NMF analysis of the combined tissue + organoid GCA data
Peter Carbonetto
Last updated: 2026-03-17
Checks: 7 0
Knit directory:
single-cell-jamboree/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7e5b3fd | Peter Carbonetto | 2026-03-17 | wflow_publish("gca.Rmd", verbose = T, view = F) |
| html | 0284003 | Peter Carbonetto | 2026-03-17 | Subsampled the EC cells for the structure plots in the gca analysis. |
| Rmd | 4a1993a | Peter Carbonetto | 2026-03-17 | wflow_publish("gca.Rmd", verbose = T, view = F) |
| html | 96c0613 | Peter Carbonetto | 2026-03-16 | Ran wflow_publish("gca.Rmd"). |
| Rmd | f6d72d2 | Peter Carbonetto | 2026-03-16 | wflow_publish("gca.Rmd", verbose = T, view = F) |
| Rmd | 82c9fdb | Peter Carbonetto | 2026-03-16 | Made some adjustments to the structure plots in gca.Rmd. |
| Rmd | dc2cdfb | Peter Carbonetto | 2026-03-16 | Working on structure plots in gca analysis. |
| Rmd | 94b4614 | Peter Carbonetto | 2026-03-16 | Working on the structure plots in gca.Rmd. |
| html | f1fbc12 | Peter Carbonetto | 2026-03-16 | First build of the gca analysis. |
| Rmd | 73025b1 | Peter Carbonetto | 2026-03-16 | wflow_publish("gca.Rmd", view = F, verbose = T) |
Here we explore whether non-negative matrix factorization (NMF)
identifies factors (i.e., gene programs) common to both the tissues and
organoids. These results were generated using the
fit_gca_nmf.R script, found here.
Load the packages needed for this analysis:
library(ggplot2)
library(cowplot)
library(fastTopics)Set the seed for reproducibility:
set.seed(1)Load the results from running fitting an NMF model using flashier, with \(k = 30\):
load("../output/gca_nmf_k=20.RData")
L <- fl_nmf_ldf$L
colnames(L) <- paste0("k",1:20)For better Structure Plots, subsample the EC cells:
rows1 <- which(sample_info$celltype == "EC")
rows2 <- which(sample_info$celltype != "EC")
rows1 <- sample(rows1,8000)
rows <- c(rows1,rows2)
rows <- sort(rows)
L <- L[rows,]
sample_info <- sample_info[rows,]These are the factors (gene programs) that are mostly specific to tissues. Observe that the memberships in the organoids (bottom) are mostly very small.
topics_tissue <- c("k2","k3","k9","k12","k14","k15","k20")
topic_colors <-
c("#FFB300","#803E75","#FF6800","#A6BDD7","#C10020","#CEA262",
"#93AA00","#007D34","#F6768E","#00538A","#FF7A5C","darkblue",
"#FF8E00","yellow","cyan","#F4C800","#817066","#593315",
"#F13A13","forestgreen")
rows1 <- which(sample_info$origin == "Tissue")
rows2 <- which(sample_info$origin == "Org")
p1 <- structure_plot(L[rows1,],gap = 10,topics = topics_tissue,
grouping = factor(sample_info[rows1,"celltype"]),
colors = topic_colors) +
labs(y = "membership")
p2 <- structure_plot(L[rows2,],gap = 10,topics = topics_tissue,
grouping = factor(sample_info[rows2,"celltype"]),
colors = topic_colors) +
labs(y = "membership") +
ylim(0,1)
plot_grid(p1,p2,nrow = 2,ncol = 1)
Note: that I’m using the cell-type labels in the “celltype” column of the meta data as a reference point for understanding the gene programs.
Next, these are the gene programs mostly specific to organoids. Again, notice that the memberships are mostly very small in the tissues:
topics_organoid <- c("k4","k6","k8","k11","k13","k17","k18")
rows1 <- which(sample_info$origin == "Tissue")
rows2 <- which(sample_info$origin == "Org")
p3 <- structure_plot(L[rows1,],gap = 10,topics = topics_organoid,
grouping = factor(sample_info[rows1,"celltype"]),
colors = topic_colors) +
labs(y = "membership") +
ylim(0,2)
p4 <- structure_plot(L[rows2,],gap = 10,topics = topics_organoid,
grouping = factor(sample_info[rows2,"celltype"]),
colors = topic_colors) +
labs(y = "membership")
plot_grid(p3,p4,nrow = 2,ncol = 1)
Finally, these are the gene programs that appear in both the tissues and the organoids. Some of the
topics_shared <- c("k5","k7","k10","k16","k19")
rows1 <- which(sample_info$origin == "Tissue")
rows2 <- which(sample_info$origin == "Org")
p5 <- structure_plot(L[rows1,],gap = 10,topics = topics_shared,
grouping = factor(sample_info[rows1,"celltype"]),
colors = topic_colors) +
labs(y = "membership")
p6 <- structure_plot(L[rows2,],gap = 10,topics = topics_shared,
grouping = factor(sample_info[rows2,"celltype"]),
colors = topic_colors) +
labs(y = "membership")
plot_grid(p5,p6,nrow = 2,ncol = 1)
sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS 15.7.4
#
# 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] fastTopics_0.7-38 cowplot_1.1.3 ggplot2_4.0.1
#
# loaded via a namespace (and not attached):
# [1] gtable_0.3.6 xfun_0.52 bslib_0.9.0
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