Detecting hidden heterogeneity in single cell RNA-Seq data

Load packages

library(irlba) 
library(iasva)
library(sva)
library(Rtsne)
library(pheatmap)
library(corrplot)
library(DescTools)
library(RColorBrewer)
library(SummarizedExperiment)
set.seed(100)
color.vec <- brewer.pal(3, "Set1")

Load the islet single cell RNA-Seq data

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).

counts_file <- system.file("extdata", "iasva_counts_test.Rds",
                         package = "iasva")
# matrix of read counts where genes are rows, samples are columns
counts <- readRDS(counts_file)
# matrix of sample annotations/metadata
anns_file <- system.file("extdata", "iasva_anns_test.Rds",
                         package = "iasva")
anns <- readRDS(anns_file)

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 a 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 the geometric library size vector, which will be used as a known factor in the IA-SVA algorithm.

geo_lib_size <- colSums(log(counts + 1))
barplot(geo_lib_size, xlab = "Cell", ylab = "Geometric Lib Size", las = 2)

lcounts <- log(counts + 1)

# PC1 and Geometric library size correlation
pc1 <- irlba(lcounts - rowMeans(lcounts), 1)$v[, 1]
cor(geo_lib_size, pc1)

## [1] -0.99716

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(100)
patient_id <- anns$Patient_ID
mod <- model.matrix(~patient_id + geo_lib_size)
# create a summarizedexperiment class
summ_exp <- SummarizedExperiment(assays = counts)
iasva.res<- iasva(summ_exp, mod[, -1],verbose = FALSE, 
                  permute = FALSE, num.sv = 5)

## IA-SVA running...

## 
## SV 1 Detected!

## 
## SV 2 Detected!

## 
## SV 3 Detected!

## 
## SV 4 Detected!

## 
## SV 5 Detected!

## 
## # of significant surrogate variables: 5

iasva.sv <- iasva.res$sv
plot(iasva.sv[, 1], iasva.sv[, 2], xlab = "SV1", ylab = "SV2")

cell_type <- as.factor(iasva.sv[, 1] > -0.1) 
levels(cell_type) <- c("Cell1", "Cell2")
table(cell_type)

## cell_type
## Cell1 Cell2 
##     6    95

# We identified 6 outlier cells based on SV1 that are marked in red
pairs(iasva.sv, main = "IA-SVA", pch = 21, col = color.vec[cell_type],
      bg = color.vec[cell_type], oma = c(4,4,6,12))
legend("right", levels(cell_type), fill = color.vec, bty = "n")

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

cor(geo_lib_size, iasva.sv[, 1])

## [1] -0.1469422

corrplot(cor(iasva.sv))

As shown in the above figure, SV1 clearly separates alpha cells into two groups: 6 outlier cells (marked in red) and the rest of the alpha cells (marked in blue). ## 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, default R-squared cutoff).

marker.counts <- find_markers(summ_exp, as.matrix(iasva.sv[,1]))

## # of markers (): 33

## total # of unique markers: 33

nrow(marker.counts)

## [1] 33

rownames(marker.counts)

##  [1] "PMEPA1"   "FAM198B"  "FLT1"     "ENG"      "SOX4"     "ITGA5"   
##  [7] "PXDN"     "PRDM1"    "ERG"      "CLIC4"    "A2M"      "PPAP2B"  
## [13] "THBS1"    "CLIC2"    "S100A16"  "STC1"     "ACVRL1"   "COL4A1"  
## [19] "MSN"      "TNFAIP2"  "MMP2"     "SERPINE1" "SPARC"    "SPARCL1" 
## [25] "ESAM"     "KDR"      "CD9"      "CXCR4"    "PODXL"    "PLVAP"   
## [31] "CALD1"    "MMP1"     "ADAMTS4"

anno.col <- data.frame(cell_type = cell_type)
rownames(anno.col) <- colnames(marker.counts)
head(anno.col)

##             cell_type
## 4th-C63_S30     Cell2
## 4th-C66_S36     Cell2
## 4th-C18_S31     Cell2
## 4th-C57_S18     Cell1
## 4th-C56_S17     Cell2
## 4th-C68_S41     Cell2

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

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(100)
tsne.res <- Rtsne(t(lcounts), dims = 2)

plot(tsne.res$Y, main = "tSNE", xlab = "tSNE Dim1", ylab = "tSNE Dim2",
     pch = 21, col = color.vec[cell_type], bg = color.vec[cell_type],
     oma = c(4, 4, 6, 12))
legend("bottomright", levels(cell_type), 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(100)
tsne.res <- Rtsne(unique(t(log(marker.counts + 1))), dims = 2)
plot(tsne.res$Y, main = "tSNE post IA-SVA", xlab = "tSNE Dim1",
     ylab = "tSNE Dim2", pch = 21, col = color.vec[cell_type],
     bg = color.vec[cell_type], oma = c(4, 4, 6, 12))
legend("bottomright", levels(cell_type), fill = color.vec, bty = "n")

tSNE using SV1 marker genes better seperate these ourlier cells. This analyses suggest that gene selection using IA-SVA combined with tSNE analyses can be a powerful way to detect rare cells introducing variability in the single cell gene expression data.

Using a faster implementation of IA-SVA (fast_iasva)

Here, we run a faster implementation of IA-SVA using the same known factors (patient_id and geo_lib_size) as demonstrated above. This function is useful when working with particularly large datasets.

iasva.res <- fast_iasva(summ_exp, mod[, -1], num.sv = 5)

## fast IA-SVA running...

## 
## SV 1 Detected!

## 
## SV 2 Detected!

## 
## SV 3 Detected!

## 
## SV 4 Detected!

## 
## SV 5 Detected!

## 
## # of obtained surrogate variables: 5

Tuning parameters for IA-SVA

The R-squared thresholds used to identify marker genes (find_markers) can greatly influence the 1) number of marker genes identified and 2) the quality of clustering results. With the study_R2() function, users can visualize how different R-squared thresholds influence both factors.

study_res <- study_R2(summ_exp, iasva.sv)

## # of markers (): 274

## total # of unique markers: 274

## # of markers (): 177

## total # of unique markers: 177

## # of markers (): 123

## total # of unique markers: 123

## # of markers (): 84

## total # of unique markers: 84

## # of markers (): 54

## total # of unique markers: 54

## # of markers (): 39

## total # of unique markers: 39

## # of markers (): 30

## total # of unique markers: 30

## # of markers (): 23

## total # of unique markers: 23

## # of markers (): 7

## total # of unique markers: 7

## # of markers (): 3

## total # of unique markers: 3

## # of markers (): 0

## total # of unique markers: 0

This function produces a plot of the number of genes selected vs. the cluster quality (average silhouette score) for different R-squared values.

Session Info

sessionInfo()

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] SummarizedExperiment_1.22.0 Biobase_2.52.0             
##  [3] GenomicRanges_1.44.0        GenomeInfoDb_1.28.0        
##  [5] IRanges_2.26.0              S4Vectors_0.30.0           
##  [7] BiocGenerics_0.38.0         MatrixGenerics_1.4.0       
##  [9] matrixStats_0.58.0          RColorBrewer_1.1-2         
## [11] DescTools_0.99.41           corrplot_0.88              
## [13] pheatmap_1.0.12             Rtsne_0.15                 
## [15] sva_3.40.0                  BiocParallel_1.26.0        
## [17] genefilter_1.74.0           mgcv_1.8-35                
## [19] nlme_3.1-152                iasva_1.10.0               
## [21] irlba_2.3.3                 Matrix_1.3-3               
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7           bit64_4.0.5            httr_1.4.2            
##  [4] tools_4.1.0            bslib_0.2.5.1          R6_2.5.0              
##  [7] DBI_1.1.1              colorspace_2.0-1       Exact_2.1             
## [10] bit_4.0.4              compiler_4.1.0         expm_0.999-6          
## [13] DelayedArray_0.18.0    sass_0.4.0             scales_1.1.1          
## [16] mvtnorm_1.1-1          proxy_0.4-25           stringr_1.4.0         
## [19] digest_0.6.27          rmarkdown_2.8          XVector_0.32.0        
## [22] htmltools_0.5.1.1      highr_0.9              fastmap_1.1.0         
## [25] limma_3.48.0           rlang_0.4.11           rstudioapi_0.13       
## [28] RSQLite_2.2.7          farver_2.1.0           jquerylib_0.1.4       
## [31] jsonlite_1.7.2         RCurl_1.98-1.3         magrittr_2.0.1        
## [34] GenomeInfoDbData_1.2.6 Rcpp_1.0.6             munsell_0.5.0         
## [37] lifecycle_1.0.0        stringi_1.6.2          yaml_2.2.1            
## [40] edgeR_3.34.0           rootSolve_1.8.2.1      MASS_7.3-54           
## [43] zlibbioc_1.38.0        grid_4.1.0             blob_1.2.1            
## [46] crayon_1.4.1           lmom_2.8               lattice_0.20-44       
## [49] Biostrings_2.60.0      splines_4.1.0          annotate_1.70.0       
## [52] KEGGREST_1.32.0        locfit_1.5-9.4         knitr_1.33            
## [55] boot_1.3-28            gld_2.6.2              XML_3.99-0.6          
## [58] evaluate_0.14          data.table_1.14.0      png_0.1-7             
## [61] vctrs_0.3.8            gtable_0.3.0           cachem_1.0.5          
## [64] xfun_0.23              xtable_1.8-4           e1071_1.7-6           
## [67] class_7.3-19           survival_3.2-11        AnnotationDbi_1.54.0  
## [70] memoise_2.0.0          cluster_2.1.2