pbmc3k 10X result
DongyueXie
2023-02-16
Last updated: 2023-02-19
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Knit directory: gsmash/
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Data
Note: compared to previous results, I changed the initialization for ebpm_normal from log(1+x/s) to ebpm_exponential_mixture posterior log of mean.
The pbmc3k data is from 10X, and I downloaded Gene/cell matrix(raw) dataset.
The quality control and cell types annotation are done by Seurat, following the tutorial here.
The resulting dataset has 2638 cells and 13714 genes.
pbmc3k = readRDS('data/pbmc3k_10x/pbmc3k.rds')
dim(pbmc3k$counts)Loading required package: MatrixNULLThe cell types are
table(pbmc3k$cell_type)
Naive CD4 T CD14+ Mono Memory CD4 T B CD8 T FCGR3A+ Mono
711 480 472 344 279 162
NK DC Platelet
144 32 14 Filter out genes
I filtered out genes that expressed in less than 10 cells, and no further preprocessing. The final data matrix is
library(ebpmf)
library(Matrix)
counts = pbmc3k$counts
counts_filtered = counts[,colSums(counts!=0)>10]
dim(counts_filtered)[1] 2638 10908pbmc3k_sparse = ebpmf_log(counts_filtered,flash_control = list())
saveRDS(pbmc3k_sparse,'pbmc3k_sparse.rds')
pbmc3k_nonnegL = ebpmf_log(counts_filtered,flash_control = list(ebnm.fn=c(ebnm::ebnm_point_exponential,ebnm::ebnm_point_normal),loadings_sign = 1))
saveRDS(pbmc3k_nonnegL,'pbmc3k_nonnegL_pe.rds')
pbmc3k_nonnegLF = ebpmf_log(counts_filtered,flash_control = list(ebnm.fn=c(ebnm::ebnm_point_exponential,ebnm::ebnm_point_exponential),factors_sign=1,loadings_sign = 1))
saveRDS(pbmc3k_nonnegLF,'pbmc3k_nonnegLF_pe.rds')
pbmc3k_nonnegL = ebpmf_log(counts_filtered,flash_control = list(ebnm.fn=c(ebnm::ebnm_unimodal_nonnegative,ebnm::ebnm_point_normal),loadings_sign = 1))
saveRDS(pbmc3k_nonnegL,'pbmc3k_nonnegL_un.rds')
pbmc3k_nonnegLF = ebpmf_log(counts_filtered,flash_control = list(ebnm.fn=c(ebnm::ebnm_unimodal_nonnegative,ebnm::ebnm_unimodal_nonnegative),factors_sign=1,loadings_sign = 1))
saveRDS(pbmc3k_nonnegLF,'pbmc3k_nonnegLF_un.rds')Model fitting
The model is the Empirical Bayes Poisson matrix factorization model,
and \(l_0\) is fixed at
log(rowMeans(Y)), \(f_0\)
is initialized at log(colSums(Y)/sum(l_0), then let the
model estiamtes \(f_0\) during
iterations.
summary_plot = function(res){
plot(res$K_trace,ylab='',xlab='iterations',main='K over iterations',pch=20)
plot(res$sigma2,ylab='',xlab='gene',main = 'gene specific variance',pch=20,col='grey70')
plot(colSums(counts_filtered/rowSums(counts_filtered)),res$sigma2,xlab='gene expression',ylab='gene specific varaince',pch=20,col='grey60')
plot(colSums(counts_filtered==0)/nrow(counts_filtered),res$sigma2,xlab='gene expression sparsity',ylab='gene specific varaince',pch = 20, col='grey50')
}
source('code/poisson_STM/plot_factors.R')
source('code/poisson_STM/structure_plot.R')Sparse loadings and factors
pbmc3k_sparse = readRDS('output/pbmc3k_10x/pbmc_sparse.rds')summary_plot(pbmc3k_sparse)
plot.factors(pbmc3k_sparse$fit_flash,pbmc3k$cell_type)
Nonnegative loadings with point exponential prior, and sparse factors
pbmc3k_nonnegL_pe = readRDS('output/pbmc3k_10x/pbmc_nonnegL_pe.rds')summary_plot(pbmc3k_nonnegL_pe)
plot.factors(pbmc3k_nonnegL_pe$fit_flash,pbmc3k$cell_type,nonnegative = T)
structure_plot_general(pbmc3k_nonnegL_pe$fit_flash$L.pm,pbmc3k_nonnegL_pe$fit_flash$F.pm,pbmc3k$cell_type,LD=T)Running tsne on 532 x 7 matrix.Running tsne on 363 x 7 matrix.Running tsne on 368 x 7 matrix.Running tsne on 259 x 7 matrix.Running tsne on 214 x 7 matrix.Running tsne on 119 x 7 matrix.Running tsne on 110 x 7 matrix.Running tsne on 24 x 7 matrix.
Nonnegative loadings and factors with point exponential prior
pbmc3k_nonnegLF_pe = readRDS('output/pbmc3k_10x/pbmc_nonnegLF_pe.rds')summary_plot(pbmc3k_nonnegLF_pe)
plot.factors(pbmc3k_nonnegLF_pe$fit_flash,pbmc3k$cell_type,nonnegative = T)
structure_plot_general(pbmc3k_nonnegLF_pe$fit_flash$L.pm,pbmc3k_nonnegLF_pe$fit_flash$F.pm,pbmc3k$cell_type,LD=T)Running tsne on 532 x 3 matrix.Running tsne on 363 x 3 matrix.Running tsne on 368 x 3 matrix.Running tsne on 259 x 3 matrix.Running tsne on 214 x 3 matrix.Running tsne on 119 x 3 matrix.Running tsne on 110 x 3 matrix.Running tsne on 24 x 3 matrix.
sessionInfo()R version 4.2.2 Patched (2022-11-10 r83330)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] fastTopics_0.6-142 ggplot2_3.4.0 ebpmf_2.0.5 Matrix_1.5-3
[5] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rtsne_0.16 ebpm_0.0.1.3 colorspace_2.0-3
[4] smashr_1.3-6 ellipsis_0.3.2 mr.ash_0.1-87
[7] rprojroot_2.0.3 fs_1.5.2 rstudioapi_0.14
[10] farver_2.1.1 MatrixModels_0.5-1 ggrepel_0.9.2
[13] fansi_1.0.3 codetools_0.2-19 splines_4.2.2
[16] cachem_1.0.6 knitr_1.41 jsonlite_1.8.4
[19] nloptr_2.0.3 mcmc_0.9-7 ashr_2.2-54
[22] smashrgen_1.1.6 uwot_0.1.14 compiler_4.2.2
[25] httr_1.4.4 RcppZiggurat_0.1.6 fastmap_1.1.0
[28] lazyeval_0.2.2 cli_3.4.1 later_1.3.0
[31] htmltools_0.5.4 quantreg_5.94 prettyunits_1.1.1
[34] tools_4.2.2 coda_0.19-4 gtable_0.3.1
[37] glue_1.6.2 reshape2_1.4.4 dplyr_1.0.10
[40] Rcpp_1.0.9 softImpute_1.4-1 jquerylib_0.1.4
[43] vctrs_0.5.1 iterators_1.0.14 wavethresh_4.7.2
[46] xfun_0.35 stringr_1.5.0 ps_1.7.2
[49] trust_0.1-8 lifecycle_1.0.3 irlba_2.3.5.1
[52] NNLM_0.4.4 getPass_0.2-2 MASS_7.3-58.2
[55] scales_1.2.1 hms_1.1.2 promises_1.2.0.1
[58] parallel_4.2.2 SparseM_1.81 yaml_2.3.6
[61] pbapply_1.6-0 sass_0.4.4 stringi_1.7.8
[64] SQUAREM_2021.1 highr_0.9 deconvolveR_1.2-1
[67] foreach_1.5.2 caTools_1.18.2 truncnorm_1.0-8
[70] shape_1.4.6 horseshoe_0.2.0 rlang_1.0.6
[73] pkgconfig_2.0.3 matrixStats_0.63.0 bitops_1.0-7
[76] ebnm_1.0-12 stm_2.0.5 evaluate_0.19
[79] lattice_0.20-45 invgamma_1.1 purrr_0.3.5
[82] labeling_0.4.2 htmlwidgets_1.6.0 Rfast_2.0.6
[85] cowplot_1.1.1 processx_3.8.0 tidyselect_1.2.0
[88] plyr_1.8.8 magrittr_2.0.3 R6_2.5.1
[91] generics_0.1.3 pillar_1.8.1 whisker_0.4.1
[94] withr_2.5.0 survival_3.5-0 mixsqp_0.3-48
[97] tibble_3.1.8 crayon_1.5.2 utf8_1.2.2
[100] plotly_4.10.1 rmarkdown_2.19 progress_1.2.2
[103] grid_4.2.2 data.table_1.14.6 callr_3.7.3
[106] git2r_0.30.1 digest_0.6.31 vebpm_0.4.3
[109] tidyr_1.2.1 httpuv_1.6.7 MCMCpack_1.6-3
[112] RcppParallel_5.1.5 munsell_0.5.0 glmnet_4.1-6
[115] viridisLite_0.4.1 flashier_0.2.34 bslib_0.4.2
[118] quadprog_1.5-8