In dleelab/iasvaExamples: iasva example data package

Here, we illustrate how to use the iasva package to detect cell cycle stage difference within single cell RNA sequencing data. We use single cell RNA sequencing (scRNA-Seq) data obtained from human glioblastoma samples (Petel et. al., 2014). This dataset is included in a R data package ("iasvaExamples") containing data examples for IA-SVA (https://github.com/dleelab/iasvaExamples). To install the package, follow the instruction provided in the GitHub page.

Install packages

#devtools
library(devtools)
#iasva
devtools::install_github("UcarLab/iasva")
#iasvaExamples  
devtools::install_github("dleelab/iasvaExamples")

Load packages

rm(list=ls())
library(irlba) # partial SVD, the augmented implicitly restarted Lanczos bidiagonalization algorithm
library(iasva)
library(iasvaExamples)
library(sva)
library(SCnorm)
library(scran)
library(scater)
library(Rtsne)
library(pheatmap)
library(corrplot)
library(DescTools) #pcc i.e., Pearson's contingency coefficient
library(RColorBrewer)
library(SummarizedExperiment)
library(vioplot)
color.vec <- brewer.pal(3, "Set1")

# Normalization.
normalize <- function(counts) 
{
    normfactor <- colSums(counts)
    return(t(t(counts)/normfactor)*median(normfactor))
}

Load the glioblastoma single cell RNA-Seq data

data("Patel_Glioblastoma_scRNAseq_Read_Counts")
data("Patel_Glioblastoma_scRNAseq_Annotations")
data("Patel_Glioblastoma_scRNAseq_ENSG_ID")
ls()
counts <- Patel_Glioblastoma_scRNAseq_Read_Counts
anns <- Patel_Glioblastoma_scRNAseq_Annotations
ENSG.ID <- Patel_Glioblastoma_scRNAseq_ENSG_ID
dim(anns)
dim(counts)
length(ENSG.ID)

summary(anns)
table(anns$patient_id, anns$subtype)
ContCoef(table(anns$patient_id, anns$subtype))

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

Extract glioblastoma cells from Patient MGH30

We use read counts of glioblastoma cells from Patient MGH30 (n = 74).

counts <- counts[, (anns$patient_id=="MGH30")] 
anns <- subset(anns, (patient_id=="MGH30"))
dim(counts)
dim(anns)
anns <- droplevels(anns)
prop.zeros <- sum(counts==0)/length(counts)
prop.zeros
# filter out genes that are sparsely and lowly expressed
filter = apply(counts, 1, function(x) length(x[x>5])>=3)
counts = counts[filter,]
dim(counts)
ENSG.ID <- ENSG.ID[filter]
length(ENSG.ID)
prop.zeros <- sum(counts==0)/length(counts)
prop.zeros
Subtype <- anns$subtype
Patient_ID <- anns$patient_id
mito.genes <- grep(pattern = "^MT-", x = rownames(x = counts), value = TRUE)
Percent_Mito <- colSums(counts[mito.genes, ])/colSums(counts)

## Normalization using SCnorm
## count-depth relationship for all genes
Conditions = rep(c(1), each=74)
countDeptEst <- plotCountDepth(Data = counts, Conditions = Conditions,
                               FilterCellProportion = .1, NCores=3)

DataNorm <- SCnorm(Data = counts, Conditions = Conditions,
                   PrintProgressPlots = FALSE,
                   FilterCellNum = 10,
                   NCores=3)
counts <- results(DataNorm)
summary(colSums(counts))
dim(counts)

Calculate the number of detected genes

It is well known that the number of detected genes in each cell explains a very large portion of variability in 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 number of detected genes (e.g., r > 0.9). Here, we calculate the number of detected genes for glioblastoma cells, which will be used as an known factor in the IA-SVA analyses.

Num_Detected_Genes <- colSums(counts>0)
Geo_Lib <- colSums(log(counts+1))
summary(Geo_Lib)
barplot(Geo_Lib, xlab="Cell", las=2, ylab = "Geometric library size")
lcounts <- log(counts + 1)
# PC1 and Geometric library size correlation
pc1 = irlba(lcounts - rowMeans(lcounts), 1)$v[,1] ## partial SVD
cor(Num_Detected_Genes, pc1)
cor(Geo_Lib, pc1)

Run IA-SVA

Here, we run IA-SVA using Geo_Lib_Size as a known factor and identify five hidden factors. SVs are plotted in a pairwise fashion to uncover which SVs can seperate cells.

set.seed(3445)
mod <- model.matrix(~Geo_Lib)
summ_exp <- SummarizedExperiment(assays = counts)
iasva.res<- iasva(summ_exp, mod[,-1],verbose=FALSE, permute=FALSE, num.sv=5)
iasva.sv <- iasva.res$sv

Cluster <- as.factor(iasva.sv[,1] < 0.1) 
levels(Cluster)=c("Cluster1","Cluster2")
table(Cluster)

pairs(iasva.sv[,1:5], main="IA-SVA", pch=21, col=color.vec[Cluster],
      bg=color.vec[Cluster], oma=c(4,4,6,14))
legend("right", levels(Cluster), fill=color.vec, bty="n")

plot(iasva.sv[,1:2], main="IA-SVA", pch=21, xlab="SV1", ylab="SV2",
     col=color.vec[Cluster], bg=color.vec[Cluster])

cor(Num_Detected_Genes, iasva.sv[,1])
cor(Geo_Lib, iasva.sv[,1])
corrplot(cor(iasva.sv))

Find marker genes for the detected heterogeneity (SV1).

Here, using the find_markers() function we find marker genes that are significantly associated with SV1 (multiple testing adjusted p-value < 0.05, default significance cutoff, and R-squared value > 0.3).

# try different R2 thresholds
pdf(paste0("Clustering_analyses_figure3_sv1.pdf"))
r2.results <- study_R2(summ_exp, iasva.sv,selected.svs=1, no.clusters=2)
dev.off()

marker.counts.SV1 <- find_markers(summ_exp, 
                            as.matrix(iasva.sv[,c(1)]), rsq.cutoff = 0.4)
marker.counts.SV1.long <- find_markers(summ_exp, 
                              as.matrix(iasva.sv[,c(1)]), rsq.cutoff = 0.3)
nrow(marker.counts.SV1) 
nrow(marker.counts.SV1.long)

anno.col2 <- data.frame(Cluster=Cluster, SV1=iasva.sv[,1])
rownames(anno.col2) <- colnames(marker.counts.SV1)
head(anno.col2)

cluster.col <- color.vec[1:2]
names(cluster.col) <- as.vector(levels(Cluster))
anno.colors <- list(Cluster=cluster.col)
anno.colors

pheatmap(log(marker.counts.SV1+1), show_colnames =FALSE, 
         clustering_method = "ward.D2",cutree_cols = 2,annotation_col = anno.col2,
         annotation_colors = anno.colors)

pheatmap(log(marker.counts.SV1.long+1), show_colnames =FALSE, 
         clustering_method = "ward.D2",cutree_cols = 2,annotation_col = anno.col2,
         annotation_colors = anno.colors)

gene.list <- rownames(marker.counts.SV1)
write.table(gene.list, file = paste0("CC_genes.short.txt"),
            col.names =F, row.names = F, quote = F)

gene.list <- rownames(marker.counts.SV1.long)
write.table(gene.list, file = paste0("CC_genes.long.txt"),
            col.names =F, row.names = F, quote = F)

Theses marker genes are strongly enriched in cell-cycle related GO terms and KEGG pathways. (See Supplementary Figure 6 in https://doi.org/10.1101/151217)

Cell type assignment using scran R package

ENSG.counts <- counts
rownames(ENSG.counts) <- ENSG.ID
sce <- SingleCellExperiment(list(counts=ENSG.counts))

# load human cell cycle markers
hs.pairs <- readRDS(system.file("exdata", 
                                "human_cycle_markers.rds", package="scran"))
assigned <- cyclone(sce, pairs=hs.pairs)
head(assigned$scores)
table(assigned$phases)
phase <- rep("S", ncol(sce))
phase[assigned$scores$G1 > 0.5 & assigned$scores$G2M < 0.5] <- "G1"
phase[assigned$scores$G1 < 0.5 & assigned$scores$G2M > 0.5] <- "G2M"
phase[assigned$scores$G1 < 0.5 & assigned$scores$G2M < 0.5] <- "S"
phase[assigned$scores$G1 > 0.5 & assigned$scores$G2M > 0.5] <- "Unknown"
table(phase)

G1 <- iasva.sv[,1][phase=="G1"]
S <- iasva.sv[,1][phase=="S"]
G2M <- iasva.sv[,1][phase=="G2M"]
Unknown <- iasva.sv[,1][phase=="Unknown"]
vioplot(G1, S, G2M, Unknown, names=c("G1", "S", "G2M", "Unknown"), 
   col="gold")
title(xlab="Cell-cycle stage predictions", ylab="IA-SVA factor (SV1)")

Run tSNE to detect the hidden heterogeneity.

For comparison purposes, we applied tSNE on read counts of all genes to identify the hidden heterogeneity. We used the Rtsne R package with default settings.

set.seed(43324)
tsne.res <- Rtsne(t(lcounts), dims = 2, perplexity = 20)
plot(tsne.res$Y, main="tSNE", xlab="Dim1", ylab="Dim2", 
     pch=21, col=color.vec[Cluster], bg=color.vec[Cluster], oma=c(4,4,6,12))
legend("bottomright", levels(Cluster), border="white",
       fill=color.vec, bty="n")

As shown above, tSNE fails to detect the outlier cells that are identified by IA-SVA when all genes are used. Same color coding is used as above.

Run tSNE post IA-SVA analyses, i.e., run tSNE on marker genes associated with SV1 as detected by IA-SVA.

Here, we apply tSNE on the marker genes for SV1 obtained from IA-SVA

set.seed(3452)
tsne.res <- Rtsne(unique(t(log(marker.counts.SV1.long+1))),
                  dims = 2, perplexity = 20)

plot(tsne.res$Y, main="IA-SVA + tSNE", xlab="tSNE Dim1",
     ylab="tSNE Dim2", pch=21, col=color.vec[Cluster],
     bg=color.vec[Cluster], oma=c(4,4,6,12))
legend("topright", levels(Cluster), border="white", fill=color.vec, bty="n")

Run principal component analysis (PCA) to detect the hidden heterogeneity (SV1).

Here, we use PCA to detect the cell cycle stage difference (SV1) detected by IA-SVA.

set.seed(3333)
pca.res = irlba(lcounts - rowMeans(lcounts), 5)$v ## partial SVD

pairs(pca.res[,1:5], main="PCA", pch=21, col=color.vec[Cluster],
      bg=color.vec[Cluster],
      oma=c(4,4,6,14))
legend("right", levels(Cluster), fill=color.vec, bty="n")

plot(pca.res[,1:2], main="PCA", pch=21, xlab="PC1", ylab="PC2",
     col=color.vec[Cluster], bg=color.vec[Cluster])
legend("bottomleft", levels(Cluster), border="white", fill=color.vec, bty="n")

PCA failed to capture the heterogeneity.

Run surrogate variable analysis (SVA) to detect the hidden heterogeneity (SV1).

Here, for comparison purposes we use SVA to detect the hidden heterogeneity in our example data.

mod1 <- model.matrix(~Geo_Lib)
mod0 <- cbind(mod1[,1])
sva.res = svaseq(counts,mod1,mod0, n.sv=5)$sv
pairs(sva.res[,1:5], main="SVA", pch=21, col=color.vec[Cluster],
      bg=color.vec[Cluster], oma=c(4,4,6,14))
legend("right", levels(Cluster), border="white", fill=color.vec, bty="n")
plot(sva.res[,1:2], main="SVA", xlab="SV1", ylab="SV2",
     pch=21, col=color.vec[Cluster], bg=color.vec[Cluster])
legend("topleft", levels(Cluster), border="white", fill=color.vec, bty="n")

SVA failed to detect the cell cycle stage difference.

Correlation between SV1 and the geometric library size

cor(Num_Detected_Genes, iasva.sv[,1])
cor(Geo_Lib, iasva.sv[,1])

By allowing correlation between factors, IA-SVA accurately detects the cell cycle stage difference, which is moderately correlated (|r|=0.44) with the geometric library size (the first principal component). Existing methods fail to detect the heterogeneity due to the orthogonality assumption.

pdf(file=paste0("Patel_Glioblastoma_MGH30_CellCycle_Figure3ABCD.pdf"), width=5, height=8)
layout(matrix(c(1,2,3,4,5,5), nrow=3, ncol=2, byrow=TRUE))
plot(iasva.sv[,1:2], main="IA-SVA", pch=21, xlab="SV1", ylab="SV2",
     col=color.vec[Cluster], bg=color.vec[Cluster], oma=c(4,4,6,12))
legend("topright", levels(Cluster), border="white", fill=color.vec, bty="n")
plot(pca.res[,1:2], main="PCA", pch=21, xlab="PC1", 
     ylab="PC2", col=color.vec[Cluster], bg=color.vec[Cluster])
plot(sva.res[,1:2], main="USVA", xlab="SV1", ylab="SV2",
     pch=21, col=color.vec[Cluster], bg=color.vec[Cluster])
plot(tsne.res$Y, main="tSNE", xlab="Dimension 1", 
     ylab="Dimension 2", pch=21, col=color.vec[Cluster], bg=color.vec[Cluster])
vioplot(G1, S, G2M, Unknown, names=c("G1", "S", "G2M", "Unknown"), 
   col="gold")
title(xlab="Cell-cycle stage predictions", ylab="IA-SVA factor")
dev.off()

anno.col2 <- data.frame(Cluster=Cluster)
rownames(anno.col2) <- colnames(marker.counts.SV1)
head(anno.col2)

cluster.col <- color.vec[1:2]
names(cluster.col) <- as.vector(levels(Cluster))
anno.colors <- list(Cluster=cluster.col)
anno.colors

pheatmap(log(marker.counts.SV1.long+1), show_colnames =FALSE, 
         clustering_method = "ward.D2",cutree_cols = 2,annotation_col = anno.col2,
         annotation_colors = anno.colors)
pheatmap(log(marker.counts.SV1.long+1), show_colnames =FALSE,
         clustering_method = "ward.D2",cutree_cols = 2,annotation_col = anno.col2,
         annotation_colors = anno.colors,
         filename=paste0("Patel_Glioblastoma_MGH30_iasva_SV1_genes_rsqcutoff0.3_pheatmap_iasvaV0.95_Figure3F.pdf"),
         width=6, height=16)

write.table(as.data.frame(rownames(marker.counts.SV1)), 
            file=paste0("Patel_Glioblastoma_MGH30_Cellcycle_SV1_Genes_rsqcutoff0.4.txt"),
            quote=F, row.names=F, col.names=F, sep=" ")

write.table(as.data.frame(rownames(marker.counts.SV1.long)), 
            file=paste0("Patel_Glioblastoma_MGH30_Cellcycle_SV1_Genes_rsqcutoff0.3.txt"), 
            quote=F, row.names=F, col.names=F, sep=" ")

Session Info

sessionInfo()

dleelab/iasvaExamples documentation built on Aug. 16, 2022, 4:21 a.m.