Perform differential expression analysis on topics and clusters identified in mixture of FACS-purified PBMC data
Peter Carbonetto
Last updated: 2021-01-06
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Knit directory: single-cell-topics/analysis/
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| Rmd | b92d4db | Peter Carbonetto | 2021-01-06 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”) |
| html | a0b6c2b | Peter Carbonetto | 2021-01-06 | Build site. |
| Rmd | ed8a595 | Peter Carbonetto | 2021-01-06 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| html | 0411340 | Peter Carbonetto | 2021-01-06 | Added scatterplots to plots_purified_pbmc analysis. |
| Rmd | e16bf80 | Peter Carbonetto | 2021-01-06 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”) |
| Rmd | a12c42a | Peter Carbonetto | 2021-01-05 | Implemented function lfc_scatterplot in functions_for_plots_purified_pbmc.R. |
| Rmd | 9fd0455 | Peter Carbonetto | 2021-01-05 | Added steps to save volcano plots in plots_purified_pbmc analysis. |
| html | fad8e3d | Peter Carbonetto | 2021-01-05 | First build of plots_purified_pbmc page. |
| Rmd | bf07930 | Peter Carbonetto | 2021-01-05 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| Rmd | 40c2d84 | Peter Carbonetto | 2021-01-04 | Working on log-fold change scatterplots in plots_purified_pbmc analysis. |
| Rmd | e437ddf | Peter Carbonetto | 2021-01-04 | Working on volcano plots in plots_purified_pbmc analysis. |
Here we perform a differential expression analysis using the topic model fit to the mixture of FACS-purified data, as well as the clusters identified from this topic model.
Load the packages used in the analysis below, as well as additional functions that we will use to generate some of the plots.
library(Matrix)
library(fastTopics)
library(ggplot2)
library(ggrepel)
library(cowplot)
source("../code/functions_for_plots_purified_pbmc.R")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"))$fit
fit <- poisson2multinom(fit)
samples <- readRDS("../output/pbmc-purified/clustering-pbmc-purified.rds")Perform differential expression analysis using the FACS labeling:
celltype <- as.character(samples$celltype)
celltype[celltype == "CD4+/CD45RA+/CD25- Naive T" |
celltype == "CD8+ Cytotoxic T" |
celltype == "CD4+/CD45RO+ Memory" |
celltype == "CD8+/CD45RA+ Naive Cytotoxic" |
celltype == "CD4+ T Helper2" |
celltype == "CD4+/CD25 T Reg"] <- "T cell"
celltype <- factor(celltype)
timing <- system.time(
diff_count_facs <- diff_count_clusters(celltype,counts))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21952 x 5 = 109760 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 109.51 seconds.
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$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
Perform differential expression analysis using the clusters:
timing <- system.time(
diff_count_clusters <- diff_count_clusters(samples$cluster,counts))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21952 x 7 = 153664 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 174.35 seconds.
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$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
Perform differential expression analysis using the multinomial topic model, after removing the dendritic cells cluster, and after merging the two T-cell topics:
rows <- which(samples$cluster != "dendritic")
fit_merged <- select_loadings(fit,loadings = rows)
fit_merged <- merge_topics(fit,c(1,6))
timing <- system.time(
diff_count_topics <- diff_count_analysis(fit_merged,counts))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21952 x 5 = 109760 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 104.70 seconds.
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$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
Perform differential expression analysis on the T cells only:
rows <- which(samples$cluster == "T")
fit_t <- select_loadings(fit,loadings = rows)
timing <- system.time(
diff_count_t <- diff_count_analysis(fit_t,counts[rows,]))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21952 x 6 = 131712 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 89.14 seconds.
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$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
Having completed the differential expression calculations, as an example, we visualize the differential expression results for topic 3 (“B cells”) using a volcano plot:
p1 <- volcano_plot_better(diff_count_topics,"k3",genes$symbol,0.998,
"topic 3 (B cells)",show_legend = TRUE)
print(p1)
Here are more volcano plots:
p2 <- volcano_plot_better(diff_count_topics,"k3",genes$symbol,0.998,
"topic 3 (B cells)")
p3 <- volcano_plot_better(diff_count_topics,"k4",genes$symbol,0.998,
"topic 4 (NK cells)")
p4 <- volcano_plot_better(diff_count_topics,"k2",genes$symbol,0.998,
"topic 2 (CD14+)")
p5 <- volcano_plot_better(diff_count_topics,"k5",genes$symbol,0.998,
"topic 5 (CD34+)")
p6 <- volcano_plot_better(diff_count_topics,"k1+k6",genes$symbol,0.998,
"topics 1 + 6 (T cells)")
p7 <- volcano_plot_better(diff_count_t,"k1",genes$symbol,0.997,
"topic 1 (CD4+/CD8+)")
p8 <- volcano_plot_better(diff_count_clusters,"CD8+",genes$symbol,0.998,
"CD8+ cluster") + xlim(-11,11)
p9 <- volcano_plot_better(diff_count_clusters,"dendritic",genes$symbol,0.997,
"dendritic cells cluster")
plot_grid(p2,p3,p4,p5,p6,p7,p8,p9,nrow = 3,ncol = 3)
The results of the differential expression analyses can also be browsed in interactive volcano plots:
volcano_plotly(diff_count_topics,"k3","volcano_plot_purified_pbmc_bcells.html",
genes$symbol,title = "topic 3 (B cells)")
volcano_plotly(diff_count_topics,"k4","volcano_plot_purified_pbmc_nk.html",
genes$symbol,title = "topic 4 (NK cells)")
volcano_plotly(diff_count_topics,"k2","volcano_plot_purified_pbmc_cd14.html",
genes$symbol,title = "topic 2 (CD14+)")
volcano_plotly(diff_count_topics,"k5","volcano_plot_purified_pbmc_cd34.html",
genes$symbol,title = "topic 5 (CD34+)")
volcano_plotly(diff_count_topics,"k1+k6",
"volcano_plot_purified_pbmc_tcells.html",genes$symbol,
title = "topics 1 + 6 (T cells)")
volcano_plotly(diff_count_t,"k1","volcano_plot_purified_pbmc_cd4cd8.html",
genes$symbol,title = "topic 1 (CD4+/CD8+)")
volcano_plotly(diff_count_clusters,"CD8+",
"volcano_plot_purified_pbmc_cd8.html",genes$symbol,
title = "CD8+ cluster")
volcano_plotly(diff_count_clusters,"dendritic",
"volcano_plot_purified_pbmc_dendritic.html",genes$symbol,
title = "dendritic cells cluster")The interactive volcano plots can also be viewed by clicking on these links:
p10 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD19+ B","k3",
genes$symbol,label_above_quantile = 0.998,
xlab = "B cells FACS subpopulation",ylab = "topic 3")
p11 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD56+ NK","k4",
genes$symbol,label_above_quantile = 0.998,
xlab = "NK cells FACS subpopulation",ylab = "topic 4")
p12 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD14+ Monocyte","k2",
genes$symbol,label_above_quantile = 0.998,
xlab = "CD14+ FACS subpopulation",ylab = "topic 2")
p13 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD34+","k5",
genes$symbol,label_above_quantile = 0.998,
xlab = "CD34+ FACS subpopulation",ylab = "topic 4")
p14 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"T cell","k1+k6",
genes$symbol,label_above_quantile = 0.998,
xlab = "T cells FACS subpopulation",
ylab = "topics 1 + 6")
plot_grid(p10,p11,p12,p13,p14,nrow = 3,ncol = 2)
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 ggrepel_0.9.0 ggplot2_3.3.0 fastTopics_0.4-13
# [5] Matrix_1.2-18
#
# loaded via a namespace (and not attached):
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# [61] vctrs_0.2.1 tools_3.6.2 glue_1.3.1
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