Examine topic-model-based single-cell likelihoods in FACS-purified PBMC data
Peter Carbonetto
Last updated: 2021-01-05
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Knit directory: single-cell-topics/analysis/
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Here we calculate single-cell likelihoods to assess how well the multinomial topic model captures expression in different cells and cell types.
Load the packages used in the analysis below.
library(Matrix)
library(fastTopics)
library(ggplot2)
library(cowplot)Load the count data, the \(K = 6\) topic model fit, and the 7 clusters identified in the clustering analysis.
load("../data/pbmc_purified.RData")
fit <- readRDS(file.path("../output/pbmc-purified/rds",
"fit-pbmc-purified-scd-ex-k=6.rds"))$fit
fit <- poisson2multinom(fit)
samples <- readRDS("../output/pbmc-purified/clustering-pbmc-purified.rds")Calculate the multinomial topic model likelihood for each cell.
loglik <- loglik_multinom_topic_model(counts,fit)This can be used to assess how well the topic model “fits” each cell.
pdat <- data.frame(loglik)
p1 <- ggplot(pdat,aes(loglik)) +
geom_histogram(bins = 64,color = "white",fill = "black") +
scale_x_continuous(breaks = seq(-20000,0,2500)) +
labs(y = "number of cells") +
theme_cowplot(font_size = 10)
print(p1)
Combine the T-cell subpopulations into one category:
celltype <- as.character(samples$celltype)
celltype[celltype == "CD14+ Monocyte"] <- "CD14+"
celltype[celltype == "CD4+/CD45RA+/CD25- Naive T" |
celltype == "CD4+/CD45RO+ Memory" |
celltype == "CD8+/CD45RA+ Naive Cytotoxic" |
celltype == "CD4+ T Helper2" |
celltype == "CD4+/CD25 T Reg"] <- "T cell"
celltype <- factor(celltype)Most of the poorly fit cells are in the CD34+ subpopulation:
pdat <- data.frame(loglik = loglik,celltype = celltype)
p2 <- ggplot(pdat,aes(x = loglik)) +
geom_histogram(bins = 64,color = "white",fill = "black") +
facet_wrap(vars(celltype),scales = "free_y",ncol = 2) +
scale_x_continuous(breaks = seq(-20000,0,2500)) +
labs(y = "number of cells") +
theme_cowplot(font_size = 9)
print(p2)
TO DO: Revise this text.
Here, we compare the single-cell likelihoods under the multinomial topic model against the likelihoods under a simple “hard clustering” model in which the cells in each FACS subpopulation share the same underlying pattern of expression. This serves as a sanity check, as we expect the fit to improve with the more flexible topic model.
fit_subpop <- init_poisson_nmf_from_clustering(counts,samples$celltype)
fit_subpop <- poisson2multinom(fit_subpop)
loglik_subpop <- loglik_multinom_topic_model(counts,fit_subpop)Indeed, the topic model provides a better fit for almost all cells:
facs_colors <- c("dodgerblue", # B cells
"forestgreen", # CD14+
"darkmagenta", # CD34+
"gray", # NK cells
"tomato", # cytotoxic T cells
"gold") # T cells
pdat <- data.frame(x = loglik_subpop,y = loglik,celltype = celltype)
p3 <- ggplot(pdat,aes(x = x,y = y,fill = celltype)) +
geom_point(shape = 21,color = "white") +
geom_abline(intercept = 0,slope = 1,linetype = "dotted") +
scale_x_continuous(limits = c(-15000,0),breaks = seq(-20000,0,2500)) +
scale_y_continuous(limits = c(-15000,0),breaks = seq(-20000,0,2500)) +
scale_fill_manual(values = facs_colors) +
labs(x = "cluster model",y = "topic model",fill = "FACS subpopulation") +
theme_cowplot(font_size = 9)
print(p3)
| Version | Author | Date |
|---|---|---|
| 6619bbd | Peter Carbonetto | 2021-01-04 |
The improvement in fit is greatest for cells in the CD34+ and T cell FACS subpopulations:
p4 <- ggplot(pdat,aes(x = x,y = y,fill = celltype)) +
geom_point(shape = 21,color = "white") +
geom_abline(intercept = 0,slope = 1,linetype = "dotted") +
facet_wrap(vars(celltype)) +
scale_x_continuous(limits = c(-15000,0),breaks = seq(-20000,0,5000)) +
scale_y_continuous(limits = c(-15000,0),breaks = seq(-20000,0,5000)) +
scale_fill_manual(values = facs_colors) +
labs(x = "cluster model",y = "topic model",fill = "FACS subpopulation") +
theme_cowplot(font_size = 9)
print(p4)
| Version | Author | Date |
|---|---|---|
| 6619bbd | Peter Carbonetto | 2021-01-04 |
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
#
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
#
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] cowplot_1.0.0 ggplot2_3.3.0 fastTopics_0.4-13 Matrix_1.2-18
#
# loaded via a namespace (and not attached):
# [1] ggrepel_0.9.0 Rcpp_1.0.5 lattice_0.20-38
# [4] tidyr_1.0.0 prettyunits_1.1.1 assertthat_0.2.1
# [7] zeallot_0.1.0 rprojroot_1.3-2 digest_0.6.23
# [10] R6_2.4.1 backports_1.1.5 MatrixModels_0.4-1
# [13] evaluate_0.14 coda_0.19-3 httr_1.4.2
# [16] pillar_1.4.3 rlang_0.4.5 progress_1.2.2
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