Last updated: 2023-01-23

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Rmd 19e097f DongyueXie 2023-01-23 wflow_publish("analysis/run_flash_on_latent_M_glmpca.Rmd")

I first fit a glmpca on pbmc3k data, then fit a flash on \(L_{glmpca}F'_{glmpca}\).

How does it compare with flash fit, and glmpca fit?

library(fastTopics)
library(Matrix)
library(stm)
data(pbmc_facs)
flash_glmpca = readRDS('/project2/mstephens/dongyue/poisson_mf/pbmc3k/flash_glmpca.rds')
source('code/poisson_STM/plot_factors.R')
plot.factors(flash_glmpca,pbmc_facs$samples$subpop,title='flash on glmpca')

source('code/poisson_STM/plot_factors_general.R')
fit_glmpca_poi = readRDS('/project2/mstephens/dongyue/poisson_mf/pbmc3k/glmpca_pbmc3k_poi.rds')
plot.factors.general(fit_glmpca_poi$loadings,pbmc_facs$samples$subpop,title='glmpca poi')

What’s the gene expression-variance relationship?

plot(colSums(pbmc_facs$counts/rowSums(pbmc_facs$counts)),flash_glmpca$residuals.sd^2,xlab='gene expression',ylab='gene variance',main='pbmc3k, flash on glmpca',pch='.',cex=2,col='grey40')

This gene expression-variance relationship looks very similar to the splitting method results, see here.


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] ggplot2_3.4.0      stm_1.3.1          Matrix_1.5-3       fastTopics_0.6-142
[5] workflowr_1.6.2   

loaded via a namespace (and not attached):
  [1] mcmc_0.9-7         bitops_1.0-7       matrixStats_0.59.0
  [4] fs_1.5.0           progress_1.2.2     httr_1.4.4        
  [7] rprojroot_2.0.2    tools_4.1.0        bslib_0.2.5.1     
 [10] utf8_1.2.2         R6_2.5.1           irlba_2.3.5.1     
 [13] uwot_0.1.14        DBI_1.1.1          lazyeval_0.2.2    
 [16] colorspace_2.0-3   withr_2.5.0        wavethresh_4.7.2  
 [19] tidyselect_1.2.0   prettyunits_1.1.1  ebpm_0.0.1.3      
 [22] compiler_4.1.0     git2r_0.28.0       cli_3.5.0         
 [25] quantreg_5.94      SparseM_1.81       plotly_4.10.1     
 [28] labeling_0.4.2     horseshoe_0.2.0    sass_0.4.0        
 [31] smashrgen_1.1.4    caTools_1.18.2     flashier_0.2.34   
 [34] scales_1.2.1       SQUAREM_2021.1     quadprog_1.5-8    
 [37] pbapply_1.6-0      mixsqp_0.3-48      stringr_1.4.0     
 [40] digest_0.6.30      rmarkdown_2.9      deconvolveR_1.2-1 
 [43] MCMCpack_1.6-3     vebpm_0.4.0        pkgconfig_2.0.3   
 [46] htmltools_0.5.3    highr_0.9          fastmap_1.1.0     
 [49] invgamma_1.1       htmlwidgets_1.5.4  rlang_1.0.6       
 [52] rstudioapi_0.13    farver_2.1.1       jquerylib_0.1.4   
 [55] generics_0.1.3     jsonlite_1.8.3     dplyr_1.0.10      
 [58] magrittr_2.0.3     smashr_1.3-6       Rcpp_1.0.9        
 [61] munsell_0.5.0      fansi_1.0.3        RcppZiggurat_0.1.6
 [64] lifecycle_1.0.3    stringi_1.6.2      whisker_0.4       
 [67] yaml_2.3.6         MASS_7.3-54        plyr_1.8.6        
 [70] Rtsne_0.16         grid_4.1.0         parallel_4.1.0    
 [73] promises_1.2.0.1   ggrepel_0.9.2      crayon_1.5.2      
 [76] lattice_0.20-44    cowplot_1.1.1      splines_4.1.0     
 [79] hms_1.1.2          knitr_1.33         pillar_1.8.1      
 [82] softImpute_1.4-1   reshape2_1.4.4     glue_1.6.2        
 [85] evaluate_0.14      trust_0.1-8        data.table_1.14.6 
 [88] RcppParallel_5.1.5 nloptr_1.2.2.2     vctrs_0.5.1       
 [91] httpuv_1.6.1       MatrixModels_0.5-1 gtable_0.3.1      
 [94] purrr_0.3.5        ebnm_1.0-11        tidyr_1.2.1       
 [97] assertthat_0.2.1   ashr_2.2-54        xfun_0.24         
[100] Rfast_2.0.6        NNLM_0.4.4         coda_0.19-4       
[103] later_1.3.0        survival_3.2-11    viridisLite_0.4.1 
[106] glmpca_0.2.0       truncnorm_1.0-8    tibble_3.1.8      
[109] ellipsis_0.3.2