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)
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$tech == "celseq2")
sample_info <- sample_info[i,]
counts <- counts[i,]
sample_info <- transform(sample_info,celltype = factor(celltype))
Here is the topic model (with 9 topics):
celltype <- sample_info$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)
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)
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)
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"))
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)
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)
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