NMF analysis of the LPS data set
Peter Carbonetto
Last updated: 2025-07-04
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single-cell-jamboree/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 3d27ef8 | Peter Carbonetto | 2025-07-04 | Added GSEA of factor k6 to the lps analysis. |
| Rmd | cafd86d | Peter Carbonetto | 2025-07-04 | Implemented draft pathway analysis in temp4.R; next need to incorporate into lps.Rmd. |
| Rmd | b7aff59 | Peter Carbonetto | 2025-07-01 | Added some scatterplots to compare topics and factors in the lps analysis. |
| Rmd | 31afa30 | Peter Carbonetto | 2025-06-30 | Added some notes/thoughts to lps.Rmd. |
| Rmd | 23d0a0c | Peter Carbonetto | 2025-06-30 | Fixed one of the structure plots in the lps analysis. |
| Rmd | 842772b | Peter Carbonetto | 2025-06-30 | Added k=9 fits for the topic model and flashier NMF to the lps analysis. |
| Rmd | eb06be5 | Peter Carbonetto | 2025-06-30 | Added a structure plot for the k=9 topic model to the lps analysis. |
| Rmd | 3bfc932 | Peter Carbonetto | 2025-06-27 | Added a note to lps.Rmd. |
| Rmd | 53085f1 | Peter Carbonetto | 2025-06-26 | Working on adding a new topic model fit with k=9 topics in the lps example to illutrate some key ideas. |
| Rmd | ce314bb | Peter Carbonetto | 2025-06-09 | First try at running fastTopics and flashier on the pancreas_cytokine data, for mouse = S1 only; from this analysis I learned that I need to remove the mt and rp genes. |
| html | 4abf00c | Peter Carbonetto | 2025-06-06 | Ran wflow_publish("lps.Rmd"). |
| Rmd | f38b586 | Peter Carbonetto | 2025-06-06 | A small fix to the lps analysis. |
| Rmd | aae4257 | Peter Carbonetto | 2025-06-06 | A couple fixes to the lps analysis. |
| Rmd | 6cbad5f | Peter Carbonetto | 2025-06-06 | Added a structure plot to the lps analysis. |
| Rmd | 90d6c06 | Peter Carbonetto | 2025-06-06 | Improved the structure plots in the lps analysis. |
| Rmd | dac95b5 | Peter Carbonetto | 2025-06-06 | Made a few changes to the flashier fit in the lps analysis. |
| Rmd | 9e1f127 | Peter Carbonetto | 2025-06-05 | Added code to pancreas_cytokine analysis to prepare the scrna-seq data downloaded from geo. |
| Rmd | 8d945a1 | Peter Carbonetto | 2025-06-04 | Added flashier fit to lps analysis; need to revise this and the topic modeling result. |
| Rmd | dac6198 | Peter Carbonetto | 2025-06-04 | Working on topic modeling results for lps data. |
| Rmd | 8f39607 | Peter Carbonetto | 2025-06-04 | Added steps to the lps analysis to load and prepare the data. |
| html | 2bfef0b | Peter Carbonetto | 2025-06-04 | First build of the LPS analysis. |
| Rmd | 85adf3f | Peter Carbonetto | 2025-06-04 | wflow_publish("lps.Rmd") |
Here we will revisit the LPS data set that we analyzed using a topic model in the Takahama et al Nat Immunol paper (LPS = lipopolysaccharide). I believe some interesting insights can be gained by examining this data set more deeply.
Load packages used to process the data, perform the analyses, and create the plots.
library(Matrix)
library(readr)
library(data.table)
library(fastTopics)
library(NNLM)
library(ebnm)
library(flashier)
library(pathways)
library(singlecelljamboreeR)
library(ggplot2)
library(ggrepel)
library(cowplot)Prepare the data for analysis with fastTopics and flashier
Load the RNA-seq counts:
read_lps_data <- function (file) {
counts <- fread(file)
class(counts) <- "data.frame"
genes <- counts[,1]
counts <- t(as.matrix(counts[,-1]))
colnames(counts) <- genes
samples <- rownames(counts)
samples <- strsplit(samples,"_")
samples <- data.frame(tissue = sapply(samples,"[[",1),
timepoint = sapply(samples,"[[",2),
mouse = sapply(samples,"[[",3))
samples <- transform(samples,
tissue = factor(tissue),
timepoint = factor(timepoint),
mouse = factor(mouse))
return(list(samples = samples,counts = counts))
}
out <- read_lps_data("../data/lps.csv.gz")
samples <- out$samples
counts <- out$counts
rm(out)Remove a sample that appears to be an outlier based on the NMF analyses:
i <- which(rownames(counts) != "iLN_d2_20")
samples <- samples[i,]
counts <- counts[i,]Remove genes that are expressed in fewer than 5 samples:
j <- which(colSums(counts > 0) > 4)
counts <- counts[,j]This is the dimension of the data set we will analyze:
dim(counts)
# [1] 363 33533For the Gaussian-based analyses, we will need the shifted log counts:
a <- 1
s <- rowSums(counts)
s <- s/mean(s)
shifted_log_counts <- log1p(counts/(a*s))Topic model (fastTopics)
First let’s fit a topic model with \(K = 9\) topics to the counts. This is probably an insufficient number of topics to fully capture the interesting structure in the data, but this is done on purpose since I want to illustrate how the topic model prioritizes the structure.
set.seed(1)
tm_k9 <- fit_poisson_nmf(counts,k = 9,init.method = "random",method = "em",
numiter = 20,verbose = "none",
control = list(numiter=4,nc=8,extrapolate=FALSE))
tm_k9 <- fit_poisson_nmf(counts,fit0 = tm_k9,method = "scd",numiter = 40,
control = list(numiter = 4,nc = 8,extrapolate = TRUE),
verbose = "none")
Warning: The above code chunk cached its results, but
it won’t be re-run if previous chunks it depends on are updated. If you
need to use caching, it is highly recommended to also set
knitr::opts_chunk$set(autodep = TRUE) at the top of the
file (in a chunk that is not cached). Alternatively, you can customize
the option dependson for each individual chunk that is
cached. Using either autodep or dependson will
remove this warning. See the
knitr cache options for more details.
Structure plot comparing the topics to the tissue types:
rows <- order(samples$timepoint)
topic_colors <- c("magenta","darkorange","darkblue","forestgreen",
"dodgerblue","gray","red","olivedrab","darkmagenta",
"sienna","limegreen","royalblue","lightskyblue",
"gold")
samples <- transform(samples,
tissue = factor(tissue,c("PBMC","BM","CO","SI","iLN","SP",
"TH","SK","BR","LI","HE","KI","LU")))
structure_plot(tm_k9,grouping = samples$tissue,gap = 4,
topics = 1:9,colors = topic_colors,
loadings_order = rows) +
labs(fill = "") +
theme(axis.text.x = element_text(angle = 0,hjust = 0.5))
Abbreviations used: BM = bone marrow; BR = brain; CO = colon; HE = heart; iLN = inguinal lymph node; KI = kidney; LI = liver; LU = lung; SI = small intestine; SK = skin; SP = spleen; TH = thymus.
The topics largely correspond to the different tissues, although because there are more tissues than topics, some tissues that are more similar to each other shared the same topic. It is also interesting that, for the most part, none of the topics are capturing changes downstream of the LPS treatment. So presumably these expression changes are more subtle than the differences in expression among the tissues.
Fit a topic model with \(K = 14\) topics to the counts:
set.seed(1)
tm <- fit_poisson_nmf(counts,k = 14,init.method = "random",method = "em",
numiter = 20,verbose = "none",
control = list(numiter = 4,nc = 8,extrapolate = FALSE))
tm <- fit_poisson_nmf(counts,fit0 = tm,method = "scd",numiter = 40,
control = list(numiter = 4,nc = 8,extrapolate = TRUE),
verbose = "none")
Warning: The above code chunk cached its results, but
it won’t be re-run if previous chunks it depends on are updated. If you
need to use caching, it is highly recommended to also set
knitr::opts_chunk$set(autodep = TRUE) at the top of the
file (in a chunk that is not cached). Alternatively, you can customize
the option dependson for each individual chunk that is
cached. Using either autodep or dependson will
remove this warning. See the
knitr cache options for more details.
Structure plot comparing the topics to the tissue types:
structure_plot(tm,grouping = samples$tissue,gap = 4,
topics = 1:14,colors = topic_colors,
loadings_order = rows) +
labs(fill = "") +
theme(axis.text.x = element_text(angle = 0,hjust = 0.5),
legend.key.height = unit(0.01,"cm"),
legend.key.width = unit(0.2,"cm"),
legend.text = element_text(size = 6))
| Version | Author | Date |
|---|---|---|
| 4abf00c | Peter Carbonetto | 2025-06-06 |
This next structure plot better highlights the topics that are capturing expression changes over time, some being presumably driven by the LPS-induced sepsis:
topic_colors <- c("magenta","gray50","gray65","gray40",
"gray85","gray75","red","gray80","gray90",
"gray60","limegreen","gray70","gray55",
"gold")
structure_plot(tm,grouping = samples$tissue,gap = 4,
topics = 1:14,colors = topic_colors,
loadings_order = rows) +
labs(fill = "") +
theme(axis.text.x = element_text(angle = 0,hjust = 0.5),
legend.key.height = unit(0.01,"cm"),
legend.key.width = unit(0.2,"cm"),
legend.text = element_text(size = 6))
| Version | Author | Date |
|---|---|---|
| 4abf00c | Peter Carbonetto | 2025-06-06 |
EBNMF (flashier)
Similar to the topic modeling analysis, let’s start by fitting an EBNMF to the shifted log counts using flashier, first with \(K = 9\). Since the greedy initialization does not seem to work well in this example, I’ll use a different initialization strategy: obtain a “good” initialization using the NNLM package, then use this initialization to fit a NMF using flashier. This approach is implemented in the following function:
flashier_nmf <- function (X, k = 3, n.threads = 1) {
n <- nrow(X)
m <- ncol(X)
nmf0 <- nnmf(shifted_log_counts,k = 1,loss = "mse",method = "scd",
max.iter = 10,verbose = 0,n.threads = n.threads)
W0 <- nmf0$W
H0 <- nmf0$H
W0 <- cbind(W0,matrix(runif(n*(k-1)),n,k-1))
H0 <- rbind(H0,matrix(runif(m*(k-1)),k-1,m))
nmf <- nnmf(X,k,init = list(W = W0,H = H0),loss = "mse",method = "scd",
max.iter = 10,verbose = 0,n.threads = n.threads)
x <- rpois(1e7,1/n)
s1 <- sd(log(x + 1))
out <- flash_init(X,var_type = 2,S = s1)
out <- flash_factors_init(out,list(nmf$W,t(nmf$H)),ebnm_point_exponential)
out <- flash_backfit(out,extrapolate = FALSE,maxiter = 100,verbose = 0)
return(flash_backfit(out,extrapolate = TRUE,maxiter = 100,verbose = 0))
}Now fit an NMF to the shifted log counts, with \(K = 9\):
set.seed(1)
fl_nmf_k9 <- flashier_nmf(shifted_log_counts,k = 9,n.threads = 8)
# Warning in c_nnmf(A, as.integer(k), init.mask$Wi, init.mask$Hi, init.mask$Wm, :
# Target tolerance not reached. Try a larger max.iter.
# Warning in report.maxiter.reached(verbose.lvl): Maximum number of iterations
# reached.
# Warning in report.maxiter.reached(verbose.lvl): Maximum number of iterations
# reached.
Warning: The above code chunk cached its results, but
it won’t be re-run if previous chunks it depends on are updated. If you
need to use caching, it is highly recommended to also set
knitr::opts_chunk$set(autodep = TRUE) at the top of the
file (in a chunk that is not cached). Alternatively, you can customize
the option dependson for each individual chunk that is
cached. Using either autodep or dependson will
remove this warning. See the
knitr cache options for more details.
Structure plot comparing the topics to the tissue types:
rows <- order(samples$timepoint)
topic_colors <- c("powderblue","dodgerblue","olivedrab","limegreen",
"forestgreen","red","darkmagenta","gray","darkorange",
"cyan","royalblue","darkblue","lightskyblue",
"gold","sienna")
L <- ldf(fl_nmf_k9,type = "i")$L
structure_plot(L,grouping = samples$tissue,gap = 4,
topics = 1:9,colors = topic_colors,
loadings_order = rows) +
labs(fill = "",y = "membership")
Like the topic model, the EBNMF model with \(K = 9\) does not capture any changes downstream of the LPS-induced sepsis.
Next fit an NMF to the shifted log counts using flashier, with \(K = 15\):
set.seed(1)
fl_nmf <- flashier_nmf(shifted_log_counts,k = 15,n.threads = 8)
Warning: The above code chunk cached its results, but
it won’t be re-run if previous chunks it depends on are updated. If you
need to use caching, it is highly recommended to also set
knitr::opts_chunk$set(autodep = TRUE) at the top of the
file (in a chunk that is not cached). Alternatively, you can customize
the option dependson for each individual chunk that is
cached. Using either autodep or dependson will
remove this warning. See the
knitr cache options for more details.
Structure plot comparing the factors to the tissue types:
L <- ldf(fl_nmf,type = "i")$L
structure_plot(L,grouping = samples$tissue,gap = 4,
topics = 1:15,colors = topic_colors,
loadings_order = rows) +
labs(fill = "",y = "membership") +
theme(axis.text.x = element_text(angle = 0,hjust = 0.5),
legend.key.height = unit(0.01,"cm"),
legend.key.width = unit(0.25,"cm"),
legend.text = element_text(size = 7))
| Version | Author | Date |
|---|---|---|
| 4abf00c | Peter Carbonetto | 2025-06-06 |
This next structure plot better highlights the topics that capture the processes that are driven or may be driven by LPS-induced sepsis:
rows <- order(samples$timepoint)
topic_colors <- c("gray95","gray70","gray80","gray50",
"gray60","red","gray75","gray","gray85",
"gray90","gray65","darkblue","gray45",
"gray35","gray75")
L <- ldf(fl_nmf,type = "i")$L
structure_plot(L,grouping = samples$tissue,gap = 4,
topics = 1:15,colors = topic_colors,
loadings_order = rows) +
labs(fill = "",y = "membership") +
theme(axis.text.x = element_text(angle = 0,hjust = 0.5),
legend.key.height = unit(0.01,"cm"),
legend.key.width = unit(0.25,"cm"),
legend.text = element_text(size = 7))
| Version | Author | Date |
|---|---|---|
| 4abf00c | Peter Carbonetto | 2025-06-06 |
Factors isolating responses to LPS-induced sepsis
From the Structure plots, it appears that topic 7, and possibly topic 1, are capturing processes activated by LPS. However, I conjecture that it is difficult to determine which genes should members of topic 1 and which are members of the colon and small intensine topics. Indeed, topic 1 shares many genes with these two topics:
pdat <- cbind(data.frame(gene = colnames(counts)),
poisson2multinom(tm)$F)
rows <- which(pdat$k1 < 0.01)
pdat[rows,"gene"] <- ""
p1 <- ggplot(pdat,aes(x = k1,y = k4,label = gene)) +
geom_point() +
geom_text_repel(color = "gray",size = 2.5,max.overlaps = Inf) +
scale_x_continuous(trans = "sqrt") +
scale_y_continuous(trans = "sqrt") +
theme_cowplot(font_size = 10)
p2 <- ggplot(pdat,aes(x = k1,y = k13,label = gene)) +
geom_point() +
geom_text_repel(color = "gray",size = 2.5,max.overlaps = Inf) +
scale_x_continuous(trans = "sqrt") +
scale_y_continuous(trans = "sqrt") +
theme_cowplot(font_size = 10)
plot_grid(p1,p2,nrow = 1,ncol = 2)
Still, it is interesting that three genes, Reg3b, Reg3g and Lypd8, stand out in topic 1 as distinct from the colon and SI topics. Let’s now contrast this to the situation for topic 7:
pdat <- cbind(data.frame(gene = colnames(counts)),
poisson2multinom(tm)$F)
rows <- which(pdat$k7 < 0.008)
pdat[rows,"gene"] <- ""
p1 <- ggplot(pdat,aes(x = k7,y = k6,label = gene)) +
geom_point() +
geom_text_repel(color = "gray",size = 2.5,max.overlaps = Inf) +
scale_x_continuous(trans = "sqrt") +
scale_y_continuous(trans = "sqrt") +
theme_cowplot(font_size = 10)
p2 <- ggplot(pdat,aes(x = k7,y = k8,label = gene)) +
geom_point() +
geom_text_repel(color = "gray",size = 2.5,max.overlaps = Inf) +
scale_x_continuous(trans = "sqrt") +
scale_y_continuous(trans = "sqrt") +
theme_cowplot(font_size = 10)
plot_grid(p1,p2,nrow = 1,ncol = 2)
For illustration, I compared topic 7 to the kidney and lung topics. The key point here is that the topic model has selected genes for topic 7 that are very independent of the tissue topics. So this looks quite promising. Let’s now see if the result is similar for the EBNMF model fitted to the shifted log counts:
F <- ldf(fl_nmf,type = "i")$F
colnames(F) <- paste0("k",1:15)
pdat <- data.frame(tm = poisson2multinom(tm)$F[,"k7"],
nmf = F[,"k6"],
gene = rownames(F))
rows <- which(with(pdat,tm < 0.005 & nmf < 0.8))
pdat[rows,"gene"] <- ""
ggplot(pdat,aes(x = (tm)^(1/3),y = nmf,label = gene)) +
geom_point() +
geom_text_repel(color = "gray",size = 2.5,max.overlaps = Inf) +
labs(x = "topic 7",y = "factor 6") +
theme_cowplot(font_size = 12)
Indeed, factor 6 and topic 7 are cpaturing very similar expression patterns.
Next I ran GSEA on the on factor 6. (Running GSEA on topic 7 is complicated by the fact that it would be better to “shrink” the estimates before running GSEA, whereas this was automatically done for EBNMF result.)
data(gene_sets_mouse)
gene_sets <- gene_sets_mouse$gene_sets
gene_info <- gene_sets_mouse$gene_info
gene_set_info <- gene_sets_mouse$gene_set_info
j <- which(with(gene_sets_mouse$gene_set_info,
(database == "MSigDB-C2" &
grepl("CP",sub_category_code,fixed = TRUE)) |
(database == "MSigDB-C5") &
grepl("GO",sub_category_code,fixed = TRUE)))
genes <- sort(intersect(rownames(F),gene_info$Symbol))
i <- which(is.element(gene_info$Symbol,genes))
gene_info <- gene_info[i,]
gene_set_info <- gene_set_info[j,]
gene_sets <- gene_sets[i,j]
rownames(gene_sets) <- gene_info$Symbol
rownames(gene_set_info) <- gene_set_info$id
gene_set_info <- gene_set_info[,-2]
F <- ldf(fl_nmf,type = "i")$F
colnames(F) <- paste0("k",1:15)
gsea_fl_nmf <- singlecelljamboreeR::perform_gsea(F[,"k6"],gene_sets,
gene_set_info,L = 15,
verbose = FALSE)
out <- gsea_fl_nmf$selected_gene_sets
out$top_genes <- sapply(out$top_genes,function (x) paste(x,collapse = " "))
write_csv(out,"lps_gsea_fl_nmf.csv",quote = "none")The top gene set is the IFN-\(\alpha/beta\) signaling pathway, but other gene sets clearly relate to inflammation and immune system function:
print(gsea_fl_nmf$selected_gene_sets[c(2:7,9)],n = Inf)
# # A tibble: 18 × 7
# CS gene_set lbf pip coef genes name
# <fct> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
# 1 L5 M973 194. 1 0.222 49 REACTOME_INTERFERON_ALPHA_BETA_SIGN…
# 2 L1 M16779 178. 1.00 0.0858 306 GO_RESPONSE_TO_VIRUS
# 3 L2 M15265 171. 1 0.113 171 GO_RESPONSE_TO_INTERFERON_GAMMA
# 4 L3 M27686 125. 0.915 0.118 115 REACTOME_EUKARYOTIC_TRANSLATION_INI…
# 5 L3 M29813 125. 0.0855 0.128 96 REACTOME_RESPONSE_OF_EIF2AK4_GCN2_T…
# 6 L8 M18914 80.9 1 0.269 15 GO_CXCR_CHEMOKINE_RECEPTOR_BINDING
# 7 L4 M27605 64.2 1.00 0.142 44 REACTOME_INTERLEUKIN_10_SIGNALING
# 8 L9 M17120 60.4 1.00 0.0475 378 GO_NUCLEAR_SPECK
# 9 L7 M27895 59.8 0.995 0.0910 101 REACTOME_INTERLEUKIN_1_SIGNALING
# 10 L13 M19119 58.4 1.00 0.0514 313 GO_CADHERIN_BINDING
# 11 L6 M10009 52.4 1.00 0.0445 382 GO_MYELOID_CELL_DIFFERENTIATION
# 12 L15 M13922 44.3 0.984 0.0489 274 GO_INTRINSIC_APOPTOTIC_SIGNALING_PA…
# 13 L10 M39818 43.5 1.00 0.0495 264 WP_IL18_SIGNALING_PATHWAY
# 14 L14 M10214 41.4 0.841 0.0462 291 GO_RIBOSOME_BIOGENESIS
# 15 L14 M3080 41.4 0.159 0.0513 227 GO_RRNA_METABOLIC_PROCESS
# 16 L11 M2251 38.3 0.855 0.0436 307 GO_EXTERNAL_SIDE_OF_PLASMA_MEMBRANE
# 17 L11 M17505 38.3 0.137 0.0441 288 GO_SECRETORY_GRANULE_MEMBRANE
# 18 L12 M17194 36.5 0.990 0.0422 317 GO_VESICLE_LUMENThis is the same scatterplot as the one just above, but with the genes in the IFN-\(\alpha/beta\) signaling pathway highlighted:
pdat$pathway <- FALSE
pathway_genes <- names(which(gene_sets[,"M973"] > 0))
pdat[pathway_genes,"pathway"] <- TRUE
pdat <- pdat[order(pdat$pathway),]
ggplot(pdat,aes(x = (tm)^(1/3),y = nmf,label = gene,color = pathway)) +
geom_point() +
geom_text_repel(color = "gray",size = 2.5,max.overlaps = Inf) +
scale_color_manual(values = c("darkblue","orangered")) +
labs(x = "topic 7",y = "factor 6") +
theme_cowplot(font_size = 12)
sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS 15.5
#
# 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 ggrepel_0.9.5
# [3] ggplot2_3.5.0 singlecelljamboreeR_0.1-15
# [5] pathways_0.1-20 flashier_1.0.55
# [7] ebnm_1.1-34 NNLM_0.4.4
# [9] fastTopics_0.7-25 data.table_1.17.4
# [11] readr_2.1.5 Matrix_1.6-5
#
# loaded via a namespace (and not attached):
# [1] pbapply_1.7-2 rlang_1.1.5 magrittr_2.0.3
# [4] git2r_0.33.0 horseshoe_0.2.0 matrixStats_1.2.0
# [7] susieR_0.14.6 compiler_4.3.3 vctrs_0.6.5
# [10] reshape2_1.4.4 RcppZiggurat_0.1.6 quadprog_1.5-8
# [13] stringr_1.5.1 pkgconfig_2.0.3 crayon_1.5.2
# [16] fastmap_1.1.1 labeling_0.4.3 utf8_1.2.4
# [19] promises_1.2.1 rmarkdown_2.26 tzdb_0.4.0
# [22] bit_4.0.5 purrr_1.0.2 Rfast_2.1.0
# [25] xfun_0.42 cachem_1.0.8 trust_0.1-8
# [28] jsonlite_1.8.8 progress_1.2.3 highr_0.10
# [31] later_1.3.2 reshape_0.8.9 BiocParallel_1.36.0
# [34] irlba_2.3.5.1 parallel_4.3.3 prettyunits_1.2.0
# [37] R6_2.5.1 bslib_0.6.1 stringi_1.8.3
# [40] RColorBrewer_1.1-3 SQUAREM_2021.1 jquerylib_0.1.4
# [43] Rcpp_1.0.12 knitr_1.45 R.utils_2.12.3
# [46] httpuv_1.6.14 splines_4.3.3 tidyselect_1.2.1
# [49] yaml_2.3.8 codetools_0.2-19 lattice_0.22-5
# [52] tibble_3.2.1 plyr_1.8.9 withr_3.0.2
# [55] evaluate_1.0.3 Rtsne_0.17 RcppParallel_5.1.10
# [58] pillar_1.9.0 whisker_0.4.1 plotly_4.10.4
# [61] softImpute_1.4-1 generics_0.1.3 vroom_1.6.5
# [64] rprojroot_2.0.4 invgamma_1.1 truncnorm_1.0-9
# [67] hms_1.1.3 munsell_0.5.0 scales_1.3.0
# [70] ashr_2.2-66 gtools_3.9.5 RhpcBLASctl_0.23-42
# [73] glue_1.8.0 scatterplot3d_0.3-44 lazyeval_0.2.2
# [76] tools_4.3.3 fgsea_1.35.4 fs_1.6.5
# [79] fastmatch_1.1-6 grid_4.3.3 tidyr_1.3.1
# [82] colorspace_2.1-0 deconvolveR_1.2-1 cli_3.6.4
# [85] Polychrome_1.5.1 workflowr_1.7.1 fansi_1.0.6
# [88] mixsqp_0.3-54 viridisLite_0.4.2 dplyr_1.1.4
# [91] uwot_0.2.3 gtable_0.3.4 R.methodsS3_1.8.2
# [94] sass_0.4.9 digest_0.6.34 farver_2.1.1
# [97] htmlwidgets_1.6.4 R.oo_1.26.0 htmltools_0.5.8.1
# [100] lifecycle_1.0.4 httr_1.4.7 bit64_4.0.5