Last updated: 2022-12-06
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Rmd | 39f087b | DongyueXie | 2022-12-06 | wflow_publish("analysis/run_PMF_on_pbmc.Rmd") |
I take the pbmc data from fastTopics
package, and run
splitting PMF on the dataset.
library(fastTopics)
library(Matrix)
library(stm)
Attaching package: 'stm'
The following object is masked from 'package:fastTopics':
poisson2multinom
data(pbmc_facs)
counts <- pbmc_facs$counts
table(pbmc_facs$samples$subpop)
B cell CD14+ CD34+ NK cell T cell
767 163 687 673 1484
## use only B cell and NK cell
cells = pbmc_facs$samples$subpop%in%c('B cell', 'NK cell')
Y = counts[cells,]
dim(Y)
[1] 1440 16791
# filter out genes that has few expressions(3% cells)
genes = (colSums(Y>0) > 0.03*dim(Y)[1])
Y = Y[,genes]
# make sure there is no zero col and row
sum(rowSums(Y)==0)
[1] 0
sum(colSums(Y)==0)
[1] 0
dim(Y)
[1] 1440 4015
S = tcrossprod(c(rowSums(Y)),c(colSums(Y)))/sum(Y)
Y = as.matrix(Y)
There are 5 main cell types and 16791 genes.
To start with, I considered two cell types, B cell, and NK cell. Then I filtered out genes that have no expression in more than 3% cells. The gene filtering is mainly for reducing the data size and the running time.
The final dataset is of dimension 1440 cells by 4015 genes. I set the
scaling factors as sij=yi+y+jy++. For comparison, I also fit
flash
on transformed count data, as ˜yij=log(1+yijsijaj0.5) where aj=median(s⋅j). This
transformation is derived from ˜yij=log(yijsij+0.5aj). However
flash
was not able to terminate at Kmax=50. The splitting PMF estiamted
K=3. In addition, the svd seems to
also support K=3, as the scree plot
show 3 points clearly separated from the rest.
fit = readRDS('output/poisson_MF_simulation/fit_pbmc_2cells.rds')
fit_flashier = readRDS('output/poisson_MF_simulation/fit_flashier_pbmc_2cells.rds')
fit_svd = readRDS('output/poisson_MF_simulation/fit_svd_pbmc_2cells.rds')
plot(fit_svd$d)
fit$run_time
Time difference of 1.768594 hours
plot(fit$eblo_trace,type='l')
The PMF algorithm converges after 3000 iterations and 106mins.
fit$fit_flash$n.factors
[1] 3
plot(fit$sigma2,ylab = 'sigma2',xlab='gene',col='grey50')
plot(colSums(Y/c(rowSums(Y)))/dim(Y)[1],fit$sigma2,xlab='gene mean count(after library size adjustment)')
plot(colSums(Y==0)/dim(Y)[1],fit$sigma2,xlab='sparsity')
Plot of factors:
par(mfrow=c(3,1))
plot(fit$fit_flash$F.pm[,1],xlab='gene',ylab='first factor')
plot(fit$fit_flash$F.pm[,2],xlab='gene',ylab='second factor')
plot(fit$fit_flash$F.pm[,3],xlab='gene',ylab='third factor')
Plot of Loading:
cell_names = as.character(pbmc_facs$samples$subpop[cells])
color_cell = replace(cell_names,which(cell_names=='B cell'),'red')
color_cell = replace(color_cell,which(cell_names=='NK cell'),'blue')
par(mfrow=c(3,1))
plot(fit$fit_flash$L.pm[,1],xlab='cells',ylab='first loading',col=color_cell)
plot(fit$fit_flash$L.pm[,2],xlab='cells',ylab='second loading',col=color_cell)
plot(fit$fit_flash$L.pm[,3],xlab='cells',ylab='third factor',col=color_cell)
Plot of first two loadings:
par(mfrow=c(1,1))
plot(fit$fit_flash$L.pm[,1],fit$fit_flash$L.pm[,2],col=color_cell,xlab='first loading',ylab='second loading')
legend(c('topright'),c('B cell','NK cell'),col=c('red','blue'),pch=c(1,1))
Plot of second two loadings:
par(mfrow=c(1,1))
plot(fit$fit_flash$L.pm[,2],fit$fit_flash$L.pm[,3],col=color_cell,xlab='second loading',ylab='third loading')
legend(c('topleft'),c('B cell','NK cell'),col=c('red','blue'),pch=c(1,1))
Use Jason’s method for visualizing loadings:
source('code/poisson_STM/plot_factors.R')
plot.factors(fit$fit_flash,cell_names,title='splitting PMF')
plot.factors(fit_flashier,cell_names,kset = c(1:10),title='flashier')
sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.6 stm_1.0.9 Matrix_1.5-1 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 rprojroot_2.0.3
[7] fs_1.5.2 rstudioapi_0.14 farver_2.1.1
[10] MatrixModels_0.5-1 ggrepel_0.9.2 fansi_1.0.3
[13] splines_4.2.1 cachem_1.0.6 rootSolve_1.8.2.3
[16] knitr_1.40 jsonlite_1.8.2 nloptr_2.0.3
[19] mcmc_0.9-7 ashr_2.2-54 uwot_0.1.14
[22] compiler_4.2.1 httr_1.4.4 assertthat_0.2.1
[25] fastmap_1.1.0 lazyeval_0.2.2 cli_3.4.1
[28] later_1.3.0 htmltools_0.5.3 quantreg_5.94
[31] prettyunits_1.1.1 tools_4.2.1 coda_0.19-4
[34] gtable_0.3.1 glue_1.6.2 reshape2_1.4.4
[37] dplyr_1.0.10 Rcpp_1.0.9 softImpute_1.4-1
[40] jquerylib_0.1.4 vctrs_0.4.2 wavethresh_4.7.2
[43] xfun_0.33 stringr_1.4.1 ps_1.7.1
[46] trust_0.1-8 lifecycle_1.0.3 irlba_2.3.5.1
[49] NNLM_0.4.4 nleqslv_3.3.3 getPass_0.2-2
[52] MASS_7.3-58 scales_1.2.1 hms_1.1.2
[55] promises_1.2.0.1 parallel_4.2.1 SparseM_1.81
[58] yaml_2.3.5 pbapply_1.6-0 sass_0.4.2
[61] stringi_1.7.8 SQUAREM_2021.1 highr_0.9
[64] deconvolveR_1.2-1 caTools_1.18.2 truncnorm_1.0-8
[67] horseshoe_0.2.0 rlang_1.0.6 pkgconfig_2.0.3
[70] matrixStats_0.62.0 bitops_1.0-7 ebnm_1.0-9
[73] evaluate_0.17 lattice_0.20-45 invgamma_1.1
[76] purrr_0.3.5 htmlwidgets_1.5.4 labeling_0.4.2
[79] cowplot_1.1.1 processx_3.7.0 tidyselect_1.2.0
[82] plyr_1.8.7 magrittr_2.0.3 R6_2.5.1
[85] generics_0.1.3 DBI_1.1.3 pillar_1.8.1
[88] whisker_0.4 withr_2.5.0 survival_3.4-0
[91] mixsqp_0.3-48 tibble_3.1.8 crayon_1.5.2
[94] utf8_1.2.2 plotly_4.10.1 rmarkdown_2.17
[97] progress_1.2.2 grid_4.2.1 data.table_1.14.6
[100] callr_3.7.2 git2r_0.30.1 digest_0.6.29
[103] vebpm_0.3.3 tidyr_1.2.1 httpuv_1.6.6
[106] MCMCpack_1.6-3 RcppParallel_5.1.5 munsell_0.5.0
[109] viridisLite_0.4.1 flashier_0.2.34 bslib_0.4.0
[112] quadprog_1.5-8