Last updated: 2023-02-20

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The dataset is “droplet” UMI count data from Montoro et al (2018)—these are gene expression profiles of trachea epithelial cells in C57BL/6 mice obtained using droplet-based 3’ single-cell RNA-seq—for topic modeling analysis.

See here for data preparations.

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

library(Matrix) 
load('/project2/mstephens/pcarbo/git/single-cell-topics/data/droplet.RData')
counts = counts[,colSums(counts!=0)>10]
dim(counts)
[1]  7193 14315

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,counts){
  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/rowSums(counts)),res$sigma2,xlab='gene expression',ylab='gene specific varaince',pch=20,col='grey60')
  plot(colSums(counts==0)/nrow(counts),res$sigma2,xlab='gene expression sparsity',ylab='gene specific varaince',pch = 20, col='grey50')
}

Sparse loadings and factors

source('code/poisson_STM/plot_factors.R')
res = readRDS('/project2/mstephens/dongyue/poisson_mf/droplet/ebpmf_droplet_sparse.rds')
summary_plot(res,counts)

Version Author Date
603e676 DongyueXie 2023-01-23 

plot.factors(res$fit_flash,samples$tissue)

plot(res$fit_flash$pve[-c(1,2)],xlab='factors',ylab='PVE')

Version Author Date
603e676 DongyueXie 2023-01-23
eb3aa65 DongyueXie 2022-12-07 
f0_init = log(colSums(counts)/sum(rowMeans(counts)))
plot(res$fit_flash$F.pm[,2],f0_init,xlab='f0 estimates',ylab='f0 init values',pch=20,col='grey70')
abline(a=0,b=1)

Version Author Date
603e676 DongyueXie 2023-01-23
eb3aa65 DongyueXie 2022-12-07

Nonnegative loadings with point exponential prior, and sparse factors

source('code/poisson_STM/structure_plot.R')
res = readRDS('/project2/mstephens/dongyue/poisson_mf/droplet/ebpmf_droplet_nonnegL.rds')
summary_plot(res,counts)

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

plot.factors(res$fit_flash,samples$tissue,nonnegative = T)
Warning: Removed 313 rows containing missing values (`geom_point()`).

structure_plot_general(res$fit_flash$L.pm[,order(res$fit_flash$pve,decreasing = T)[1:14]],res$fit_flash$F.pm[,order(res$fit_flash$pve,decreasing = T)[1:14]],samples$tissue,LD=T)
Running tsne on 1117 x 12 matrix.
Running tsne on 112 x 12 matrix.
Running tsne on 683 x 12 matrix.
Running tsne on 25 x 12 matrix.
Running tsne on 41 x 12 matrix.

plot(res$fit_flash$pve[-c(1,2)],xlab='factors',ylab='PVE')

Nonnegative loadings and factors with point exponential prior

res = readRDS('/project2/mstephens/dongyue/poisson_mf/droplet/ebpmf_droplet_nonnegLF.rds')
summary_plot(res,counts)

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

plot.factors(res$fit_flash,samples$tissue,nonnegative = T)
Warning: Removed 313 rows containing missing values (`geom_point()`).

structure_plot_general(res$fit_flash$L.pm,res$fit_flash$F.pm,samples$tissue,LD=T)
Running tsne on 1117 x 12 matrix.
Running tsne on 112 x 12 matrix.
Running tsne on 683 x 12 matrix.
Running tsne on 25 x 12 matrix.
Running tsne on 41 x 12 matrix.

plot(res$fit_flash$pve[-c(1,2)],xlab='factors',ylab='PVE')


sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /software/R-4.1.0-no-openblas-el7-x86_64/lib64/R/lib/libRblas.so
LAPACK: /software/R-4.1.0-no-openblas-el7-x86_64/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C         LC_TIME=C           
 [4] LC_COLLATE=C         LC_MONETARY=C        LC_MESSAGES=C       
 [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C        
[10] LC_TELEPHONE=C       LC_MEASUREMENT=C     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      Matrix_1.5-3       workflowr_1.6.2   

loaded via a namespace (and not attached):
 [1] mcmc_0.9-7         fs_1.5.0           progress_1.2.2     httr_1.4.4        
 [5] rprojroot_2.0.2    tools_4.1.0        bslib_0.2.5.1      utf8_1.2.2        
 [9] R6_2.5.1           irlba_2.3.5.1      uwot_0.1.14        DBI_1.1.1         
[13] lazyeval_0.2.2     colorspace_2.0-3   withr_2.5.0        prettyunits_1.1.1 
[17] tidyselect_1.2.0   compiler_4.1.0     git2r_0.28.0       cli_3.5.0         
[21] quantreg_5.94      SparseM_1.81       plotly_4.10.1      labeling_0.4.2    
[25] horseshoe_0.2.0    sass_0.4.0         flashier_0.2.34    scales_1.2.1      
[29] SQUAREM_2021.1     quadprog_1.5-8     pbapply_1.6-0      mixsqp_0.3-48     
[33] stringr_1.4.0      digest_0.6.30      rmarkdown_2.9      MCMCpack_1.6-3    
[37] deconvolveR_1.2-1  pkgconfig_2.0.3    htmltools_0.5.3    fastmap_1.1.0     
[41] invgamma_1.1       highr_0.9          htmlwidgets_1.5.4  rlang_1.0.6       
[45] rstudioapi_0.13    jquerylib_0.1.4    farver_2.1.1       generics_0.1.3    
[49] jsonlite_1.8.3     dplyr_1.0.10       magrittr_2.0.3     Rcpp_1.0.9        
[53] munsell_0.5.0      fansi_1.0.3        lifecycle_1.0.3    stringi_1.6.2     
[57] whisker_0.4        yaml_2.3.6         MASS_7.3-54        Rtsne_0.16        
[61] plyr_1.8.6         grid_4.1.0         parallel_4.1.0     promises_1.2.0.1  
[65] ggrepel_0.9.2      crayon_1.5.2       lattice_0.20-44    cowplot_1.1.1     
[69] splines_4.1.0      hms_1.1.2          knitr_1.33         pillar_1.8.1      
[73] softImpute_1.4-1   reshape2_1.4.4     glue_1.6.2         evaluate_0.14     
[77] trust_0.1-8        stm_1.3.6          data.table_1.14.6  RcppParallel_5.1.5
[81] vctrs_0.5.1        httpuv_1.6.1       MatrixModels_0.5-1 gtable_0.3.1      
[85] purrr_0.3.5        ebnm_1.0-11        tidyr_1.2.1        assertthat_0.2.1  
[89] ashr_2.2-54        xfun_0.24          coda_0.19-4        later_1.3.0       
[93] survival_3.2-11    viridisLite_0.4.1  truncnorm_1.0-8    tibble_3.1.8      
[97] ellipsis_0.3.2