Last updated: 2023-01-05
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Knit directory: gsmash/
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File | Version | Author | Date | Message |
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Rmd | 25e29e2 | DongyueXie | 2023-01-05 | wflow_publish("analysis/run_PMF_on_pbmc_more_iterations.Rmd") |
I have a previous result run the very initial version of the splitting PMF. Now I revise the code and re-run the model. THe main diff is a. run vga 1 iter every big iteration; b. 1000 iterations; c. add 1 dimension each add_greedy attempt.
I set the scaling factors as sij=yi+y+jy++. For comparison, I also fit
flash
on transformed count data, as ˜yij=log(1+yijsia0.5) where si=∑jyij, a=median(si).
library(fastTopics)
library(Matrix)
library(stm)
data(pbmc_facs)
counts <- pbmc_facs$counts
table(pbmc_facs$samples$subpop)
B cell CD14+ CD34+ NK cell T cell
767 163 687 673 1484
fit = readRDS('/project2/mstephens/dongyue/poisson_mf/pbmc3k/pbmc_splitting_point_normal_vga1.rds')
plot(fit$K_trace, ylab='K',xlab='iterations')
plot(fit$elbo_trace,ylab='elbo',xlab='iterations',type='l')
plot(colSums(counts/c(rowSums(counts)))/dim(counts)[1],fit$sigma2,xlab='gene mean count(after library size adjustment)')
plot(colSums(counts==0)/dim(counts)[1],fit$sigma2,xlab='sparsity')
source('code/poisson_STM/plot_factors.R')
cell_names = as.character(pbmc_facs$samples$subpop)
plot.factors(fit$fit_flash,cell_names,title='splitting PMF')
fit_flashier = readRDS('/project2/mstephens/dongyue/poisson_mf/pbmc3k/flash_pbmc3k.rds')
plot.factors(fit_flashier,cell_names,title='flashier')
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$loadings,cell_names,title='glmpca poi')
fit_glmpca_nb = readRDS('/project2/mstephens/dongyue/poisson_mf/pbmc3k/glmpca_pbmc3k_nb.rds')
plot.factors.general(fit_glmpca_nb$loadings,cell_names,title='glmpca nb')
fit$run_time
Time difference of 10.15867 hours
lapply(fit$run_time_break_down,mean)
$run_time_vga_init
Time difference of 28.89131 secs
$run_time_flash_init
Time difference of 71.49468 secs
$run_time_vga
[1] 11.04436
$run_time_flash_init_factor
[1] 4.846896
$run_time_flash_greedy
[1] 0.4588499
$run_time_flash_backfitting
[1] 13.73861
$run_time_flash_nullcheck
[1] 1.401617
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_1.2.6 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_1.1.1 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_0.3.8 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