scde using PBMC data
DongyueXie
2020-01-15
Last updated: 2020-01-16
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Knit directory: misc/
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Introduction
In my previous analysis, I tried RUV methods on single-cell RNA-Seq data. I only used top 1000 expressed genes from the dataset. So only a few of gene expressions are 0. This is not what typically scRNA data are. Also, I tried only in total 50 cells.
This time, I’m going to try PBMC data prepared by Satjia Lab.
library(MAST)Loading required package: SingleCellExperimentLoading required package: SummarizedExperimentLoading required package: GenomicRangesLoading required package: stats4Loading required package: BiocGenericsLoading required package: parallel
Attaching package: 'BiocGenerics'The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLBThe following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
anyDuplicated, append, as.data.frame, basename, cbind,
colnames, dirname, do.call, duplicated, eval, evalq, Filter,
Find, get, grep, grepl, intersect, is.unsorted, lapply, Map,
mapply, match, mget, order, paste, pmax, pmax.int, pmin,
pmin.int, Position, rank, rbind, Reduce, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.minLoading required package: S4Vectors
Attaching package: 'S4Vectors'The following object is masked from 'package:base':
expand.gridLoading required package: IRangesLoading required package: GenomeInfoDbLoading required package: BiobaseWelcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.Loading required package: DelayedArrayLoading required package: matrixStats
Attaching package: 'matrixStats'The following objects are masked from 'package:Biobase':
anyMissing, rowMediansLoading required package: BiocParallel
Attaching package: 'DelayedArray'The following objects are masked from 'package:matrixStats':
colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRangesThe following objects are masked from 'package:base':
aperm, apply, rowsum
Attaching package: 'MAST'The following object is masked from 'package:stats':
filterlibrary(Seurat)
Attaching package: 'Seurat'The following object is masked from 'package:SummarizedExperiment':
Assaysdatax = readRDS('data/pbmc_counts.rds')@assays$RNA
datax = datax[rowSums(datax)>0,]
clusters = readRDS('data/pbmc.rds')
cell_cluster = clusters@colData$seuratHow many zeros are there? A lot
sum(datax==0)/(dim(datax)[1]*dim(datax)[2])[1] 0.9381137Let’s only use the Naive CD4+ T cells, which corresponds to the first cluster.
CDT_idx = which(cell_cluster == 1)
CDT = datax[,CDT_idx]
set.seed(12345)
group1_idx = sample(1:ncol(CDT),ncol(CDT)/2)
group1 = CDT[,group1_idx]
group2 = CDT[,-group1_idx]
## for each gene, run a two-sample t test
p_values1 = c()
for(i in 1:nrow(CDT)){
p_values1[i] = t.test(log(group1[i,]+1),log(group2[i,]+1),alternative='two.sided')$p.value
}
hist(p_values1,breaks = 15)
summary(p_values1) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0001 0.2539 0.4368 0.4842 0.7154 0.9997 1225 There are 1225 genes that have no expression in any Naive CD4+ T cells. Remove these genes. Now we have not filtered out any genes.
CDT = CDT[-which((rowSums(CDT)==0)),]Again, t-test does not apply here since for a lot of genes, only one cell has non-zero read counts among two groups.
How about apply TMM then t-test?
library(edgeR)Loading required package: limma
Attaching package: 'limma'The following object is masked from 'package:BiocGenerics':
plotMA
Attaching package: 'edgeR'The following object is masked from 'package:SingleCellExperiment':
cpmtmm = calcNormFactors(CDT,method='TMM')
CDTnorm = cpm(CDT,tmm)
group1 = CDTnorm[,group1_idx]
group2 = CDTnorm[,-group1_idx]
## for each gene, run a two-sample t test
p_values1 = c()
for(i in 1:nrow(CDT)){
p_values1[i] = t.test(log(group1[i,]+1),log(group2[i,]+1),alternative='two.sided')$p.value
}
hist(p_values1,breaks = 15)
summary(p_values1) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0001423 0.2534142 0.4427949 0.4865154 0.7256823 0.9999975 Now let’s apply ROTS method for scDE, which is one of the best performance method tested in Sonenson and Delorenzi.
First Normalize the counts by Trimmed Mean of M-values as required by ROTS. Then apply ROTS.
group = rep(0,ncol(CDT))
group[group1_idx] = 1
library(ROTS)
#ROTS_results = ROTS(data = CDTnorm, groups = group , B = 100 , K = 500 , seed = 1234)
load('data/ROTS_results.RData')
summary(ROTS_results, fdr = 0.05)ROTS results:
Number of resamplings: 100
a1: 4.6
a2: 1
Top list size: 390
Reproducibility value: 0.1662051
Z-score: 5.651558
0 rows satisfy the condition.
Row ROTS-statistic pvalue FDRhist(ROTS_results$pvalue,breaks = 15)
Let’s reduce the number of cells in each group. Reudce from 355 to 50.
n_cell = 100
set.seed(12345)
CDT_subset = CDT[,sample(1:ncol(CDT),n_cell)]
tmm = calcNormFactors(CDT_subset,method='TMM')
CDTnorm_subset = cpm(CDT_subset,tmm)
group = rep(0,ncol(CDTnorm_subset))
group1_idx = sample(1:n_cell,n_cell/2)
group[group1_idx] = 1
ROTS_results2 = ROTS(data = CDTnorm_subset, groups = group , B = 100 , K = 500 , seed = 1234)Bootstrapping samplesOptimizing parametersCalculating p-valuesCalculating FDRsummary(ROTS_results2, fdr = 0.05)ROTS results:
Number of resamplings: 100
a1: 3
a2: 1
Top list size: 190
Reproducibility value: 0.1723158
Z-score: 2.933863
0 rows satisfy the condition.
Row ROTS-statistic pvalue FDRhist(ROTS_results2$pvalue,breaks = 15)
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] ROTS_1.12.0 edgeR_3.26.8
[3] limma_3.40.6 Seurat_3.1.2
[5] MAST_1.10.0 SingleCellExperiment_1.6.0
[7] SummarizedExperiment_1.14.1 DelayedArray_0.10.0
[9] BiocParallel_1.18.1 matrixStats_0.55.0
[11] Biobase_2.44.0 GenomicRanges_1.36.1
[13] GenomeInfoDb_1.20.0 IRanges_2.18.3
[15] S4Vectors_0.22.1 BiocGenerics_0.30.0
loaded via a namespace (and not attached):
[1] backports_1.1.5 blme_1.0-4 workflowr_1.5.0
[4] sn_1.5-4 plyr_1.8.4 igraph_1.2.4.1
[7] lazyeval_0.2.2 splines_3.6.1 listenv_0.8.0
[10] ggplot2_3.2.1 TH.data_1.0-10 digest_0.6.21
[13] htmltools_0.4.0 gdata_2.18.0 magrittr_1.5
[16] cluster_2.1.0 ROCR_1.0-7 globals_0.12.5
[19] RcppParallel_4.4.4 R.utils_2.9.0 sandwich_2.5-1
[22] colorspace_1.4-1 ggrepel_0.8.1 xfun_0.10
[25] dplyr_0.8.3 crayon_1.3.4 RCurl_1.95-4.12
[28] jsonlite_1.6 lme4_1.1-21 zeallot_0.1.0
[31] survival_2.44-1.1 zoo_1.8-6 ape_5.3
[34] glue_1.3.1 gtable_0.3.0 zlibbioc_1.30.0
[37] XVector_0.24.0 leiden_0.3.1 future.apply_1.4.0
[40] abind_1.4-5 scales_1.0.0 mvtnorm_1.0-11
[43] bibtex_0.4.2 Rcpp_1.0.2 metap_1.2
[46] plotrix_3.7-7 viridisLite_0.3.0 reticulate_1.13
[49] rsvd_1.0.2 SDMTools_1.1-221.2 tsne_0.1-3
[52] htmlwidgets_1.5.1 httr_1.4.1 gplots_3.0.1.1
[55] RColorBrewer_1.1-2 TFisher_0.2.0 ica_1.0-2
[58] pkgconfig_2.0.3 R.methodsS3_1.7.1 uwot_0.1.5
[61] locfit_1.5-9.1 tidyselect_0.2.5 rlang_0.4.0
[64] reshape2_1.4.3 later_1.0.0 munsell_0.5.0
[67] tools_3.6.1 ggridges_0.5.2 evaluate_0.14
[70] stringr_1.4.0 yaml_2.2.0 npsurv_0.4-0
[73] knitr_1.25 fs_1.3.1 fitdistrplus_1.0-14
[76] caTools_1.17.1.2 purrr_0.3.2 RANN_2.6.1
[79] pbapply_1.4-2 future_1.15.1 nlme_3.1-141
[82] whisker_0.4 R.oo_1.23.0 compiler_3.6.1
[85] plotly_4.9.1 png_0.1-7 lsei_1.2-0
[88] tibble_2.1.3 stringi_1.4.3 lattice_0.20-38
[91] Matrix_1.2-17 nloptr_1.2.1 multtest_2.40.0
[94] vctrs_0.2.0 mutoss_0.1-12 pillar_1.4.2
[97] lifecycle_0.1.0 Rdpack_0.11-0 lmtest_0.9-37
[100] RcppAnnoy_0.0.13 data.table_1.12.6 cowplot_1.0.0
[103] bitops_1.0-6 irlba_2.3.3 gbRd_0.4-11
[106] httpuv_1.5.2 R6_2.4.0 promises_1.1.0
[109] KernSmooth_2.23-15 gridExtra_2.3 codetools_0.2-16
[112] boot_1.3-23 MASS_7.3-51.4 gtools_3.8.1
[115] assertthat_0.2.1 rprojroot_1.3-2 sctransform_0.2.1
[118] mnormt_1.5-5 multcomp_1.4-12 GenomeInfoDbData_1.2.1
[121] grid_3.6.1 tidyr_1.0.0 minqa_1.2.4
[124] rmarkdown_1.16 Rtsne_0.15 git2r_0.26.1
[127] numDeriv_2016.8-1.1