Last updated: 2024-12-18

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Here we build on the “another look” analysis and explore tree-based representations of the pancreas data.

First, load the packages needed for this analysis.

library(Matrix)
library(fastTopics)
library(ggplot2)
library(cowplot)
library(ape)

Set the seed for reproducibility.

set.seed(1)

CEL-Seq2 data

Load the CEL-Seq2 pancreas data and the outputs generated by running the compute_pancreas_celseq2_factors.R script.

load("../data/pancreas.RData")
load("../output/pancreas_celseq2_factors.RData")
i           <- which(sample_info$tech == "celseq2")
sample_info <- sample_info[i,]
counts      <- counts[i,]
sample_info <- transform(sample_info,celltype = factor(celltype))

Topic model (fastTopics)

Here is the topic model (with 9 topics):

celltype <- sample_info$celltype
celltype <-
 factor(celltype,
        c("acinar","ductal","activated_stellate","quiescent_stellate",
          "endothelial","macrophage","mast","schwann","alpha","beta",
          "delta","gamma","epsilon"))
L <- poisson2multinom(pnmf)$L
structure_plot(L,grouping = celltype,gap = 20,perplexity = 70,n = Inf)

Fit a tree to the topics using the neighbor-joining tree algorithm:

par(mar = c(1,1,1,1))
k <- 9
F <- poisson2multinom(pnmf)$F
D <- as.matrix(dist(t(F)))
rownames(D) <- paste0("k",1:k)
colnames(D) <- paste0("k",1:k)
tr <- nj(D)
tr$edge.length <- abs(tr$edge.length)
plot(tr,cex = 0.75,edge.width = 1.5,adj = 5,font = 1)

Flashier NMF

Here is the empirical Bayes NMF fit (with 9 factors):

L <- fl_nmf_ldf$L
k <- ncol(L)
colnames(L) <- paste0("k",1:k)
structure_plot(L[,-1],grouping = celltype,gap = 20,perplexity = 70,n = Inf) +
  labs(y = "membership",fill = "factor",color = "factor")

Fit a tree to these factors using the neighbor-joining tree algorithm:

par(mar = c(1,1,1,1))
k  <- 9
ks <- 2:9
D  <- as.matrix(dist(t(fl_nmf_ldf$F)))
rownames(D) <- paste0("k",1:k)
colnames(D) <- paste0("k",1:k)
tr <- nj(D[ks,ks])
tr$edge.length <- abs(tr$edge.length)
plot(tr,cex = 0.75,edge.width = 1.5,adj = 5,font = 1)

NMF (NNLM)

Let’s now have a look at the “vanilla” NMF (produced by the NNLM package). As before, this NMF has 9 factors.

scale_cols <- function (A, b)
  t(t(A) * b)
W <- nmf$W
k <- ncol(W)
d <- apply(W,2,max)
W <- scale_cols(W,1/d)
colnames(W) <- paste0("k",1:k)
structure_plot(W,grouping = celltype,gap = 20,perplexity = 70,n = Inf) +
  labs(y = "membership",fill = "factor",color = "factor")

Fit a tree to these factors using the neighbor-joining tree algorithm:

par(mar = c(1,1,1,1))
H <- nmf$H
d <- apply(H,2,max)
H <- scale_cols(H,1/d)
D <- as.matrix(dist(H))
rownames(D) <- paste0("k",1:k)
colnames(D) <- paste0("k",1:k)
tr <- nj(D)
tr$edge.length <- abs(tr$edge.length)
plot(tr,cex = 0.75,edge.width = 1.5,adj = 5,font = 1)

Smart-seq2 data

Load the Smart-Seq2 data and the outputs generated from running the compute_pancreas_smartseq2_factors.R script.

load("../data/pancreas.RData")
load("../output/pancreas_smartseq2_factors.RData")
i           <- which(sample_info$tech == "smartseq2")
sample_info <- sample_info[i,]
counts      <- counts[i,]
sample_info <- transform(sample_info,celltype = factor(celltype))
celltype <- sample_info$celltype
celltype <-
 factor(celltype,
        c("acinar","ductal","activated_stellate","quiescent_stellate",
          "endothelial","macrophage","mast","schwann","alpha",
          "beta","delta","gamma","epsilon"))

Topic model (fastTopics)

Here is the topic model with 9 topics:

L <- poisson2multinom(pnmf)$L
structure_plot(L,grouping = celltype,gap = 20,perplexity = 70,n = Inf)

Fit a tree to the topics using the neighbor-joining tree algorithm:

par(mar = c(1,1,1,1))
k <- 9
F <- poisson2multinom(pnmf)$F
D <- as.matrix(dist(t(F)))
rownames(D) <- paste0("k",1:k)
colnames(D) <- paste0("k",1:k)
tr <- nj(D)
tr$edge.length <- abs(tr$edge.length)
plot(tr,cex = 0.75,edge.width = 1.5,adj = 5,font = 1)

Flashier NMF

Here is the empirical Bayes NMF fit (with 9 factors):

L <- fl_nmf_ldf$L
k <- ncol(L)
colnames(L) <- paste0("k",1:k)
celltype_factors  <- 2:7
other_factors <- c(1,8,9)
p1 <- structure_plot(L[,celltype_factors],grouping = celltype,gap = 20,
                     perplexity = 70,n = Inf) +
  labs(y = "membership",fill = "factor",color = "factor",
       title = "cell-type factors")
other_colors <- c("#66c2a5","#fc8d62","#8da0cb")
p2 <- structure_plot(L[,other_factors],grouping = celltype,gap = 20,
                     perplexity = 70,n = Inf) +
  labs(y = "membership",fill = "factor",color = "factor",
       title = "other factors") +
  scale_color_manual(values = other_colors) +
  scale_fill_manual(values = other_colors)
plot_grid(p1,p2,nrow = 2,ncol = 1)

Fit a tree to the cell-type factors using the neighbor-joining tree algorithm:

par(mar = c(1,1,1,1))
k  <- 9
D  <- as.matrix(dist(t(fl_nmf_ldf$F)))
rownames(D) <- paste0("k",1:k)
colnames(D) <- paste0("k",1:k)
tr <- nj(D[celltype_factors,celltype_factors])
tr$edge.length <- abs(tr$edge.length)
plot(tr,cex = 0.75,edge.width = 1.5,adj = 5,font = 1)

NMF (NNLM)

This is the NMF decomposition (with 9 factors):

scale_cols <- function (A, b)
  t(t(A) * b)
W <- nmf$W
k <- ncol(W)
d <- apply(W,2,max)
W <- scale_cols(W,1/d)
colnames(W) <- paste0("k",1:k)
celltype_factors  <- c(3:6,8,9)
other_factors <- c(1,2,7)
p1 <- structure_plot(W[,celltype_factors],grouping = celltype,
                     gap = 20,perplexity = 70,n = Inf) +
  labs(y = "membership",fill = "factor",color = "factor",
       title = "cell-type factors")
p2 <- structure_plot(W[,other_factors],grouping = celltype,
                     gap = 20,perplexity = 70,n = Inf) +
  scale_color_manual(values = other_colors) +
  scale_fill_manual(values = other_colors) +
  labs(y = "membership",fill = "factor",color = "factor",
       title = "other factors")
plot_grid(p1,p2,nrow = 2,ncol = 1)

Fit a tree to the cell-type factors using the neighbor-joining tree algorithm:

par(mar = c(1,1,1,1))
H <- nmf$H
d <- apply(H,2,max)
H <- scale_cols(H,1/d)
D <- as.matrix(dist(H))
rownames(D) <- paste0("k",1:k)
colnames(D) <- paste0("k",1:k)
tr <- nj(D[celltype_factors,celltype_factors])
tr$edge.length <- abs(tr$edge.length)
plot(tr,cex = 0.75,edge.width = 1.5,adj = 5,font = 1)


sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS Sonoma 14.7.1
# 
# 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] ape_5.8-1          cowplot_1.1.3      ggplot2_3.5.0      fastTopics_0.6-193
# [5] Matrix_1.6-5      
# 
# loaded via a namespace (and not attached):
#  [1] gtable_0.3.4        xfun_0.42           bslib_0.6.1        
#  [4] htmlwidgets_1.6.4   ggrepel_0.9.5       lattice_0.22-5     
#  [7] quadprog_1.5-8      vctrs_0.6.5         tools_4.3.3        
# [10] generics_0.1.3      parallel_4.3.3      tibble_3.2.1       
# [13] fansi_1.0.6         highr_0.10          pkgconfig_2.0.3    
# [16] data.table_1.15.2   SQUAREM_2021.1      RcppParallel_5.1.7 
# [19] lifecycle_1.0.4     truncnorm_1.0-9     farver_2.1.1       
# [22] compiler_4.3.3      stringr_1.5.1       git2r_0.33.0       
# [25] progress_1.2.3      munsell_0.5.0       RhpcBLASctl_0.23-42
# [28] httpuv_1.6.14       htmltools_0.5.7     sass_0.4.8         
# [31] yaml_2.3.8          lazyeval_0.2.2      plotly_4.10.4      
# [34] crayon_1.5.2        later_1.3.2         pillar_1.9.0       
# [37] jquerylib_0.1.4     whisker_0.4.1       tidyr_1.3.1        
# [40] uwot_0.2.2.9000     cachem_1.0.8        nlme_3.1-164       
# [43] gtools_3.9.5        tidyselect_1.2.1    digest_0.6.34      
# [46] Rtsne_0.17          stringi_1.8.3       dplyr_1.1.4        
# [49] purrr_1.0.2         ashr_2.2-66         labeling_0.4.3     
# [52] rprojroot_2.0.4     fastmap_1.1.1       grid_4.3.3         
# [55] colorspace_2.1-0    cli_3.6.2           invgamma_1.1       
# [58] magrittr_2.0.3      utf8_1.2.4          withr_3.0.0        
# [61] prettyunits_1.2.0   scales_1.3.0        promises_1.2.1     
# [64] rmarkdown_2.26      httr_1.4.7          workflowr_1.7.1    
# [67] hms_1.1.3           pbapply_1.7-2       evaluate_0.23      
# [70] knitr_1.45          viridisLite_0.4.2   irlba_2.3.5.1      
# [73] rlang_1.1.3         Rcpp_1.0.12         mixsqp_0.3-54      
# [76] glue_1.7.0          jsonlite_1.8.8      R6_2.5.1           
# [79] fs_1.6.3