Last updated: 2023-03-05

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Rmd 0734ec1 DongyueXie 2023-03-05 wflow_publish("analysis/simu_study_pbmc_truevarlarge.Rmd")

Introduction

So this is a revised version of “Large true variance, large init variance” in a previous simulation study. I added revised version of ebnmf, and added the nmf with squared loss.

For visualization purpose, I set the number of K for ebnmf to be the true one - 10. Otherwise it will keep adding new factors till Kmax.(say 20)

res = readRDS('/project2/mstephens/dongyue/poisson_mf/pbmc3k_simulation/simu_pbmc_fasttopics_large_true_var_large_init_var.rds')
source('code/poisson_STM/plot_factors.R')
source('code/poisson_STM/plot_factors_general.R')
source('code/poisson_STM/structure_plot.R')
source('code/poisson_STM/get_loadings_order.R')
loadings_order = get_loadings_order(res$sim_data$Loading,res$sim_data$Factor,
                                    grouping = pbmc_facs$samples$subpop,n_samples = 5000)
Perplexity automatically changed to 53 because the original setting of 100 was too large for the number of samples (163)
plot0=structure_plot_general(res$sim_data$Loading,res$sim_data$Factor,
                             grouping =pbmc_facs$samples$subpop,title = 'True',print_plot = F,
                             loadings_order = loadings_order)
Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"
order_topic = function(ref,est){
  n = nrow(ref)
  p = ncol(est)
  ref = apply(ref,2,function(z){z/norm(z,'2')})
  est = apply(est,2,function(z){z/norm(z,'2')})
  dist_mat = matrix(nrow=p,ncol=p)
  for(i in 1:p){
    for(j in 1:p){
      dist_mat[i,j] = sqrt(mean((est[,i]-ref[,j])^2))
    }
  }
  order_output = c()
  for(i in 1:p){
    order_output[i] = which.min(dist_mat[,i])
    rm_idx = c()
    while(i > 1 & order_output[i]%in%order_output[-i]){
      rm_idx = c(rm_idx,order_output[i])
      temp = dist_mat[,i]
      temp[rm_idx] = Inf
      order_output[i] = which.min(temp)
    }
  }
  order_output
}
for(i in c(1,3,4,5)){
  order_nmf = order_topic(res$sim_data$Loading[,-c(1,2)],res$output[[i]]$fitted_model$nmf$W[,-1])
  plot1 = structure_plot_general((res$output[[i]]$fitted_model$nmf$W[,-1])[,order_nmf],
                             (t(res$output[[i]]$fitted_model$nmf$H[-1,]))[,order_nmf],
                             grouping=pbmc_facs$samples$subpop,
                             title='log transformation+NMF(squared loss)',
                             print_plot = F,
                             loadings_order=loadings_order,
                             remove_l0f0 = F)
  order_ebpmf = order_topic(res$sim_data$Loading,res$output[[i]]$fitted_model$ebpmf$fit_flash$L.pm)
  plot2 = structure_plot_general(res$output[[i]]$fitted_model$ebpmf$fit_flash$L.pm[,order_ebpmf],
                                 res$output[[i]]$fitted_model$ebpmf$fit_flash$F.pm[,order_ebpmf],
                                 grouping =pbmc_facs$samples$subpop,
                                 title='EBPMF',
                                 print_plot = F,
                                 loadings_order=loadings_order)
  order_flash = order_topic(res$sim_data$Loading[,-c(1,2)],res$output[[i]]$fitted_model$flash$L.pm[,-1])
  plot3 = structure_plot_general((res$output[[i]]$fitted_model$flash$L.pm[,-1])[,order_flash],
                                 (res$output[[i]]$fitted_model$flash$L.pm[,-1])[,order_flash],
                                 grouping =pbmc_facs$samples$subpop,
                                 title='log transformation+EBNMF',
                                 remove_l0f0 = F,
                                 print_plot = F,
                                 loadings_order=loadings_order)
  grid.arrange(plot0,plot1,plot3, plot2,nrow=4)
}
Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"

Warning in structure_plot(fit_list, grouping = grouping, loadings_order =
loadings_order, : Input argument "n" is ignored when "loadings_order" is not
"embed"


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] gridExtra_2.3      fastTopics_0.6-142 ggplot2_3.4.1      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_1.4.5        
  [7] rprojroot_2.0.2    tools_4.1.0        bslib_0.2.5.1     
 [10] utf8_1.2.3         R6_2.5.1           irlba_2.3.5.1     
 [13] uwot_0.1.14        lazyeval_0.2.2     colorspace_2.1-0  
 [16] withr_2.5.0        wavethresh_4.7.2   prettyunits_1.1.1 
 [19] tidyselect_1.2.0   ebpm_0.0.1.3       compiler_4.1.0    
 [22] git2r_0.28.0       cli_3.6.0          quantreg_5.94     
 [25] SparseM_1.81       plotly_4.10.1      labeling_0.4.2    
 [28] horseshoe_0.2.0    sass_0.4.0         smashrgen_1.1.4   
 [31] caTools_1.18.2     flashier_0.2.34    scales_1.2.1      
 [34] SQUAREM_2021.1     quadprog_1.5-8     pbapply_1.7-0     
 [37] mixsqp_0.3-48      stringr_1.5.0      digest_0.6.31     
 [40] rmarkdown_2.9      MCMCpack_1.6-3     deconvolveR_1.2-1 
 [43] vebpm_0.4.4        pkgconfig_2.0.3    htmltools_0.5.4   
 [46] ebpmf_2.0.8        highr_0.9          fastmap_1.1.0     
 [49] invgamma_1.1       htmlwidgets_1.6.1  rlang_1.0.6       
 [52] rstudioapi_0.13    farver_2.1.1       jquerylib_0.1.4   
 [55] generics_0.1.3     jsonlite_1.8.4     dplyr_1.1.0       
 [58] magrittr_2.0.3     smashr_1.3-6       Matrix_1.5-3      
 [61] Rcpp_1.0.10        munsell_0.5.0      fansi_1.0.4       
 [64] lifecycle_1.0.3    RcppZiggurat_0.1.6 stringi_1.6.2     
 [67] whisker_0.4        yaml_2.3.7         MASS_7.3-54       
 [70] Rtsne_0.16         grid_4.1.0         parallel_4.1.0    
 [73] promises_1.2.0.1   ggrepel_0.9.3      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   glue_1.6.2         evaluate_0.14     
 [85] trust_0.1-8        data.table_1.14.8  RcppParallel_5.1.7
 [88] vctrs_0.5.2        nloptr_1.2.2.2     httpuv_1.6.1      
 [91] MatrixModels_0.5-1 gtable_0.3.1       purrr_1.0.1       
 [94] ebnm_1.0-11        tidyr_1.3.0        ashr_2.2-54       
 [97] xfun_0.24          Rfast_2.0.7        NNLM_0.4.4        
[100] coda_0.19-4        later_1.3.0        survival_3.2-11   
[103] viridisLite_0.4.1  truncnorm_1.0-8    tibble_3.1.8      
[106] ellipsis_0.3.2