Detecting hidden heterogeneity in single cell RNA-Seq data

Donghyung Lee

2018-05-03

The iasva package can be used to detect hidden heterogenity within bulk or single cell sequencing data. To illustrate how to use the iasva package for heterogenity detection, we use real-world single cell RNA sequencing (scRNA-Seq) data obtained from human pancreatic islet samples (Lawlor et. al., 2016). 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(Rtsne)
library(pheatmap)
library(corrplot)
library(DescTools) #pcc i.e., Pearson's contingency coefficient
library(RColorBrewer)
library(SummarizedExperiment)

color.vec <- brewer.pal(9, "Set1")[c(1,9)]

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

Load the islet single cell RNA-Seq data

data("Lawlor_Islet_scRNAseq_Read_Counts")
data("Lawlor_Islet_scRNAseq_Annotations")
ls()
## [1] "color.vec"                         "Lawlor_Islet_scRNAseq_Annotations"
## [3] "Lawlor_Islet_scRNAseq_Read_Counts" "normalize"
counts <- Lawlor_Islet_scRNAseq_Read_Counts
anns <- Lawlor_Islet_scRNAseq_Annotations
dim(anns)
## [1] 638  26
dim(counts)
## [1] 26542   638
summary(anns)
##      run             cell.type             COL1A1          INS       
##  Length:638         Length:638         Min.   :1.00   Min.   :1.000  
##  Class :character   Class :character   1st Qu.:1.00   1st Qu.:1.000  
##  Mode  :character   Mode  :character   Median :1.00   Median :1.000  
##                                        Mean   :1.03   Mean   :1.414  
##                                        3rd Qu.:1.00   3rd Qu.:2.000  
##                                        Max.   :2.00   Max.   :2.000  
##                                                                      
##      PRSS1            SST             GCG            KRT19      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :1.000   Median :1.000   Median :1.000   Median :1.000  
##  Mean   :1.038   Mean   :1.039   Mean   :1.375   Mean   :1.044  
##  3rd Qu.:1.000   3rd Qu.:1.000   3rd Qu.:2.000   3rd Qu.:1.000  
##  Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##                                                                 
##       PPY          num.genes             Cell_ID        UNOS_ID   
##  Min.   :1.000   Min.   :3529   10th_C1_S59  :  1   ACCG268 :136  
##  1st Qu.:1.000   1st Qu.:5270   10th_C10_S104:  1   ACJV399 :108  
##  Median :1.000   Median :6009   10th_C11_S96 :  1   ACEL337 :103  
##  Mean   :1.028   Mean   :5981   10th_C13_S61 :  1   ACIW009 : 93  
##  3rd Qu.:1.000   3rd Qu.:6676   10th_C14_S53 :  1   ACCR015A: 57  
##  Max.   :2.000   Max.   :8451   10th_C16_S105:  1   ACIB065 : 57  
##                                 (Other)      :632   (Other) : 84  
##       Age        Biomaterial_Provider    Gender              Phenotype  
##  Min.   :22.00   IIDP      : 45       Female:303   Non-Diabetic   :380  
##  1st Qu.:29.00   Prodo Labs:593       Male  :335   Type 2 Diabetic:258  
##  Median :42.00                                                          
##  Mean   :39.63                                                          
##  3rd Qu.:53.00                                                          
##  Max.   :56.00                                                          
##                                                                         
##                Race          BMI          Cell_Type     Patient_ID 
##  African American:175   Min.   :22.00   INS    :264   P1     :136  
##  Hispanic        :165   1st Qu.:26.60   GCG    :239   P8     :108  
##  White           :298   Median :32.95   KRT19  : 28   P3     :103  
##                         Mean   :32.85   SST    : 25   P7     : 93  
##                         3rd Qu.:35.80   PRSS1  : 24   P5     : 57  
##                         Max.   :55.00   none   : 21   P6     : 57  
##                                         (Other): 37   (Other): 84  
##  Sequencing_Run Batch       Coverage       Percent_Aligned 
##  12t    : 57    B1:193   Min.   :1206135   Min.   :0.8416  
##  4th    : 57    B2:148   1st Qu.:2431604   1st Qu.:0.8769  
##  9th    : 57    B3:297   Median :3042800   Median :0.8898  
##  10t    : 56             Mean   :3160508   Mean   :0.8933  
##  7th    : 55             3rd Qu.:3871697   3rd Qu.:0.9067  
##  3rd    : 53             Max.   :5931638   Max.   :0.9604  
##  (Other):303                                               
##  Mitochondrial_Fraction Num_Expressed_Genes
##  Min.   :0.003873       Min.   :3529       
##  1st Qu.:0.050238       1st Qu.:5270       
##  Median :0.091907       Median :6009       
##  Mean   :0.108467       Mean   :5981       
##  3rd Qu.:0.142791       3rd Qu.:6676       
##  Max.   :0.722345       Max.   :8451       
## 
ContCoef(table(anns$Gender, anns$Cell_Type))
## [1] 0.225969
ContCoef(table(anns$Phenotype, anns$Cell_Type))
## [1] 0.1145096
ContCoef(table(anns$Race, anns$Cell_Type))
## [1] 0.3084146
ContCoef(table(anns$Patient_ID, anns$Cell_Type))
## [1] 0.5232058
ContCoef(table(anns$Batch, anns$Cell_Type))
## [1] 0.3295619

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

Extract alpha cells (GCG expressed cells) from non-diabetics

To illustrate how IA-SVA can be used to detect hidden heterogeneity within a homogenous cell population (i.e., alpha cells), we use read counts of alpha cells from healthy (non-diabetic) subjects (n = 101).

# Selected T2D patients/GCG cell type
counts <- counts[, (anns$Phenotype!="Non-Diabetic")&(anns$Cell_Type=="GCG")]
anns <- subset(anns, (Phenotype!="Non-Diabetic")&(Cell_Type=="GCG"))
dim(counts)

[1] 26542 101

dim(anns)

[1] 101 26

anns <- droplevels(anns)
prop.zeros <- sum(counts==0)/length(counts)
prop.zeros

[1] 0.6952497

# 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)

[1] 14384 101

prop.zeros <- sum(counts==0)/length(counts)
prop.zeros

[1] 0.4520968

## Normalization using SCnorm
## count-depth relationship for all genes
Conditions = rep(c(1), each=101)
countDeptEst <- plotCountDepth(Data = counts, Conditions = Conditions,
                               FilterCellProportion = .1, NCores=3)

DataNorm <- SCnorm(Data = counts, Conditions = Conditions,
                   PrintProgressPlots = FALSE,
                   FilterCellNum = 10,
                   NCores=3)
## Setting up parallel computation using 3 cores
## Gene filter is applied within each condition.
## 1223 genes in condition 1 will not be included in the normalization due to 
##              the specified filter criteria.
## A list of these genes can be accessed in output, 
##     see vignette for example.
## Finding K for Condition 1
## Trying K = 1
## Trying K = 2
## Trying K = 3
## Trying K = 4
## Trying K = 5
## Trying K = 6
## Trying K = 7
## Trying K = 8
## Done!
counts <- results(DataNorm)
summary(colSums(counts))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 1274488 1315313 1332682 1358957 1386293 1650415

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 islet 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(Num_Detected_Genes)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    5787    7043    7925    7881    8652   10188
summary(Geo_Lib)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   16571   20527   24507   24349   27475   33830
barplot(Num_Detected_Genes, xlab="Cell", las=2,
        ylab = "Number of detected genes")

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)
## [1] 0.9778019
cor(Geo_Lib, pc1)
## [1] 0.99461

Run IA-SVA

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

set.seed(4543535)
Patient_ID <- anns$Patient_ID
mod <- model.matrix(~Patient_ID+Geo_Lib)
summ_exp <- SummarizedExperiment(assays = counts)
iasva.res<- iasva(summ_exp, mod[,-1],verbose=FALSE, permute=FALSE, num.sv=5) ##irlba
## IA-SVA running...
## SV1 Detected!
## SV2 Detected!
## SV3 Detected!
## SV4 Detected!
## SV5 Detected!
## # of significant surrogate variables: 5
iasva.sv <- iasva.res$sv
plot(iasva.sv[,1], iasva.sv[,2], xlab="SV1", ylab="SV2")

Cluster <- as.factor(iasva.sv[,2] < 0.1) 
levels(Cluster)=c("Cell1","Cell2")
table(Cluster)
## Cluster
## Cell1 Cell2 
##     6    95
# We identified 6 outlier cells based on SV2 that are marked in red
pairs(iasva.sv, main="IA-SVA", pch=21, col=color.vec[Cluster],
      bg=color.vec[Cluster], oma=c(4,4,6,12)) #4,4,6,12
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[,2])
## [1] 0.1571675
cor(Geo_Lib, iasva.sv[,2])
## [1] 0.2009796
corrplot(cor(iasva.sv))

As shown in the above figure, SV2 clearly separates alpha cells into two groups: 6 outlier cells (marked in red) and the rest of the alpha cells (marked in green). SV3 and SV4 also capture outlier cells. However, we will focus on SV2 in the rest of the analyses.

Find marker genes for the detected heterogeneity (SV2).

Here, using the find_markers() function we find marker genes (n=105 genes) that are significantly associated with SV2 (multiple testing adjusted p-value < 0.05, default significance cutoff, and R-squared value > 0.3, default R-squared cutoff).

# try different R2 thresholds
pdf("Clustering_analyses_figure2.pdf")
r2.results <- study_R2(summ_exp, iasva.sv,selected.svs=2, no.clusters=2)
## # of markers (): 396
## total # of unique markers:  396# of markers (): 237
## total # of unique markers:  237# of markers (): 177
## total # of unique markers:  177# of markers (): 143
## total # of unique markers:  143# of markers (): 108
## total # of unique markers:  108# of markers (): 93
## total # of unique markers:  93# of markers (): 72
## total # of unique markers:  72# of markers (): 58
## total # of unique markers:  58# of markers (): 47
## total # of unique markers:  47# of markers (): 35
## total # of unique markers:  35# of markers (): 27
## total # of unique markers:  27# of markers (): 22
## total # of unique markers:  22# of markers (): 12
## total # of unique markers:  12# of markers (): 8
## total # of unique markers:  8# of markers (): 4
## total # of unique markers:  4# of markers (): 2
## total # of unique markers:  2# of markers (): 1
## total # of unique markers:  1
dev.off()
## quartz_off_screen 
##                 2
marker.counts <- find_markers(summ_exp, as.matrix(iasva.sv[,2]), rsq.cutoff = 0.6)
## # of markers (): 27
## total # of unique markers:  27
marker.counts.long <- find_markers(summ_exp, as.matrix(iasva.sv[,2]), rsq.cutoff = 0.3)
## # of markers (): 108
## total # of unique markers:  108
nrow(marker.counts)
## [1] 27
rownames(marker.counts)
##  [1] "PMEPA1"      "LINC00152"   "MIR4435-1HG" "ENG"         "ITGA5"      
##  [6] "TMEM233"     "PRDM1"       "C8orf4"      "ERG"         "THBS1"      
## [11] "MEF2C"       "LGALS1"      "CD93"        "ELTD1"       "COL4A1"     
## [16] "COL4A2"      "HBEGF"       "SPARC"       "SPARCL1"     "RAPGEF5"    
## [21] "KDR"         "GNG11"       "CD9"         "PODXL"       "PLVAP"      
## [26] "IFI16"       "RHOJ"
nrow(marker.counts.long)
## [1] 108
anno.col <- data.frame(Cluster=Cluster, SV2=iasva.sv[,2])
rownames(anno.col) <- colnames(marker.counts)
head(anno.col)
##             Cluster         SV2
## 4th-C63_S30   Cell2 -0.02724532
## 4th-C66_S36   Cell2 -0.02091476
## 4th-C18_S31   Cell2 -0.01190674
## 4th-C57_S18   Cell1  0.23502845
## 4th-C56_S17   Cell2 -0.03704850
## 4th-C68_S41   Cell2 -0.03570599
cluster.col <- color.vec[1:2]
names(cluster.col) <- as.vector(levels(Cluster))
anno.colors <- list(Cluster=cluster.col)
anno.colors
## $Cluster
##     Cell1     Cell2 
## "#E41A1C" "#999999"
pheatmap(log(marker.counts+1), show_colnames =FALSE, 
         clustering_method = "ward.D2",cutree_cols = 2,annotation_col = anno.col,
         annotation_colors = anno.colors)

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(323542534)
tsne.res <- Rtsne(t(lcounts), dims = 2)
plot(tsne.res$Y, main="tSNE", xlab="tSNE Dim1", ylab="tSNE 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 principal component analysis (PCA) to detect the hidden heterogeneity (SV2).

Here, we use PCA to detect the hidden heterogeneity (SV2) detected by IA-SVA.

set.seed(345233)
pca.res = irlba(lcounts - rowMeans(lcounts), 5)$v ## partial SVD

pairs(pca.res, main="PCA", pch=21, col=color.vec[Cluster],
      bg=color.vec[Cluster], oma=c(4,4,6,12)) #4,4,6,12
legend("right", levels(Cluster), border="white", fill=color.vec, bty="n")

plot(pca.res[,2:3], main="PCA", xlab="PC2", ylab="PC3", 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")

PC3 somewhat captures the six outlier cells, however this seperation is not as clear as the IA-SVA results.

Run surrogate variable analysis (SVA) to detect the hidden heterogeneity (SV2).

Here, for comparison purposes we use SVA (using thre SVs) to detect the hidden heterogeneity in our example data.

mod1 <- model.matrix(~Patient_ID+Geo_Lib)
mod0 <- cbind(mod1[,1])
sva.res = svaseq(counts,mod1,mod0, n.sv=5)$sv
## Number of significant surrogate variables is:  5 
## Iteration (out of 5 ):1  2  3  4  5
pairs(sva.res, main="SVA", pch=21, col=color.vec[Cluster], bg=color.vec[Cluster], oma=c(4,4,6,12)) #4,4,6,12
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")

SV2 is associated with the six outlier samples, however the seperation of these cells is not as clear as the IA-SVA results.

Correlation between SV2 and the geometric library size

cor(Num_Detected_Genes, iasva.sv[,2])
## [1] 0.1571675
cor(Geo_Lib, iasva.sv[,2])
## [1] 0.2009796
pdf(file="Lawlor_Islets_Alpha_Doublets_Figure2_ABCD.pdf", width=5, height=6)
layout(matrix(c(1,2,3,4), nrow=2, 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])
legend("topright", levels(Cluster), border="white", fill=color.vec, bty="n")
plot(pca.res[,2:3], main="PCA", pch=21, xlab="PC2", ylab="PC3", 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])
dev.off()
## quartz_off_screen 
##                 2
anno.col <- data.frame(Cluster=Cluster)
rownames(anno.col) <- colnames(marker.counts)
head(anno.col)
##             Cluster
## 4th-C63_S30   Cell2
## 4th-C66_S36   Cell2
## 4th-C18_S31   Cell2
## 4th-C57_S18   Cell1
## 4th-C56_S17   Cell2
## 4th-C68_S41   Cell2
cluster.col <- color.vec
names(cluster.col) <- as.vector(levels(Cluster))
anno.colors <- list(Cluster=cluster.col)
anno.colors
## $Cluster
##     Cell1     Cell2 
## "#E41A1C" "#999999"
pheatmap(log(marker.counts+1), show_colnames =FALSE, 
         clustering_method = "ward.D2",cutree_cols = 2,annotation_col = anno.col,
         annotation_colors = anno.colors,
         filename="Lawlor_Islets_Alpha_iasva_SV2Markers_rsqcutoff0.6_pheatmap_iasvaV0.95.pdf",
         width=6, height=5)

pheatmap(log(marker.counts.long+1), show_colnames =FALSE,
         clustering_method = "ward.D2",cutree_cols = 2,annotation_col = anno.col,
         annotation_colors = anno.colors,
         filename="Lawlor_Islets_Alpha_iasva_SV2Markers_rsqcutoff0.3_pheatmap_iasvaV0.95.pdf",
         width=6, height=14)
write.table(as.data.frame(rownames(marker.counts)),
            file="Lawlor_Islets_Alpha_Doublets_SV2_Genes_rsqcutoff0.6.txt", quote=F,
              row.names=F, col.names=F, sep=" ")

write.table(as.data.frame(rownames(marker.counts.long)),
            file="Lawlor_Islets_Alpha_Doublets_SV2_Genes_rsqcutoff0.3.txt", quote=F,
              row.names=F, col.names=F, sep=" ")

Session Info

sessionInfo()
## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] SummarizedExperiment_1.10.0 DelayedArray_0.6.0         
##  [3] matrixStats_0.53.1          Biobase_2.40.0             
##  [5] GenomicRanges_1.32.0        GenomeInfoDb_1.16.0        
##  [7] IRanges_2.14.1              S4Vectors_0.18.1           
##  [9] BiocGenerics_0.26.0         RColorBrewer_1.1-2         
## [11] DescTools_0.99.24           corrplot_0.84              
## [13] pheatmap_1.0.8              Rtsne_0.13                 
## [15] SCnorm_1.2.0                sva_3.28.0                 
## [17] BiocParallel_1.14.0         genefilter_1.62.0          
## [19] mgcv_1.8-23                 nlme_3.1-137               
## [21] iasvaExamples_1.0.0         iasva_0.99.0               
## [23] irlba_2.3.2                 Matrix_1.2-14              
## 
## loaded via a namespace (and not attached):
##  [1] bit64_0.9-7            splines_3.5.0          moments_0.14          
##  [4] expm_0.999-2           blob_1.1.1             GenomeInfoDbData_1.1.0
##  [7] yaml_2.1.19            pillar_1.2.2           RSQLite_2.1.0         
## [10] backports_1.1.2        lattice_0.20-35        quantreg_5.35         
## [13] limma_3.36.0           digest_0.6.15          XVector_0.20.0        
## [16] colorspace_1.3-2       htmltools_0.3.6        plyr_1.8.4            
## [19] XML_3.98-1.11          SparseM_1.77           zlibbioc_1.26.0       
## [22] xtable_1.8-2           mvtnorm_1.0-7          scales_0.5.0          
## [25] manipulate_1.0.1       MatrixModels_0.4-1     tibble_1.4.2          
## [28] annotate_1.58.0        ggplot2_2.2.1          lazyeval_0.2.1        
## [31] survival_2.42-3        magrittr_1.5           memoise_1.1.0         
## [34] evaluate_0.10.1        MASS_7.3-50            foreign_0.8-70        
## [37] tools_3.5.0            data.table_1.10.4-3    stringr_1.3.0         
## [40] munsell_0.4.3          cluster_2.0.7-1        AnnotationDbi_1.42.0  
## [43] compiler_3.5.0         rlang_0.2.0            grid_3.5.0            
## [46] RCurl_1.95-4.10        labeling_0.3           bitops_1.0-6          
## [49] rmarkdown_1.9          boot_1.3-20            gtable_0.2.0          
## [52] DBI_1.0.0              reshape2_1.4.3         knitr_1.20            
## [55] bit_1.1-12             rprojroot_1.3-2        stringi_1.2.2         
## [58] Rcpp_0.12.16