Automatic Grouping of Factors: Pancreas

Last updated: 2025-02-12

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Knit directory: single-cell-jamboree/analysis/

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File	Version	Author	Date	Message
Rmd	36a6139	Ziang Zhang	2025-02-12	workflowr::wflow_publish("pancreas_group.rmd")
html	cb4a691	Ziang Zhang	2025-02-12	Build site.
Rmd	69af116	Ziang Zhang	2025-02-12	workflowr::wflow_publish("pancreas_group.rmd")

Let’s try some automatic grouping of factors for the pancreas dataset, based on the grouping information provided in the metadata.

library(Matrix)
library(fastTopics)

Warning: package 'fastTopics' was built under R version 4.3.3

library(ggplot2)
library(cowplot)
set.seed(1)
subsample_cell_types <- function (x, n = 1000) {
  cells <- NULL
  groups <- levels(x)
  for (g in groups) {
    i  <-  which(x == g)
    n0 <- min(n,length(i))
    i  <- sample(i,n0)
    cells <- c(cells,i)
  }
  return(sort(cells))
}
load("../data/pancreas.RData")
load("../output/pancreas_factors.RData")
load("../output/pancreas_factors2.RData")

source("../code/group_factors.R")

flashier NMF

cells <- subsample_cell_types(sample_info$celltype,n = 500)
L <- fl_nmf_ldf$L
k <- ncol(L)
colnames(L) <- paste0("k",1:k)

Take a look at the elbow plot:

ordered_df_tech <- ANOVA_factors(L[cells,], sample_info$tech[cells], stats = "R2")
ordered_df_celltype <- ANOVA_factors(L[cells,], sample_info$celltype[cells], stats = "R2")
par(mfrow = c(2,1))
plot(ordered_df_tech$rank, ordered_df_tech$stats, type = "o", xlab = "Rank", ylab = "R2", main = "batchtypes", ylim = c(0,1.1))
text(ordered_df_tech$rank, ordered_df_tech$stats, labels = ordered_df_tech$factor, pos = 3, cex = 0.6)
abline(h = c(0.3,0.5,0.7), col = "red", lty = 2)
plot(ordered_df_celltype$rank, ordered_df_celltype$stats, type = "o", xlab = "Rank", ylab = "R2", main = "celltypes", ylim = c(0,1.1))
text(ordered_df_celltype$rank, ordered_df_celltype$stats, labels = ordered_df_celltype$factor, pos = 3, cex = 0.6)
abline(h = c(0.3,0.5,0.7), col = "red", lty = 2)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

par(mfrow = c(1,1))

Cut off the factors with $R^2$ less than 0.7:

p1 <- structure_plot_group(L = L[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.7, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = L[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.7, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

Cut off the factors with $R^2$ less than 0.5:

p1 <- structure_plot_group(L = L[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.5, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = L[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.5, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

Try reduce to the cut-off to 0.3:

p1 <- structure_plot_group(L = L[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.3, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = L[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.3, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

NMF (NNLM)

W <- nmf$W
k <- ncol(W)
d <- apply(W,2,max)
scale_cols <- function (A, b)
  t(t(A) * b)
W <- scale_cols(W,1/d)
colnames(W) <- paste0("k",1:k)

Take a look at the elbow plot:

ordered_df_tech <- ANOVA_factors(W[cells,], sample_info$tech[cells], stats = "R2")
ordered_df_celltype <- ANOVA_factors(W[cells,], sample_info$celltype[cells], stats = "R2")
par(mfrow = c(2,1))
plot(ordered_df_tech$rank, ordered_df_tech$stats, type = "o", xlab = "Rank", ylab = "R2", main = "batchtypes", ylim = c(0,1.1))
text(ordered_df_tech$rank, ordered_df_tech$stats, labels = ordered_df_tech$factor, pos = 3, cex = 0.6)
abline(h = c(0.3,0.5,0.7), col = "red", lty = 2)
plot(ordered_df_celltype$rank, ordered_df_celltype$stats, type = "o", xlab = "Rank", ylab = "R2", main = "celltypes", ylim = c(0,1.1))
text(ordered_df_celltype$rank, ordered_df_celltype$stats, labels = ordered_df_celltype$factor, pos = 3, cex = 0.6)
abline(h = c(0.3,0.5,0.7), col = "red", lty = 2)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

par(mfrow = c(1,1))

Cut off the factors with $R^2$ less than 0.7:

p1 <- structure_plot_group(L = W[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.7, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = W[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.7, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

Cut off the factors with $R^2$ less than 0.5:

p1 <- structure_plot_group(L = W[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.5, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = W[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.5, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

Cut off the factors with $R^2$ less than 0.3:

p1 <- structure_plot_group(L = W[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.3, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = W[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.3, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

Topic model (fastTopics)

L <- poisson2multinom(pnmf)$L

Take a look at the elbow plot:

ordered_df_tech <- ANOVA_factors(L[cells,], sample_info$tech[cells], stats = "R2")
ordered_df_celltype <- ANOVA_factors(L[cells,], sample_info$celltype[cells], stats = "R2")
par(mfrow = c(2,1))
plot(ordered_df_tech$rank, ordered_df_tech$stats, type = "o", xlab = "Rank", ylab = "R2", main = "batchtypes", ylim = c(0,1.1))
text(ordered_df_tech$rank, ordered_df_tech$stats, labels = ordered_df_tech$factor, pos = 3, cex = 0.6)
abline(h = c(0.3,0.5,0.7), col = "red", lty = 2)
plot(ordered_df_celltype$rank, ordered_df_celltype$stats, type = "o", xlab = "Rank", ylab = "R2", main = "celltypes", ylim = c(0,1.1))
text(ordered_df_celltype$rank, ordered_df_celltype$stats, labels = ordered_df_celltype$factor, pos = 3, cex = 0.6)
abline(h = c(0.3,0.5,0.7), col = "red", lty = 2)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

par(mfrow = c(1,1))

Cut off the factors with $R^2$ less than 0.7:

p1 <- structure_plot_group(L = L[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.7, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = L[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.7, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

Cut off the factors with $R^2$ less than 0.5:

p1 <- structure_plot_group(L = L[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.5, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = L[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.5, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

Cut off the factors with $R^2$ less than 0.3:

p1 <- structure_plot_group(L = L[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.3, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = L[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.3, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

Flashier semi-NMF

L <- fl_snmf_ldf$L
x <- apply(L,2,function (x) quantile(x,0.995))
L <- scale_cols(L,1/x)
colnames(L) <- paste0("k",1:k)

Take a look at the elbow plot:

ordered_df_tech <- ANOVA_factors(W[cells,], sample_info$tech[cells], stats = "R2")
ordered_df_celltype <- ANOVA_factors(W[cells,], sample_info$celltype[cells], stats = "R2")
par(mfrow = c(2,1))
plot(ordered_df_tech$rank, ordered_df_tech$stats, type = "o", xlab = "Rank", ylab = "R2", main = "batchtypes", ylim = c(0,1.1))
text(ordered_df_tech$rank, ordered_df_tech$stats, labels = ordered_df_tech$factor, pos = 3, cex = 0.6)
abline(h = c(0.3,0.5,0.7), col = "red", lty = 2)
plot(ordered_df_celltype$rank, ordered_df_celltype$stats, type = "o", xlab = "Rank", ylab = "R2", main = "celltypes", ylim = c(0,1.1))
text(ordered_df_celltype$rank, ordered_df_celltype$stats, labels = ordered_df_celltype$factor, pos = 3, cex = 0.6)
abline(h = c(0.3,0.5,0.7), col = "red", lty = 2)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

par(mfrow = c(1,1))

Cut off the factors with $R^2$ less than 0.7:

p1 <- structure_plot_group(L = L[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.7, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = L[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.7, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

Cut off the factors with $R^2$ less than 0.5:

p1 <- structure_plot_group(L = L[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.5, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = L[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.5, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

Cut off the factors with $R^2$ less than 0.3:

p1 <- structure_plot_group(L = L[cells,], group_vec = sample_info$tech[cells],
                           cutoff = 0.3, stats = "R2", group_name = "data-set")
p2 <- structure_plot_group(L = L[cells,], group_vec = sample_info$celltype[cells],
                           cutoff = 0.3, stats = "R2", group_name = "cell-type")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version	Author	Date
cb4a691	Ziang Zhang	2025-02-12

sessionInfo()

R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.7.4

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] cowplot_1.1.3      ggplot2_3.5.1      fastTopics_0.6-192 Matrix_1.6-4      

loaded via a namespace (and not attached):
 [1] gtable_0.3.6        xfun_0.48           bslib_0.8.0        
 [4] htmlwidgets_1.6.4   ggrepel_0.9.6       lattice_0.22-6     
 [7] quadprog_1.5-8      vctrs_0.6.5         tools_4.3.1        
[10] generics_0.1.3      parallel_4.3.1      tibble_3.2.1       
[13] fansi_1.0.6         highr_0.11          pkgconfig_2.0.3    
[16] data.table_1.16.2   SQUAREM_2021.1      RcppParallel_5.1.9 
[19] lifecycle_1.0.4     truncnorm_1.0-9     farver_2.1.2       
[22] compiler_4.3.1      stringr_1.5.1       git2r_0.33.0       
[25] progress_1.2.3      munsell_0.5.1       RhpcBLASctl_0.23-42
[28] httpuv_1.6.15       htmltools_0.5.8.1   sass_0.4.9         
[31] yaml_2.3.10         lazyeval_0.2.2      plotly_4.10.4      
[34] crayon_1.5.3        later_1.3.2         pillar_1.9.0       
[37] jquerylib_0.1.4     whisker_0.4.1       tidyr_1.3.1        
[40] uwot_0.1.16         cachem_1.1.0        gtools_3.9.5       
[43] tidyselect_1.2.1    digest_0.6.37       Rtsne_0.17         
[46] stringi_1.8.4       dplyr_1.1.4         purrr_1.0.2        
[49] ashr_2.2-66         labeling_0.4.3      rprojroot_2.0.4    
[52] fastmap_1.2.0       grid_4.3.1          colorspace_2.1-1   
[55] cli_3.6.3           invgamma_1.1        magrittr_2.0.3     
[58] utf8_1.2.4          withr_3.0.2         prettyunits_1.2.0  
[61] scales_1.3.0        promises_1.3.0      rmarkdown_2.28     
[64] httr_1.4.7          workflowr_1.7.1     hms_1.1.3          
[67] pbapply_1.7-2       evaluate_1.0.1      knitr_1.48         
[70] viridisLite_0.4.2   irlba_2.3.5.1       rlang_1.1.4        
[73] Rcpp_1.0.13-1       mixsqp_0.3-54       glue_1.8.0         
[76] rstudioapi_0.16.0   jsonlite_1.8.9      R6_2.5.1           
[79] fs_1.6.4

Automatic Grouping of Factors: Pancreas

Ziang Zhang

2025-02-12

flashier NMF

NMF (NNLM)

Topic model (fastTopics)

Flashier semi-NMF