pbmc3k data in fasttopic package, result

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Last updated: 2023-02-19

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Introduction

This pbmc3k data is from fastTopics package, and is described as “These data are a selection of the reference transcriptome profiles generated via single-cell RNA sequencing (RNA-seq) of 10 bead-enriched subpopulations of PBMCs (Donor A), described in Zheng et al (2017).”

Filter out genes

I filtered out genes that expressed in less than 10 cells, and no further preprocessing. The final data matrix is

library(fastTopics)
library(stm)
library(Matrix)
data(pbmc_facs)

## original data fit
counts = pbmc_facs$counts
counts_filtered = counts[,colSums(counts!=0)>10]
dim(counts_filtered)

[1]  3774 11487

pbmc3k_sparse = ebpmf_log(counts_filtered,flash_control = list())
saveRDS(pbmc3k_sparse,'output/pbmc_fasttopics/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,'output/pbmc_fasttopics/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,'output/pbmc_fasttopics/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,'output/pbmc_fasttopics/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,'output/pbmc_fasttopics/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 estimates \(f_0\) 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$sigma2,xlab='gene expression',ylab='gene specific variance',pch=20,col='grey60')
  plot(colSums(counts_filtered==0)/nrow(counts_filtered),res$sigma2,xlab='gene expression sparsity',ylab='gene specific variance',pch = 20, col='grey50')
}

Sparse loadings and factors

source('code/poisson_STM/plot_factors.R')
pbmc3k_sparse = readRDS('output/pbmc_fasttopics/pbmc3k_sparse.rds')

summary_plot(pbmc3k_sparse)

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plot.factors(pbmc3k_sparse$fit_flash,pbmc_facs$samples$subpop)

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f0_init = log(colSums(counts_filtered)/sum(rowMeans(counts_filtered)))
plot(pbmc3k_sparse$fit_flash$F.pm[,2],f0_init,xlab='f0 estimates',ylab='f0 init values',pch=20,col='grey70')
abline(a=0,b=1)

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Nonnegative loadings with point exponential prior, and sparse factors

source('code/poisson_STM/structure_plot.R')
pbmc3k_nonnegL_pe = readRDS('output/pbmc_fasttopics/pbmc3k_nonnegL_pe.rds')

summary_plot(pbmc3k_nonnegL_pe)

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plot(pbmc3k_nonnegL_pe$fit_flash$F.pm[,2],f0_init,xlab='f0 estimates',ylab='f0 init value',pch=20,col='grey70')
abline(a=0,b=1)

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plot.factors(pbmc3k_nonnegL_pe$fit_flash,pbmc_facs$samples$subpop,nonnegative = T)

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structure_plot_general(pbmc3k_nonnegL_pe$fit_flash$L.pm,pbmc3k_nonnegL_pe$fit_flash$F.pm,pbmc_facs$samples$subpop,LD=T)

Running tsne on 417 x 7 matrix.

Running tsne on 91 x 7 matrix.

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Nonnegative loadings with unimodal nonnegative prior, and sparse factors

pbmc3k_nonnegL_un = readRDS('output/pbmc_fasttopics/pbmc3k_nonnegL_un.rds')

summary_plot(pbmc3k_nonnegL_un)

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plot(pbmc3k_nonnegL_un$fit_flash$F.pm[,2],f0_init,xlab='f0 estimates',ylab='f0 init value',pch=20,col='grey70')
abline(a=0,b=1)

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plot.factors(pbmc3k_nonnegL_un$fit_flash,pbmc_facs$samples$subpop,nonnegative = T)

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structure_plot_general(pbmc3k_nonnegL_un$fit_flash$L.pm,pbmc3k_nonnegL_un$fit_flash$F.pm,pbmc_facs$samples$subpop,LD=T)

Running tsne on 417 x 6 matrix.

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Nonnegative loadings and factors with point exponential prior

pbmc3k_nonnegLF_pe = readRDS('output/pbmc_fasttopics/pbmc3k_nonnegLF_pe.rds')

summary_plot(pbmc3k_nonnegLF_pe)

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plot(pbmc3k_nonnegLF_pe$fit_flash$F.pm[,2],f0_init,xlab='f0 estimates',ylab='f0 init value',pch=20,col='grey70')
abline(a=0,b=1)

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plot.factors(pbmc3k_nonnegLF_pe$fit_flash,pbmc_facs$samples$subpop,nonnegative = T)

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structure_plot_general(pbmc3k_nonnegLF_pe$fit_flash$L.pm,pbmc3k_nonnegLF_pe$fit_flash$F.pm,pbmc_facs$samples$subpop,LD=T)

Running tsne on 417 x 7 matrix.

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Nonnegative loadings and factors with unimodal nonnegative prior

pbmc3k_nonnegLF_un = readRDS('output/pbmc_fasttopics/pbmc3k_nonnegLF_un.rds')

summary_plot(pbmc3k_nonnegLF_un)

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plot(pbmc3k_nonnegLF_un$fit_flash$F.pm[,2],f0_init,xlab='f0 estimates',ylab='f0 init value',pch=20,col='grey70')
abline(a=0,b=1)

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plot.factors(pbmc3k_nonnegLF_un$fit_flash,pbmc_facs$samples$subpop,nonnegative = T)

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structure_plot_general(pbmc3k_nonnegLF_un$fit_flash$L.pm,pbmc3k_nonnegLF_un$fit_flash$F.pm,pbmc_facs$samples$subpop,LD=T)

Running tsne on 417 x 11 matrix.

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Running tsne on 359 x 11 matrix.

Running tsne on 775 x 11 matrix.

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Session information

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] ggplot2_3.4.0      Matrix_1.5-3       stm_2.0.5          fastTopics_0.6-142
[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     shape_1.4.6       
 [70] truncnorm_1.0-8    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        evaluate_0.19      lattice_0.20-45   
 [79] invgamma_1.1       purrr_0.3.5        labeling_0.4.2    
 [82] htmlwidgets_1.6.0  Rfast_2.0.6        cowplot_1.1.1     
 [85] processx_3.8.0     tidyselect_1.2.0   plyr_1.8.8        
 [88] magrittr_2.0.3     R6_2.5.1           generics_0.1.3    
 [91] withr_2.5.0        pillar_1.8.1       whisker_0.4.1     
 [94] survival_3.5-0     mixsqp_0.3-48      tibble_3.1.8      
 [97] crayon_1.5.2       utf8_1.2.2         plotly_4.10.1     
[100] rmarkdown_2.19     progress_1.2.2     grid_4.2.2        
[103] data.table_1.14.6  callr_3.7.3        git2r_0.30.1      
[106] digest_0.6.31      vebpm_0.4.3        tidyr_1.2.1       
[109] httpuv_1.6.7       MCMCpack_1.6-3     RcppParallel_5.1.5
[112] munsell_0.5.0      glmnet_4.1-6       viridisLite_0.4.1 
[115] flashier_0.2.34    bslib_0.4.2        quadprog_1.5-8