In dleelab/iasvaExamples: iasva example data package

Here, we becnhmark several factor analyses methods on single cell RNA sequencing (scRNA-Seq) data obtained from human human brain samples (Darmanis et. al., 2015). All of these methods are used to uncover variation associated with different cell types and compared against each other. This dataset is included in an R data package ("iasvaExamples") containing data examples for IA-SVA (https://github.com/dleelab/iasvaExamples). To install the 'iasvaExamples' package, follow the instructions provided in the GitHub page.

Load packages

rm(list=ls())
library(iasva)
library(iasvaExamples)
library(sva)
library(RUVSeq)
library(Rtsne)
library(pheatmap)
library(corrplot)
library(DescTools) #pcc i.e., Pearson's contingency coefficient
library(RColorBrewer)
library(zinbwave)
library(nnet)
library(SummarizedExperiment)
color.vec <- brewer.pal(8, "Set1")
set.seed(3532379)
# To make the task more challenging we selected 
  #1000 genes to be used in downstream analyses. 
num.genes <- 1000

Load the brain single cell RNA-Seq data

data("Darmanis_Brain_scRNAseq_Read_Counts")
data("Darmanis_Brain_scRNAseq_Annotations")
ls()
counts.total <- Darmanis_Brain_scRNAseq_Read_Counts
anns.total <- Darmanis_Brain_scRNAseq_Annotations
dim(anns.total)
dim(counts.total)

summary(anns.total)
table(anns.total$tissue, anns.total$cell_type)
ContCoef(table(anns.total$tissue, anns.total$cell_type))
table(anns.total$age, anns.total$cell_type)
ContCoef(table(anns.total$age, anns.total$cell_type))
table(anns.total$sample_name, anns.total$cell_type)
ContCoef(table(anns.total$sample_name, anns.total$cell_type))
table(anns.total$c1_chip_id, anns.total$cell_type)
ContCoef(table(anns.total$c1_chip_id, anns.total$cell_type))

The annotations describing samples and experimental settings are stored in "anns" and the read counts information is stored in "counts".

For the downstream analyses, select neurons, oligodendrcytes and astrocytes from 2 cell types and 8 individuals.

# Select neurons and astrocytes for downstream analyses
selected.cells <- intersect(which(anns.total$tissue=="cortex"),
                            which(anns.total$cell_type=="neurons"|anns.total$cell_type=="astrocytes"))
counts <- counts.total[, selected.cells]
anns <- droplevels(anns.total[selected.cells,])
table(anns$tissue, anns$cell_type)
table(anns$tissue, anns$age)
table(anns$age, anns$sample_name)
table(anns$age, anns$c1_chip_id)
table(anns$tissue, anns$c1_chip_id)
table(anns$cell_type, anns$sample_name)
table(anns$cell_type, anns$tissue)
dim(counts)
dim(anns)
dropout.rate <- sum(counts==0)/(dim(counts)[1]*dim(counts)[2])*100
dropout.rate
## Filter out lowly-expressed genes 
filter = apply(counts, 1, function(x) length(x[x>5])>=3)
counts = as.matrix(counts[filter,])
dim(counts)
dropout.rate <- sum(counts==0)/(dim(counts)[1]*dim(counts)[2])*100
dropout.rate

# To make this task more challenging for benchamarking purposes
# we only used randomly selected 1000 genes in downstream analyses
rand.gene.list <- sample(1:nrow(counts), num.genes)
counts <- counts[rand.gene.list,]

Cell_Type <- anns$cell_type
Tissue <- anns$tissue
Cell_Type_Num <- as.integer(Cell_Type)
Samples <- as.integer(anns$sample_name)

Calculate geometric library size, i.e., library size of log-transfromed read counts

It is well known that the geometric library size (i.e., library size of log-transfromed read counts) or proportion of expressed genes in each cell explains very large portion of variability of scRNA-Seq data (Hicks et. al. 2015 BioRxiv, McDavid et. al. 2016 Nature Biotechnology). Frequently, the first principal component of log-transformed scRNA-Seq read counts is highly correlated with the geometric library size. (r > 0.9). Here, we calculate it and show the high correlation. Later this vector will be used as an known factor in IA-SVA algorithm.

Geo_Lib_Size <- colSums(log(counts+1))
barplot(Geo_Lib_Size, xlab="Cell")
lcounts <- log(counts + 1)
pca.res = svd(lcounts - rowMeans(lcounts))$v
cor(Geo_Lib_Size, pca.res[,1])
cor(Geo_Lib_Size, Cell_Type_Num)

run analyses

Library size, tissue, and sample ID are used as covariates in all factor analyses except for PCA. 3 SVs are captured with each method and the SV that has the maximum correlation with the cell type assignments is selected among these 3. This is especially important for a fair comparison of PCA.

mod1 = model.matrix(~Geo_Lib_Size+anns$sample_name)
mod0 = cbind(mod1[,1])
summ_exp <- SummarizedExperiment(assays = counts)
### IA-SVA 
start_time <- Sys.time()
iasva.res<- iasva(summ_exp, mod1[,-1],verbose=FALSE, permute=FALSE, num.sv=3)
end_time <- Sys.time()
iasva.time <- start_time - end_time
iasva.sv <- iasva.res$sv
plot(iasva.sv[,1], iasva.sv[,2],col=Cell_Type, xlab="SV1", ylab="SV2")
pairs(iasva.sv, col=Cell_Type, main="IA-SVA")
max.cor.iasva <- which.is.max(c(abs(cor(iasva.sv[,1], Cell_Type_Num)),
                abs(cor(iasva.sv[,2], Cell_Type_Num)),
                abs(cor(iasva.sv[,3], Cell_Type_Num))))
max.cor.iasva                

## PCA
ldat0 = log(counts + 1)
start_time <- Sys.time()
pca.sv = svd(ldat0 - rowMeans(ldat0))$v
end_time <- Sys.time()
pca.time <- start_time - end_time
plot(pca.sv[,1], pca.sv[,2],col=Cell_Type, xlab="SV1", ylab="SV2")
pairs(pca.sv[,1:3], col=Cell_Type, main="PCA")
max.cor.pca <- which.is.max(c(abs(cor(pca.sv[,1], Cell_Type_Num)),
                abs(cor(pca.sv[,2], Cell_Type_Num)),
                abs(cor(pca.sv[,3], Cell_Type_Num))))
max.cor.pca

## USVA
start_time <- Sys.time()
usva.sv = svaseq(counts,mod1,mod0,n.sv=3)$sv
end_time <- Sys.time()
usva.time <- start_time - end_time
plot(usva.sv[,1], usva.sv[,2],col=Cell_Type, xlab="SV1", ylab="SV2")
pairs(usva.sv[,1:3], col=Cell_Type, main="USVA")
max.cor.usva <- which.is.max(c (abs(cor(usva.sv[,1], Cell_Type_Num)),
                abs(cor(usva.sv[,2], Cell_Type_Num)),
                abs(cor(usva.sv[,3], Cell_Type_Num))))
max.cor.usva

### RUVres
design <- model.matrix(~Geo_Lib_Size+anns$sample_name)
start_time <- Sys.time()
y <- DGEList(counts=counts)
y <- calcNormFactors(y, method="TMM")
y <- estimateGLMCommonDisp(y, design)
y <- estimateGLMTagwiseDisp(y, design)
fit <- glmFit(y, design)
res <- residuals(fit, type="deviance")
set <- betweenLaneNormalization(counts, which="upper")
genes = rep(TRUE,dim(counts)[1])
ruvres.sv = RUVr(set,genes,k=3,res,isLog = T)$W
end_time <- Sys.time()
ruvres.time <- start_time - end_time
plot(ruvres.sv[,1], ruvres.sv[,2],col=Cell_Type, xlab="SV1", ylab="SV2")
pairs(ruvres.sv[,1:3], col=Cell_Type)
max.cor.ruvres <- which.is.max (c (abs(cor(ruvres.sv[,1], Cell_Type_Num)),
                abs(cor(ruvres.sv[,2], Cell_Type_Num)),
                abs(cor(ruvres.sv[,3], Cell_Type_Num))))
max.cor.ruvres

### RUVemp
start_time <- Sys.time()
y <- DGEList(counts=counts)
y <- calcNormFactors(y, method="TMM")
y <- estimateGLMCommonDisp(y, design)
y <- estimateGLMTagwiseDisp(y, design)
fit <- glmFit(y, design)
lrt <- glmLRT(fit, coef=2)
emp.genes =rank(lrt$table$LR) > 400
ruvemp.sv <- RUVg(counts, emp.genes, k=3)$W
end_time <- Sys.time()
ruvemp.time <- start_time - end_time
plot(ruvemp.sv[,1], ruvemp.sv[,2],col=Cell_Type, xlab="SV1", ylab="SV2")
pairs(ruvemp.sv[,1:3], col=Cell_Type)
max.cor.ruvemp <- which.is.max(c (abs(cor(ruvemp.sv[,1], Cell_Type_Num)),
                abs(cor(ruvemp.sv[,2], Cell_Type_Num)),
                abs(cor(ruvemp.sv[,3], Cell_Type_Num))))
max.cor.ruvemp

### ZINB WAVE
se.data <- SummarizedExperiment(assays=list(counts=counts))
se.data$LS <- Geo_Lib_Size
se.data$SN <- anns$sample_name
se.data$Tissue <- anns$tissue
# use Geometric library size + Tissue + sample is as covariates
# suggested epsilon parameter is used for the analyses
start_time <- Sys.time()
zinb_cov <- zinbFit(se.data, K=3, X="~LS+SN", epsilon=1000)
W <- getW(zinb_cov)
end_time <- Sys.time()
zinb.time <- start_time - end_time
colnames(W) <- paste0("W", 1:3)
pairs(W,col=Cell_Type)
max.cor.zinb <- which.is.max (c (abs(cor(W[,1], Cell_Type_Num)),
                abs(cor(W[,2], Cell_Type_Num)),
                abs(cor(W[,3], Cell_Type_Num))))

max.cor.zinb

Compare results

Compare results from different methods to the true cell type assignments using correlation plots.

## Plot correlation between true cell_type and estimates (SV1)
celltype.est = cbind(Cell_Type_Num, iasva.sv[,max.cor.iasva],
                     pca.sv[,max.cor.pca],usva.sv[,max.cor.usva], 
                     ruvres.sv[,max.cor.ruvres], 
                     ruvemp.sv[,max.cor.ruvemp], W[,max.cor.zinb])

colnames(celltype.est) = c("Cell Type","IA-SVA","PCA","USVA",
                           "RUVres","RUVemp", "ZINB-WAVE")
corr = abs(cor(celltype.est))
print(corr)
cols = colorRampPalette(c(color.vec[2],"white",color.vec[1]))

corrplot.mixed(corr, lower="ellipse", upper="number", mar=c(0,0,1,0))
num.cells <- dim(anns)[1]
pdf(paste0("output/Brain_scRNASeq_neuron_astro_",num.cells,"_cells.pdf"), height=6, width=6)
corrplot.mixed(corr, lower="ellipse", upper="number", mar=c(0,0,1,0))
dev.off()

time.seconds <- c(iasva.time,pca.time,usva.time,ruvres.time,ruvemp.time,zinb.time)
names(time.seconds) <- c("IA-SVA","PCA","USVA","RUVres","RUVemp", "ZINB-WAVE")
time.seconds
barplot(t(as.matrix(abs(time.seconds))), las =2, ylab = "Run time (seconds)")

pdf(paste0("output/Brain_scRNASeq_neuron_astro_",num.cells,"_cells_run_time.pdf"), height=6, width=6)
barplot(t(as.matrix(abs(time.seconds))), las =2, ylab = "Run time (seconds)")
dev.off()