NMF analysis of the LPS data set
Peter Carbonetto
Last updated: 2025-06-06
Checks: 6 1
Knit directory:
single-cell-jamboree/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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).
Load packages used to process the data, perform the analyses, and create the plots.
library(data.table)
library(fastTopics)
library(NNLM)
library(ebnm)
library(flashier)
library(singlecelljamboreeR)
library(ggplot2)
library(cowplot)Set the seed for reproducibility:
set.seed(1)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)
Fit a topic model with \(K = 14\) topics to the counts:
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$set(autodep = TRUE) at the top of the
file (in a chunk that is not cached). Alternatively, you can customize
the option dependson for each individual chunk that is
cached. Using either autodep or dependson will
remove this warning. See the
knitr cache options for more details.
Structure plot comparing the topics to the organ types:
rows <- order(samples$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","LU","CO","SI","iLN","SP",
"TH","SK","KI","LI","BR","HE")))
structure_plot(tm,grouping = samples$tissue,gap = 4,
topics = 1:14,colors = topic_colors,
loadings_order = rows) +
labs(fill = "") +
theme(axis.text.x = element_text(angle = 0,hjust = 0.5),
legend.key.height = unit(0.01,"cm"),
legend.key.width = unit(0.2,"cm"),
legend.text = element_text(size = 6))
Abbreviations used: BM = bone marrow; BR = brain; CO = colon; HE = heart; iLN = inguinal lymph node; KI = kidney; LI = liver; LU = lung; SI = small intestine; SK = skin; SP = spleen; TH = thymus.
This next structure plot better highlights the topics that capture the processes driven by LPS-induced sepsis:
topic_colors <- c("magenta","gray50","gray65","gray40",
"gray85","gray75","red","gray80","gray90",
"gray60","limegreen","gray70","gray55",
"gold")
structure_plot(tm,grouping = samples$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))
EBNMF (flashier)
Next fit an NMF to the shifted log counts using flashier, with \(K = 15\):
k <- 15
n <- nrow(shifted_log_counts)
m <- ncol(shifted_log_counts)
nmf0 <- nnmf(shifted_log_counts,k = 1,loss = "mse",method = "scd",
max.iter = 10,verbose = 0,n.threads = 4)
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(shifted_log_counts,k,init = list(W = W0,H = H0),
loss = "mse",method = "scd",max.iter = 10,
verbose = 0,n.threads = 8)
x <- rpois(1e7,1/n)
s1 <- sd(log(x + 1))
sparse_prior <- ebnm_point_exponential(x = c(rep(1,100)))
sparse_prior$fitted_g$pi <- c(0.99,0.01)
ebnm_sparse_prior <- flash_ebnm(prior_family = "point_exponential",
fix_g = TRUE,g_init = sparse_prior)
fl_nmf <- flash_init(shifted_log_counts,var_type = 2,S = s1)
fl_nmf <- flash_factors_init(fl_nmf,list(nmf$W,t(nmf$H)),
c(ebnm_sparse_prior,ebnm_point_exponential))
fl_nmf <- flash_backfit(fl_nmf,extrapolate = FALSE,maxiter = 100,verbose = 0)
fl_nmf <- flash_backfit(fl_nmf,extrapolate = TRUE,maxiter = 100,verbose = 0)
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$set(autodep = TRUE) at the top of the
file (in a chunk that is not cached). Alternatively, you can customize
the option dependson for each individual chunk that is
cached. Using either autodep or dependson will
remove this warning. See the
knitr cache options for more details.
Structure plot comparing the factors to the organ types:
rows <- order(samples$timepoint)
topic_colors <- c("powderblue","dodgerblue","olivedrab","limegreen",
"forestgreen","red","darkmagenta","gray","darkorange",
"cyan","royalblue","darkblue","lightskyblue",
"gold","sienna")
L <- ldf(fl_nmf,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))
This next structure plot better highlights the topics that capture the processes driven by LPS-induced sepsis:
rows <- order(samples$timepoint)
topic_colors <- c("gray95","gray70","gray80","limegreen",
"gray60","red","gray75","gray","gray85",
"gray90","gray65","gray50","gray45",
"gray35","gray75")
L <- ldf(fl_nmf,type = "i")$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))
sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS 15.4.1
#
# 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] workflowr_1.7.1 cowplot_1.1.3
# [3] ggplot2_3.5.0 singlecelljamboreeR_0.1-3
# [5] flashier_1.0.55 ebnm_1.1-34
# [7] NNLM_0.4.4 fastTopics_0.7-25
# [9] data.table_1.17.4
#
# loaded via a namespace (and not attached):
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# [46] yaml_2.3.8 codetools_0.2-19 processx_3.8.3
# [49] lattice_0.22-5 tibble_3.2.1 plyr_1.8.9
# [52] withr_3.0.2 evaluate_1.0.3 Rtsne_0.17
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