Setup

Set working directory

knitr::opts_knit$set(root.dir = "/research/labs/neurology/fryer/m214960/aducanumab/scripts/R")

Load libraries

# load libraries
library(cowplot)    # plot_grid()
library(dplyr)      # left_join()
library(ggplot2)    # ggplot()
library(gridExtra)  # grid.arrange()
library(harmony)    # RunHarmony()
library(parallel)   # detectCores()
library(plotly)     # plot_ly()
library(Seurat)     # Read10X_h5()
library(ShinyCell)  # makeShinyApp()
library(stringr)    # str_match()

Set variables and thresholds

# variables
sample_colors <- "forestgreen"

# thresholds
nCount.min <- 200
nCount.max <- 10000
nFeature.min <- 100
complexity.cutoff <- 0.8
mt.cutoff <- 20
ribo.cutoff <- 20
hb.cutoff <- 3

# set seed
set.seed(8)

# work in parallel
options(mc.cores = detectCores() - 1)

Load data

# Load counts
prefix <- "../../cellbender/"
suffix <- "_fpr_0.05_filtered.h5"
mouse <- CreateSeuratObject(Read10X_h5(paste0(prefix,"736B_Adu_Female",suffix)))

# Load annotation file
if (file.exists("../../rObjects/annotation.rds")) {
  genes <- readRDS("../../rObjects/annotation.rds")
} else {
  gtf.file <- "../../refs/mouse_genes.gtf"
  genes <- rtracklayer::import(gtf.file)
  genes <- as.data.frame(genes)
  genes <- genes[genes$type == "gene",]
  saveRDS(genes, "../../rObjects/annotation.rds")
}

Metadata columns

# cell.complexity
mouse$cell.complexity <- log10(mouse$nFeature_RNA) / log10(mouse$nCount_RNA)

# percent.mt
mt.genes <- genes[genes$seqnames == "chrM",13]
mouse$percent.mt <- PercentageFeatureSet(mouse, features = mt.genes)
mt.genes

##  [1] "mt-Nd1"  "mt-Nd2"  "mt-Co1"  "mt-Co2"  "mt-Atp8" "mt-Atp6" "mt-Co3" 
##  [8] "mt-Nd3"  "mt-Nd4l" "mt-Nd4"  "mt-Nd5"  "mt-Nd6"  "mt-Cytb"

# percent.ribo
# ribosomal proteins begin with 'Rps' or 'Rpl' in this annotation file
# mitochondrial ribosomes start with 'Mrps' or 'Mrpl'
gene.names <- genes$gene_name
ribo <- gene.names[grep("^Rp[sl]", gene.names)]
mt.ribo <- gene.names[grep("^Mrp[sl]", gene.names)]
ribo.combined <- c(mt.ribo,ribo)
mouse$percent.ribo.protein <- PercentageFeatureSet(mouse, features = ribo.combined)
ribo.combined

##   [1] "Mrpl15"     "Mrpl30"     "Mrps9"      "Mrpl44"     "Mrps14"    
##   [6] "Mrpl41"     "Mrps2"      "Mrps5"      "Mrps26"     "Mrps28"    
##  [11] "Mrpl47"     "Mrpl24"     "Mrpl9"      "Mrps21"     "Mrpl50"    
##  [16] "Mrpl37"     "Mrps15"     "Mrpl20"     "Mrpl33"     "Mrpl1"     
##  [21] "Mrps18c"    "Mrps17"     "Mrps33"     "Mrpl35"     "Mrpl19"    
##  [26] "Mrpl53"     "Mrps25"     "Mrpl51"     "Mrps35"     "Mrps12"    
##  [31] "Mrpl46"     "Mrps11"     "Mrpl48"     "Mrpl17"     "Mrpl23"    
##  [36] "Mrps31"     "Mrpl34"     "Mrpl4"      "Mrps22"     "Mrpl3"     
##  [41] "Mrpl54"     "Mrpl42"     "Mrps24"     "Mrpl22"     "Mrpl55"    
##  [46] "Mrps23"     "Mrpl27"     "Mrpl10"     "Mrpl45"     "Mrpl58"    
##  [51] "Mrps7"      "Mrpl38"     "Mrpl12"     "Mrpl32"     "Mrpl36"    
##  [56] "Mrps27"     "Mrps36"     "Mrps30"     "Mrps16"     "Mrpl52"    
##  [61] "Mrpl57"     "Mrpl13"     "Mrpl40"     "Mrpl39"     "Mrps6"     
##  [66] "Mrpl18"     "Mrps34"     "Mrpl28"     "Mrps18b"    "Mrpl14"    
##  [71] "Mrps18a"    "Mrpl2"      "Mrps10"     "Mrpl21"     "Mrpl11"    
##  [76] "Mrpl49"     "Mrpl16"     "Mrpl43"     "Rpl7"       "Rpl31"     
##  [81] "Rpl37a"     "Rps6kc1"    "Rpl7a"      "Rpl12"      "Rpl35"     
##  [86] "Rps21"      "Rpl22l1"    "Rps3a1"     "Rps27"      "Rpl34"     
##  [91] "Rps20"      "Rps6"       "Rps8"       "Rps6ka1"    "Rpl11"     
##  [96] "Rpl22"      "Rpl9"       "Rpl5"       "Rplp0"      "Rpl6"      
## [101] "Rpl21"      "Rpl32"      "Rps9"       "Rpl28"      "Rps5"      
## [106] "Rps19"      "Rps16"      "Rps11"      "Rpl13a"     "Rpl18"     
## [111] "Rps17"      "Rps3"       "Rpl27a"     "Rps13"      "Rps15a"    
## [116] "Rplp2"      "Rpl18a"     "Rpl13"      "Rps25"      "Rpl10-ps3" 
## [121] "Rplp1"      "Rpl4"       "Rps27l"     "Rpl29"      "Rps27rt"   
## [126] "Rpsa"       "Rpl14"      "Rps12"      "Rps15"      "Rpl41"     
## [131] "Rps26"      "Rps27a"     "Rpl26"      "Rpl23a"     "Rpl9-ps1"  
## [136] "Rps6kb1"    "Rpl23"      "Rpl19"      "Rpl27"      "Rpl38"     
## [141] "Rps7"       "Rpl10l"     "Rps29"      "Rpl36al"    "Rps6kl1"   
## [146] "Rps6ka5"    "Rps23"      "Rpl15"      "Rps24"      "Rpl36a-ps1"
## [151] "Rpl37"      "Rpl30"      "Rpl8"       "Rpl3"       "Rps19bp1"  
## [156] "Rpl39l"     "Rpl35a"     "Rpl24"      "Rps6ka2"    "Rps2"      
## [161] "Rpl3l"      "Rps10"      "Rpl10a"     "Rps28"      "Rps18"     
## [166] "Rpl7l1"     "Rpl36"      "Rpl36-ps4"  "Rps14"      "Rpl17"     
## [171] "Rps6kb2"    "Rps6ka4"    "Rpl9-ps6"   "Rpl39"      "Rpl10"     
## [176] "Rps4x"      "Rps6ka6"    "Rpl36a"     "Rps6ka3"

# percent.hb
# percent.hb - hemoglobin proteins begin with 'Hbb' or 'Hba' for mouse
hb.genes <- gene.names[grep("^Hb[ba]-", gene.names)]
mouse$percent.hb <- PercentageFeatureSet(mouse, features = hb.genes)
hb.genes

## [1] "Hbb-bt"  "Hbb-bs"  "Hbb-bh2" "Hbb-bh1" "Hbb-y"   "Hba-x"   "Hba-a1" 
## [8] "Hba-a2"

# sample
mouse$sample <- "Adu.F.736B "

Pre-filtering QC

Number of cells

# Visualize the number of cell counts per sample
data <- as.data.frame(table(mouse$sample))
colnames(data) <- c("sample","frequency")

ncells1 <- ggplot(data, aes(x = sample, y = frequency, fill = sample)) + 
  geom_col() +
  theme_classic() +
  geom_text(aes(label = frequency), 
            position=position_dodge(width=0.9), 
            vjust=-0.25) +
  scale_fill_manual(values = sample_colors) + 
  ggtitle("Raw: cells per sample") +
  theme(legend.position =  "none") + 
  scale_y_continuous(breaks = seq(0,10000, by = 2000), limits = c(0,10000)) +
  theme(axis.text.x = element_text(angle = 45, hjust=1))
ncells1

Density plots

# Visualize nCount_RNA
den1 <- ggplot(mouse@meta.data,
       aes(color = sample,
           x = nCount_RNA,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_x_log10() +
  scale_color_manual(values = sample_colors) +
  scale_fill_manual(values = sample_colors) +
  xlab("nCount_RNA") +
  ylab("Density") +
  theme(legend.position =  "none") +
  geom_vline(xintercept = nCount.min) +
  geom_vline(xintercept = nCount.max) +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Visualize nFeature_RNA
den2 <- ggplot(mouse@meta.data,
       aes(color = sample,
           x = nFeature_RNA,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_x_log10() +
  scale_color_manual(values = sample_colors) +
  theme(legend.position =  "none") +
  scale_fill_manual(values = sample_colors) +
  xlab("nFeature_RNA") +
  ylab("Density") +
  geom_vline(xintercept = nFeature.min) +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Visualize cell complexity
# Quality cells are usually above 0.85
den3 <- ggplot(mouse@meta.data,
       aes(color = sample,
           x = cell.complexity,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_color_manual(values = sample_colors) +
  theme(legend.position =  "none") +
  scale_fill_manual(values = sample_colors) +
  xlab("Cell Complexity (log10(nFeature/nCount))") +
  ylab("Density") +
  geom_vline(xintercept = complexity.cutoff) +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Visualize percent.mt
den4 <- ggplot(mouse@meta.data,
       aes(color = sample,
           x = percent.mt,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_x_continuous(n.breaks = 4) +
  geom_vline(xintercept = mt.cutoff) +
  scale_color_manual(values = sample_colors) +
  theme(legend.position =  "none") +
  scale_fill_manual(values = sample_colors) +
  xlab("% Mitochondrial Genes") +
  ylab("Density") +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Visualize percent.ribo.protein
den5 <- ggplot(mouse@meta.data,
       aes(color = sample,
           x = percent.ribo.protein,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_x_log10() +
  geom_vline(xintercept = ribo.cutoff) +
  scale_color_manual(values = sample_colors) +
  theme(legend.position =  "none") +
  scale_fill_manual(values = sample_colors) +
  xlab("% Ribosomal Protein Genes") +
  ylab("Density") +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Visualize percent.hb
den6 <- ggplot(mouse@meta.data,
       aes(color = sample,
           x = percent.hb,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_x_log10() +
  geom_vline(xintercept = hb.cutoff) +
  scale_color_manual(values = sample_colors) +
  theme(legend.position =  "none") +
  scale_fill_manual(values = sample_colors) +
  xlab("% Hemoglobin Genes") +
  ylab("Density") +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Arrange graphs in grid
plots <- list(den1,den2,den3,den4,den5,den6)
layout <- rbind(c(1,4),c(2,5),c(3,6))
grid <- grid.arrange(grobs = plots, layout_matrix = layout)

Violin plots

# nFeature, nCount, and cell.complexity violins
v1 <- VlnPlot(mouse,
              features = c("nFeature_RNA", "nCount_RNA","cell.complexity"),
              ncol = 3,
              group.by = 'sample',
              cols = sample_colors,
              pt.size = 0)
v1

#  percent violins
v2 <- VlnPlot(mouse,
              features = c("percent.mt","percent.ribo.protein","percent.hb"),
              ncol = 3,
              group.by = 'sample',
              cols = sample_colors,
              pt.size = 0)
v2

Scatter plots

s1 <- ggplot(
  mouse@meta.data,
  aes(x = nCount_RNA, y = nFeature_RNA, color = percent.mt)) + 
  geom_point() + 
  geom_smooth(method = "lm") +
    scale_x_log10() +       
  scale_y_log10() + 
  theme_classic() +
  geom_vline(xintercept = nCount.min) + 
  geom_hline(yintercept = nFeature.min) + 
  facet_wrap(~sample) +
  scale_colour_gradient(low = "gray90", high = "black", limits =c(0,100))
s1

## `geom_smooth()` using formula 'y ~ x'

s2 <- FeatureScatter(mouse,
                     feature1 = "nCount_RNA",
                     feature2 = "percent.mt",
                     group.by = 'sample',
                     cols = sample_colors,
                     shuffle = TRUE)
s2

Filtering

Cell-level filtering

# filter
mouse.filtered <- subset(mouse,
                        subset = (nCount_RNA > nCount.min) &
                          (nCount_RNA < nCount.max) &
                          (nFeature_RNA > nFeature.min) &
                          (cell.complexity > complexity.cutoff) &
                          (percent.mt < mt.cutoff) &
                          (percent.ribo.protein < ribo.cutoff) &
                          (percent.hb < hb.cutoff))

# print cells removed
print(paste0(dim(mouse)[2] - dim(mouse.filtered)[2]," cells removed"))

## [1] "1268 cells removed"

Gene-level filtering

Remove lowly expressed genes. We will keep genes that have at least 1 count in 10 cells.

# filter genes
counts <- GetAssayData(object = mouse.filtered, slot = "counts")
nonzero <- counts > 0  # produces logical
keep <- Matrix::rowSums(nonzero) >= 10  # sum the true/false
counts.filtered <- counts[keep,]  # keep certain genes

# overwrite mouse.filtered
mouse.filtered <- CreateSeuratObject(counts.filtered, 
                                    meta.data = mouse.filtered@meta.data)

# print features removed
print(paste0(dim(counts)[1] - dim(counts.filtered)[1], " features removed"))

## [1] "23318 features removed"

Remove mitochondrial genes.

# remove mt.genes
counts <- GetAssayData(object = mouse.filtered, slot = "counts")
keep <- !rownames(counts) %in% mt.genes # false when mt.gene
counts.filtered <- counts[keep,]

# overwrite mouse.filtered
mouse.filtered <- CreateSeuratObject(counts.filtered, 
                                    meta.data = mouse.filtered@meta.data)

# print features removed
print(paste0(dim(counts)[1] - dim(counts.filtered)[1], " features removed"))

## [1] "13 features removed"

# cleanup data
remove(mouse,counts,counts.filtered,nonzero)

Post-filtering QC

Number of cells

# Visualize the number of cell counts per sample
data <- as.data.frame(table(mouse.filtered$sample))
colnames(data) <- c("sample","frequency")

ncells2 <- ggplot(data, aes(x = sample, y = frequency, fill = sample)) + 
  geom_col() +
  theme_classic() +
  geom_text(aes(label = frequency), 
            position=position_dodge(width=0.9), 
            vjust=-0.25) +
  scale_fill_manual(values = sample_colors) + 
  scale_y_continuous(breaks = seq(0,10000, by = 2000), limits = c(0,10000)) +
  ggtitle("Filtered: cells per sample") +
  theme(legend.position =  "none") + 
  theme(axis.text.x = element_text(angle = 45, hjust=1))

# Arrange graphs in grid
plots <- list(ncells1,ncells2)
layout <- cbind(c(1),c(2))
grid <- grid.arrange(grobs = plots, layout_matrix = layout)

Density plots

# Visualize nCount_RNA
den1 <- ggplot(mouse.filtered@meta.data,
       aes(color = sample,
           x = nCount_RNA,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_x_log10() +
  scale_color_manual(values = sample_colors) +
  scale_fill_manual(values = sample_colors) +
  xlab("nCount_RNA") +
  ylab("Density") +
  theme(legend.position =  "none") +
  geom_vline(xintercept = nCount.min) +
  geom_vline(xintercept = nCount.max) +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Visualize nFeature_RNA
den2 <- ggplot(mouse.filtered@meta.data,
       aes(color = sample,
           x = nFeature_RNA,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_x_log10() +
  scale_color_manual(values = sample_colors) +
  theme(legend.position =  "none") +
  scale_fill_manual(values = sample_colors) +
  xlab("nFeature_RNA") +
  ylab("Density") +
  geom_vline(xintercept = nFeature.min) +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Visualize cell complexity
# Quality cells are usually above 0.85
den3 <- ggplot(mouse.filtered@meta.data,
       aes(color = sample,
           x = cell.complexity,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_color_manual(values = sample_colors) +
  theme(legend.position =  "none") +
  scale_fill_manual(values = sample_colors) +
  xlab("Cell Complexity (log10(nFeature/nCount))") +
  ylab("Density") +
  geom_vline(xintercept = complexity.cutoff) +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Visualize percent.mt
den4 <- ggplot(mouse.filtered@meta.data,
       aes(color = sample,
           x = percent.mt,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_x_continuous(n.breaks = 4) +
  geom_vline(xintercept = mt.cutoff) +
  scale_color_manual(values = sample_colors) +
  theme(legend.position =  "none") +
  scale_fill_manual(values = sample_colors) +
  xlab("% Mitochondrial Genes") +
  ylab("Density") +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Visualize percent.ribo.protein
den5 <- ggplot(mouse.filtered@meta.data,
       aes(color = sample,
           x = percent.ribo.protein,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_x_log10() +
  geom_vline(xintercept = ribo.cutoff) +
  scale_color_manual(values = sample_colors) +
  theme(legend.position =  "none") +
  scale_fill_manual(values = sample_colors) +
  xlab("% Ribosomal Protein Genes") +
  ylab("Density") +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Visualize percent.hb
den6 <- ggplot(mouse.filtered@meta.data,
       aes(color = sample,
           x = percent.hb,
           fill = sample)) +
  geom_density(alpha = 0.2) +
  theme_classic() +
  scale_x_log10() +
  geom_vline(xintercept = hb.cutoff) +
  scale_color_manual(values = sample_colors) +
  theme(legend.position =  "none") +
  scale_fill_manual(values = sample_colors) +
  xlab("% Hemoglobin Genes") +
  ylab("Density") +
  theme(legend.key.size = unit(0.25, 'cm'), legend.title = element_text(size=9))

# Arrange graphs in grid
plots <- list(den1,den2,den3,den4,den5,den6)
layout <- rbind(c(1,4),c(2,5),c(3,6))
grid <- grid.arrange(grobs = plots, layout_matrix = layout)

Violin plots

# nFeature, nCount, and cell.complexity violins
v3 <- VlnPlot(mouse.filtered,
              features = c("nFeature_RNA", "nCount_RNA","cell.complexity"),
              ncol = 3,
              group.by = 'sample',
              cols = sample_colors,
              pt.size = 0)
v3

#  percent violins
v4 <- VlnPlot(mouse.filtered,
              features = c("percent.mt","percent.ribo.protein","percent.hb"),
              ncol = 3,
              group.by = 'sample',
              cols = sample_colors,
              pt.size = 0)
v4

Scatter plots

s3 <- ggplot(
  mouse.filtered@meta.data,
  aes(x = nCount_RNA, y = nFeature_RNA, color = percent.mt)) + 
  geom_point() + 
  geom_smooth(method = "lm") +
    scale_x_log10() +       
  scale_y_log10() + 
  theme_classic() +
  geom_vline(xintercept = nCount.min) + 
  geom_hline(yintercept = nFeature.min) + 
  facet_wrap(~sample) +
  scale_colour_gradient(low = "gray90", high = "black", limits =c(0,100))
s3

## `geom_smooth()` using formula 'y ~ x'

s4 <- FeatureScatter(mouse.filtered,
                     feature1 = "nCount_RNA",
                     feature2 = "percent.mt",
                     group.by = 'sample',
                     cols = sample_colors,
                     shuffle = TRUE)
s4

Box plot

# Visualize the distribution of genes detected per cell via boxplot
b1 <- ggplot(mouse.filtered@meta.data,
       aes(x = sample, 
           y = log10(nFeature_RNA), 
           fill=sample)) + 
  geom_boxplot() + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  theme(plot.title = element_text(hjust = 0.5, face="bold")) +
  ggtitle("Unique Genes / Cell / Sample") +
  scale_color_manual(values = sample_colors) +
  scale_fill_manual(values = sample_colors) +
  xlab("Sample")
b1

PCA, UMAP, Cluster

SCTransform and PCA

# SCT
mouse.sct <- SCTransform(mouse.filtered, verbose = FALSE)

# Cluster with the SCT workflow
mouse.sct <- RunPCA(object = mouse.sct, assay = "SCT")

## PC_ 1 
## Positive:  Malat1, Snhg11, Gm26917, Syne1, Cdr1os, Chd5, Trank1, Luc7l3, Ankrd11, Pclo 
##     Pnisr, Meg3, Rbm25, Ahi1, Kmt2a, Snhg14, Tnrc6c, Mical2, Map1b, Ryr2 
##     Arpp21, Mical3, Arglu1, Srsf11, Kif1a, Auts2, Ptprd, Map1a, Mir124a-1hg, Erc2 
## Negative:  Tmsb4x, Fth1, Rps29, Rpl41, Ttr, Fau, Rplp1, Rpl37, Rps24, Rpl37a 
##     Actb, Cox8a, Rpl32, Rpl36, Rpl38, Eef1a1, Apoe, Rps28, Rpl13, Rps20 
##     Hbb-bs, Rpl39, Rps10, Ppia, Rps21, Rps26, Rpl23, Rps12, Rpl35, Ftl1 
## PC_ 2 
## Positive:  Malat1, Cd9, Tsix, Ctss, Ctsl, Ctsb, Cd68, H2-K1, B2m, Lgals3bp 
##     Lyz2, Lpl, Gpr84, H2-DMa, Hps3, Clec2d, Selenop, Csf1, Lilrb4a, Cst7 
##     Kcnj2, Erap1, Samhd1, Mgat1, Tlr2, Il4i1, Ggt5, Plau, Tfpi, Apoe 
## Negative:  Ttr, Meg3, Fth1, Cox8a, Rpl41, Hbb-bs, Snhg11, Mt3, Cox6c, Atp5e 
##     Tmsb4x, Rpl38, Cox4i1, Cdr1os, Rpl36, Rpl37, Rps24, Dbi, Atp5md, Enpp2 
##     Ppia, Cox7c, Ubb, Rpl35, Trank1, Tmsb10, Rps26, Fau, Chchd2, Uqcr11 
## PC_ 3 
## Positive:  Tmsb4x, Rplp1, Tyrobp, Rps29, Fau, B2m, Rpsa, Rps24, Rplp2, Rps12 
##     Rps28, Rpl27a, Rpl37a, Rps10, Lyz2, Rpl18a, Rpl39, Cst7, Rpl23, Rpl30 
##     Rps5, Rpl13, Rpl35a, Rpl21, Rps20, Actb, Rps4x, Rpl37, Rps13, Rpl32 
## Negative:  Malat1, Clu, Enpp2, Mt3, Chchd10, 2900040C04Rik, Ptgds, Fxyd1, Ttr, Rbp1 
##     Dbi, Ecrg4, Igfbp2, Trpm3, Kl, Gpx4, Car2, Cox6c, Hist1h2bc, Slc22a17 
##     Clic6, Map1lc3a, Vat1l, Mdh1, Ndufa11, Ckb, Prlr, Pcp4l1, Cox8b, Calml4 
## PC_ 4 
## Positive:  Chd5, 2900040C04Rik, Enpp2, Kl, Rbp1, Cdr1os, Trank1, Ecrg4, Cd9, Pcp4l1 
##     Calml4, Ndufa11, Clic6, Vat1l, Prlr, Tnrc6c, Rbm25, Cox8b, Slc22a17, Ctsb 
##     Ace, B2m, Srsf11, Uqcrb, Snhg11, Luc7l3, Car2, Atp5g1, Cd68, Ryr2 
## Negative:  Malat1, Atp1a2, Slc1a2, Gm3764, Sparcl1, Bcan, Slc4a4, Ncam1, Ptprz1, Scd2 
##     Mir100hg, Rorb, Mt1, Itih3, Ttyh1, Ntm, Slc6a11, Gja1, Ddhd1, Slc6a1 
##     Gria2, Fgfr3, Acsl3, Ndrg2, Dtna, Plpp3, Vegfa, Gjb6, Ntsr2, Appl2 
## PC_ 5 
## Positive:  Malat1, Meg3, Tsix, Mir124a-1hg, Nrxn3, Gm4258, Hbb-bs, Celf5, Tmsb10, Ccnd2 
##     Auts2, Rbfox2, R3hdm2, Ttr, Tuba1a, Trim2, Vcan, Plppr3, Sox4, Hba-a1 
##     Meis2, Tubb5, Eef1a2, Dlx6os1, Nsg2, Hba-a2, Tubb2b, Baiap2, R3hdm1, Tubb3 
## Negative:  Gm26917, Atp1a2, Syne1, Lars2, Sparcl1, Slc1a2, Apoe, Slc4a4, Bcan, Chd5 
##     Vegfa, Dtna, Cst7, Nrxn1, Acsl3, Cit, Fgfr3, Itih3, B2m, Lyz2 
##     Ddhd1, Slc6a11, Tnik, Plcd4, Gja1, Grk3, Mtss2, Slc7a10, Gjb6, Daam2

# Plot PCA
p1 <- DimPlot(object = mouse.sct, 
              reduction = "pca",
              group.by = "sample",
              cols = sample_colors,
              shuffle = TRUE) + NoLegend()
p1

# Cleanup
remove(mouse.filtered)

Significant PCs

First metric

# Determine percent of variation associated with each PC
stdv <- mouse.sct[["pca"]]@stdev
sum.stdv <- sum(mouse.sct[["pca"]]@stdev)
percent.stdv <- (stdv / sum.stdv) * 100

# Calculate cumulative percents for each PC
cumulative <- cumsum(percent.stdv)

# Determine which PC exhibits cumulative percent greater than 90% and
# and % variation associated with the PC as less than 5
co1 <- which(cumulative > 90 & percent.stdv < 5)[1]
co1

## [1] 44

Second metric

# Determine the difference between variation of PC and subsequent PC
co2 <- sort(which(
  (percent.stdv[1:length(percent.stdv) - 1] - 
     percent.stdv[2:length(percent.stdv)]) > 0.1), 
  decreasing = T)[1] + 1

# last point where change of % of variation is more than 0.1%.
co2

## [1] 7

Choose the minimum of these two metrics as the PCs covering the majority of the variation in the data.

# Minimum of the two calculation
min.pc <- min(co1, co2)
min.pc

## [1] 7

UMAP

# Run UMAP
mouse.sct <- RunUMAP(mouse.sct,
                     dims = 1:min.pc,
                     reduction = "pca",
                     n.components = 3) # set to 3 to use with VR

# Plot UMAP
DimPlot(mouse.sct)

Find clusters

# Determine the K-nearest neighbor graph
mouse.unannotated <- FindNeighbors(object = mouse.sct,
                                   assay = "SCT", # set as default after SCTransform
                                   reduction = "pca",
                                   dims = 1:min.pc)

# Determine the clusters for various resolutions
mouse.unannotated <- FindClusters(object = mouse.unannotated,
                                  algorithm = 1, # 1 = Louvain
                                  resolution = seq(0.1,0.5,by=0.1))

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 680
## Number of edges: 18437
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9538
## Number of communities: 3
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 680
## Number of edges: 18437
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9237
## Number of communities: 5
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 680
## Number of edges: 18437
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8988
## Number of communities: 5
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 680
## Number of edges: 18437
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8740
## Number of communities: 5
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 680
## Number of edges: 18437
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8524
## Number of communities: 7
## Elapsed time: 0 seconds

mouse.unannotated$seurat_clusters <- mouse.unannotated$SCT_snn_res.0.5

Explore resolutions

# 0.1
DimPlot(mouse.unannotated,
        group.by = "SCT_snn_res.0.1",
        label = TRUE)

# 0.2
DimPlot(mouse.unannotated,
        group.by = "SCT_snn_res.0.2",
        label = TRUE)

# 0.3
DimPlot(mouse.unannotated,
        group.by = "SCT_snn_res.0.3",
        label = TRUE)

# 0.4
DimPlot(mouse.unannotated,
        group.by = "SCT_snn_res.0.4",
        label = TRUE)

# 0.5
DimPlot(mouse.unannotated,
        group.by = "SCT_snn_res.0.5",
        label = TRUE)

3D UMAP

embeddings <- mouse.unannotated@reductions$umap@cell.embeddings
embeddings <- cbind(embeddings, as.character(mouse.unannotated$seurat_clusters))
colnames(embeddings)[4] <- "seurat_clusters"
embeddings <- as.data.frame(embeddings)
embeddings$seurat_clusters <- factor(embeddings$seurat_clusters,
                                     levels = c("0","1","2","3","4","5","6"))
cluster_colors <- c("gray","red1","orange","yellow","green","cyan",
                    "blue")

three.dim <- plot_ly(embeddings,
                     x = ~UMAP_1, 
                     y = ~UMAP_2, 
                     z = ~UMAP_3, 
                     color = ~seurat_clusters,
                     colors = cluster_colors,
                     size = 1) 
three.dim <- three.dim %>% add_markers() 
three.dim <- three.dim %>% layout(scene = list(xaxis = list(title = 'UMAP_1'), 
                                     yaxis = list(title = 'UMAP_2'), 
                                     zaxis = list(title = 'UMAP_3')))
three.dim

Potential Markers

Astrocytes

DefaultAssay(mouse.unannotated) <- "RNA"
mouse.unannotated <- NormalizeData(mouse.unannotated)
Idents(mouse.unannotated) <- "SCT_snn_res.0.5"
VlnPlot(mouse.unannotated,
        features = "Gfap",
        cols = cluster_colors,
        group.by = "SCT_snn_res.0.5")

VlnPlot(mouse.unannotated,
        features = "Gja1",
        cols = cluster_colors,
        group.by = "SCT_snn_res.0.5")

B-cell

Cd19: expressed in B-cells and follicular dendritic cells
Fcrla: a B-cell specific protein in mice
Cd79a & Cd79b: together form BCR complex
Ms4a1: aka Cd20, expressed beginning at pro-B phase and progressively increases in concentration until maturity
Clusters 11, 20, and 21 are B cell clusters

VlnPlot(mouse.unannotated,
        features = "Cd79b",
        cols = cluster_colors,
        group.by = "SCT_snn_res.0.5")

Endothelial cells

Single-cell profiling of human dura and meningioma reveals cellular meningeal landscape and insights into meningioma immune response

VlnPlot(mouse.unannotated,
        features = "Pecam1",
        cols = cluster_colors,
        group.by = "SCT_snn_res.0.5")

Fibroblasts

Mast cells

Macrophage/Microglia

VlnPlot(mouse.unannotated,
        features = "Tmem119",
        group.by = "SCT_snn_res.0.5")

VlnPlot(mouse.unannotated,
        features = "Itgam", # aka Cd11b
        group.by = "SCT_snn_res.0.5")

VlnPlot(mouse.unannotated,
        features = "Cd14",
        group.by = "SCT_snn_res.0.5")

VlnPlot(mouse.unannotated,
        features = "Cd68",
        group.by = "SCT_snn_res.0.5")

VlnPlot(mouse.unannotated,
        features = "Ccr5",
        group.by = "SCT_snn_res.0.5")

Neurons

Oligodendrocyte precursor cells

VlnPlot(mouse.unannotated,
        features = "Cspg5",
        cols = cluster_colors,
        split.by = "SCT_snn_res.0.5")

## The default behaviour of split.by has changed.
## Separate violin plots are now plotted side-by-side.
## To restore the old behaviour of a single split violin,
## set split.plot = TRUE.
##       
## This message will be shown once per session.

Pericytes & SMCs

T-cell

Trac/Cd3d/Cd3e/Cd3g components of the TCR

Shiny App

Cleanup object

mouse.unannotated@assays$RNA@var.features <- mouse.unannotated@assays$SCT@var.features
metadata <- mouse.unannotated@meta.data
metadata <- metadata[,c(15,16,2:14)]
mouse.unannotated@meta.data <- metadata
mouse.unannotated@assays$SCT@meta.features <- metadata
mouse.unannotated@assays$RNA@meta.features <- metadata

Output directory

# make shiny folder
DefaultAssay(mouse.unannotated) <- "RNA"
Idents(mouse.unannotated) <- mouse.unannotated$seurat_clusters
sc.config <- createConfig(mouse.unannotated)
makeShinyApp(mouse.unannotated, sc.config, gene.mapping = TRUE,
             shiny.title = "Adu_F_736B ")

Aducanumab Mice Study: Adu.F.736B

Kennedi Todd

06/13/2022

Setup

Set working directory

Load libraries

Set variables and thresholds

Load data

Metadata columns

Pre-filtering QC

Number of cells

Density plots

Violin plots

Scatter plots

Filtering

Cell-level filtering

Gene-level filtering

Post-filtering QC

Number of cells

Density plots

Violin plots

Scatter plots

Box plot

PCA, UMAP, Cluster

SCTransform and PCA

Significant PCs

UMAP

Find clusters

Explore resolutions

3D UMAP

Potential Markers

Astrocytes

B-cell

Endothelial cells

Fibroblasts

Mast cells

Macrophage/Microglia

Neurons

Oligodendrocyte precursor cells

Pericytes & SMCs

T-cell

Shiny App

Cleanup object

Output directory