN_BMIQ_PCfiltered, paste0(data.dir.pca, "London_QNBMIQ_PCfiltered_withStatusExclude.RDS"))
pheno_df <- pheno_df[pheno_df$sample %in% rownames(noOutliers),]
saveRDS(pheno_df, paste0(data.dir.pca, "pheno_withStatusExclude_df.RDS"))4 Summary after QC steps
4.1 Data and metadata
betaQN_BMIQ_PCfiltered <- readRDS(paste0(data.dir.pca, "London_QNBMIQ_PCfiltered_withStatusExclude.RDS"))
nb.samples.with.clinical.after.pca <- ncol(betaQN_BMIQ_PCfiltered)
pheno_df <- readRDS(paste0(data.dir.pca, "pheno_withStatusExclude_df.RDS"))
dim(betaQN_BMIQ_PCfiltered)## [1] 450793 77dim(pheno_df)## [1] 77 8pheno_df %>%
DT::datatable(filter = 'top',
style = "bootstrap",
extensions = 'Buttons',
options = list(scrollX = TRUE,
dom = 'Bfrtip',
buttons = I('colvis'),
keys = TRUE,
pageLength = 10),
rownames = FALSE,
caption = "Samples metadata")4.2 Numbers of samples and probes removed in each step
df.samples <- data.frame(
"Number of samples" = c(nb.samples,
nb.samples.with.clinical,
nb.samples.with.clinical.after.pca),
"Description" = c("total number of samples",
"samples with clinical data",
"Samples after PCA"),
"Difference" = c("-",
nb.samples.with.clinical - nb.samples ,
nb.samples.with.clinical.after.pca - nb.samples.with.clinical)
)
df.samples # Create summary table
df.probes <- data.frame(
"Number of probes" = c(nb.probes,
nb.probes.cg,
nb.probes.cg.dmrcate),
"Description" = c("total number of probes in raw data",
"only probes that start with cg",
"DMRcate"),
"Difference" = c("-",
nb.probes.cg - nb.probes ,
nb.probes.cg.dmrcate - nb.probes.cg)
)
df.probessave(df.samples,df.probes,file = file.path(data.dir.table, "LONDON_blood_table.rda"))5 Compute cell type proportions
Data from https://www.tandfonline.com/doi/full/10.4161/epi.23924
- Input: London_QNBMIQ_PCfiltered.RDS, pheno_blood_df.RDS
- Output: pheno_BLOOD_withBloodProp_df.rds
blood <- readRDS(paste0(data.dir.pca, "London_QNBMIQ_PCfiltered_withStatusExclude.RDS"))
nb.samples.with.clinical.after.pca <- ncol(blood)
pheno <- readRDS(paste0(data.dir.pca, "pheno_withStatusExclude_df.RDS"))
library(EpiDISH)
data(centDHSbloodDMC.m)
out.l <- epidish(blood, centDHSbloodDMC.m, method = 'RPC')
frac.m <- data.frame(out.l$estF)
pheno_final <- merge(
pheno,
frac.m,
by.x = "sample",
by.y = "row.names",
sort = FALSE
)
identical(pheno_final$sample, colnames(blood))## [1] TRUEsaveRDS(
pheno_final,
paste0(data.dir.neuron, "pheno_BLOOD_withBloodProp_withStatusExclude_df.rds")
)6 Session information
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R Under development (unstable) (2020-02-25 r77857)
## os macOS Catalina 10.15.3
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2020-04-26
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## acepack 1.4.1 2016-10-29 [1]
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## progress 1.2.2 2019-05-16 [1]
## promises 1.1.0 2019-10-04 [1]
## ProtGenerics 1.19.3 2019-12-25 [1]
## ps 1.3.2 2020-02-13 [1]
## purrr 0.3.4 2020-04-17 [1]
## quadprog 1.5-8 2019-11-20 [1]
## R.methodsS3 1.8.0 2020-02-14 [1]
## R.oo 1.23.0 2019-11-03 [1]
## R.utils 2.9.2 2019-12-08 [1]
## R6 2.4.1 2019-11-12 [1]
## randomForest 4.6-14 2018-03-25 [1]
## rappdirs 0.3.1 2016-03-28 [1]
## RColorBrewer 1.1-2 2014-12-07 [1]
## Rcpp 1.0.4.6 2020-04-09 [1]
## RCurl 1.98-1.2 2020-04-18 [1]
## readr 1.3.1 2018-12-21 [1]
## readxl 1.3.1 2019-03-13 [1]
## remotes 2.1.1 2020-02-15 [1]
## reshape 0.8.8 2018-10-23 [1]
## reshape2 * 1.4.4 2020-04-09 [1]
## rhdf5 2.31.10 2020-04-02 [1]
## Rhdf5lib 1.9.3 2020-04-15 [1]
## rlang 0.4.5.9000 2020-03-20 [1]
## rmarkdown 2.1 2020-01-20 [1]
## rngtools 1.5 2020-01-23 [1]
## ROC * 1.63.0 2019-11-06 [1]
## rpart 4.1-15 2019-04-12 [1]
## RPMM * 1.25 2017-02-28 [1]
## rprojroot 1.3-2 2018-01-03 [1]
## Rsamtools 2.3.7 2020-03-18 [1]
## RSQLite 2.2.0 2020-01-07 [1]
## rstudioapi 0.11 2020-02-07 [1]
## rtracklayer 1.47.0 2019-11-06 [1]
## S4Vectors * 0.25.15 2020-04-04 [1]
## scales * 1.1.0 2019-11-18 [1]
## scrime 1.3.5 2018-12-01 [1]
## sesame * 1.5.3 2020-03-03 [1]
## sesameData * 1.5.0 2019-10-31 [1]
## sessioninfo 1.1.1 2018-11-05 [1]
## shiny 1.4.0.2 2020-03-13 [1]
## siggenes 1.61.0 2019-11-06 [1]
## statmod 1.4.34 2020-02-17 [1]
## stringi 1.4.6 2020-02-17 [1]
## stringr 1.4.0 2019-02-10 [1]
## SummarizedExperiment * 1.17.5 2020-03-27 [1]
## survival 3.1-12 2020-04-10 [1]
## testthat 2.3.2 2020-03-02 [1]
## tibble 3.0.1 2020-04-20 [1]
## tidyr 1.0.2 2020-01-24 [1]
## tidyselect 1.0.0 2020-01-27 [1]
## TxDb.Hsapiens.UCSC.hg19.knownGene * 3.2.2 2020-04-02 [1]
## usethis 1.6.0 2020-04-09 [1]
## VariantAnnotation 1.33.4 2020-04-09 [1]
## vctrs 0.2.99.9010 2020-04-02 [1]
## wateRmelon * 1.31.0 2019-11-06 [1]
## wheatmap 0.1.0 2018-03-15 [1]
## withr 2.1.2 2018-03-15 [1]
## xfun 0.13 2020-04-13 [1]
## XML 3.99-0.3 2020-01-20 [1]
## xml2 1.3.1 2020-04-09 [1]
## xtable 1.8-4 2019-04-21 [1]
## XVector * 0.27.2 2020-03-24 [1]
## yaml 2.2.1 2020-02-01 [1]
## zlibbioc 1.33.1 2020-01-24 [1]
## source
## CRAN (R 4.0.0)
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
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## Github (tidyverse/dplyr@affb977)
## Bioconductor
## CRAN (R 4.0.0)
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## Github (r-lib/vctrs@fd24927)
## Bioconductor
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##
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library