Perform differential expression analysis on topics and clusters identified in mixture of FACS-purified PBMC data
Peter Carbonetto
Last updated: 2021-03-01
Checks: 6 1
Knit directory: single-cell-topics/analysis/
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- diff-count-analysis-clusters
- diff-count-analysis-subpop
- diff-count-analysis-topics
- gsea-1
- gsea-3
- topics-vs-facs-scatterplots-1
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | b84788d | Peter Carbonetto | 2021-03-01 | Build site. |
| Rmd | f65e1f8 | Peter Carbonetto | 2021-03-01 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| html | dac053f | Peter Carbonetto | 2021-02-28 | Added gsea plots to plots_purified_pbmc analysis. |
| Rmd | d50e70e | Peter Carbonetto | 2021-02-28 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| Rmd | 54894e2 | Peter Carbonetto | 2021-02-26 | Working on gsea in plots_purified_pbmc analysis. |
| Rmd | 36d8c92 | Peter Carbonetto | 2021-02-26 | Working on new gsea step in plots_purified_pbmc analysis. |
| html | 5773c6c | Peter Carbonetto | 2021-02-25 | Adjusted dimensions of the scatterplots in the plots_purified_pbmc |
| Rmd | 3a31fac | Peter Carbonetto | 2021-02-25 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| html | ced5127 | Peter Carbonetto | 2021-02-25 | Added scatterplots back to plots_purified_pbmc analysis. |
| Rmd | 4d9e8ba | Peter Carbonetto | 2021-02-25 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| html | d21aab5 | Peter Carbonetto | 2021-02-24 | Re-built the plots_purified_pbmc analysis with the revised volcano plots. |
| Rmd | f79d2e8 | Peter Carbonetto | 2021-02-24 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”) |
| html | 7be2e95 | Peter Carbonetto | 2021-02-24 | Rebuilt the plots_purified_pbmc analysis. |
| Rmd | 3d7de41 | Peter Carbonetto | 2021-02-24 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| Rmd | 0163047 | Peter Carbonetto | 2021-02-24 | Updated volcano plots using K=6 topic model in plots_purified_pbmc analysis. |
| Rmd | a5c70b4 | Peter Carbonetto | 2021-02-24 | Revised the FACS-based and cluster-based volcano plots in the plots_pbmc_purified analysis. |
| Rmd | 0748309 | Peter Carbonetto | 2021-02-19 | Revised volcano plots in plots_purified_pbmc analysis; working on examine_de_calculations.R. |
| Rmd | 65dbb1e | Peter Carbonetto | 2021-02-17 | Made improvements to volcano plots in plots_purified_pbmc analysis after fixing DE calculations. |
| Rmd | 14401d5 | Peter Carbonetto | 2021-02-10 | Working on GSEA in plots_purified_pbmc analysis. |
| html | 6e2b53a | Peter Carbonetto | 2021-02-10 | Adjusted dimensions of scatterplots in plots_purified_pbmc analysis. |
| Rmd | 81c6f10 | Peter Carbonetto | 2021-02-10 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| Rmd | d6340b7 | Peter Carbonetto | 2021-02-10 | Updated the volcano plots for GitHub Pages. |
| html | 9afb462 | Peter Carbonetto | 2021-02-10 | Updated volcano plots and added ribosomal gene scatterplots to |
| Rmd | 13fedd9 | Peter Carbonetto | 2021-02-10 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| html | f8e1d99 | Peter Carbonetto | 2021-02-10 | Re-built plots_purified_pbmc page. |
| Rmd | 3e298d4 | Peter Carbonetto | 2021-02-10 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| html | d1d4185 | Peter Carbonetto | 2021-02-10 | Re-built plots_purified_pbmc with interactive volcano plots. |
| Rmd | 676e053 | Peter Carbonetto | 2021-02-10 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| Rmd | 52d16f3 | Peter Carbonetto | 2021-02-10 | Created topic volcano plots for paper in plots_purified_pbmc analysis. |
| html | f596d8a | Peter Carbonetto | 2021-02-09 | Made further refinements to the volcano plots in the |
| Rmd | cf1e3d2 | Peter Carbonetto | 2021-02-09 | Added script purified_pbmc_k7.R. |
| Rmd | dd1d2ca | Peter Carbonetto | 2021-02-09 | Working on volcano plots in plots_purified_pbmc analysis. |
| html | d0775d2 | Peter Carbonetto | 2021-02-09 | Re-built plots_purified_pbmc page after making improvements to the |
| Rmd | aecd700 | Peter Carbonetto | 2021-02-09 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE) |
| Rmd | 1f0eb8c | Peter Carbonetto | 2021-02-08 | More improvements to volcano plots in plots_purified_pbmc.Rmd. |
| Rmd | be27738 | Peter Carbonetto | 2021-02-08 | Improved volcano plots for FACS cell populations in plots_purified_pbmc.Rmd. |
| html | ab3eeb8 | Peter Carbonetto | 2021-01-29 | Build site. |
| Rmd | 0b73397 | Peter Carbonetto | 2021-01-29 | Added more volcano plots to plots_purified_pbmc analysis. |
| html | 42ebc62 | Peter Carbonetto | 2021-01-29 | Added volcano plots for FACS populations in plots_purified_pbmc |
| Rmd | 4edefd0 | Peter Carbonetto | 2021-01-29 | workflowr::wflow_publish(“plots_purified_pbmc.Rmd”) |
| Rmd | 01c1139 | Peter Carbonetto | 2021-01-29 | Adding new code for volcano plots in plots_purified_pbmc analysis. |
| html | a4bc59b | Peter Carbonetto | 2021-01-06 | Re-built plots_purified_pbmc page after removing cache. |
| 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(pathways)
library(ggplot2)
library(cowplot)
library(ggrepel)
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 == "CD4+/CD45RO+ Memory" |
celltype == "CD8+/CD45RA+ Naive Cytotoxic" |
celltype == "CD4+ T Helper2" |
celltype == "CD4+/CD25 T Reg"] <- "T cell"
celltype <- factor(celltype)
table(celltype)
diff_count_facs <- diff_count_clusters(celltype,counts)
# celltype
# CD14+ Monocyte CD19+ B CD34+ CD56+ NK
# 2612 10085 9232 8385
# CD8+ Cytotoxic T T cell
# 10209 54132
# Fitting 21952 x 6 = 131712 univariate Poisson models.
# Computing log-fold change statistics.
# Stabilizing log-fold change estimates using adaptive shrinkage.
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$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
These volcano plots summarize the results of the differential expression analysis using the FACS labeling:
p1 <- volcano_plot(diff_count_facs,"CD19+ B",genes$symbol,
betamax = 10,label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("B cells")
p2 <- volcano_plot(diff_count_facs,"CD14+ Monocyte",genes$symbol,
betamax = 10,label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("CD14+ cells")
p3 <- volcano_plot(diff_count_facs,"CD34+",genes$symbol,
betamax = 10,label_above_lfc = 2,
label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("CD34+ cells")
p4 <- volcano_plot(diff_count_facs,"CD56+ NK",genes$symbol,
betamax = 10,label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("NK cells")
p5 <- volcano_plot(diff_count_facs,"CD8+ Cytotoxic T",genes$symbol,
betamax = 10,label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("CD8+ T cells")
p6 <- volcano_plot(diff_count_facs,"T cell",genes$symbol,
betamax = 10,label_above_quantile = 0.998,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("T cells")
plot_grid(p1,p2,p3,p4,p5,p6,nrow = 2,ncol = 3)
| Version | Author | Date |
|---|---|---|
| b84788d | Peter Carbonetto | 2021-03-01 |
| dac053f | Peter Carbonetto | 2021-02-28 |
| 5773c6c | Peter Carbonetto | 2021-02-25 |
| ced5127 | Peter Carbonetto | 2021-02-25 |
| d21aab5 | Peter Carbonetto | 2021-02-24 |
| 7be2e95 | Peter Carbonetto | 2021-02-24 |
| 6e2b53a | Peter Carbonetto | 2021-02-10 |
| 9afb462 | Peter Carbonetto | 2021-02-10 |
| f8e1d99 | Peter Carbonetto | 2021-02-10 |
| d1d4185 | Peter Carbonetto | 2021-02-10 |
| f596d8a | Peter Carbonetto | 2021-02-09 |
| d0775d2 | Peter Carbonetto | 2021-02-09 |
| ab3eeb8 | Peter Carbonetto | 2021-01-29 |
| 42ebc62 | Peter Carbonetto | 2021-01-29 |
| a4bc59b | Peter Carbonetto | 2021-01-06 |
| a0b6c2b | Peter Carbonetto | 2021-01-06 |
| 0411340 | Peter Carbonetto | 2021-01-06 |
| fad8e3d | Peter Carbonetto | 2021-01-05 |
Perform differential expression analysis using the clusters:
table(samples$cluster)
diff_count_clusters <- diff_count_clusters(samples$cluster,counts)
#
# B CD14+ CD34+ CD8+ dendritic NK T
# 10439 2956 8237 3757 308 8380 60578
# Fitting 21952 x 7 = 153664 univariate Poisson models.
# Computing log-fold change statistics.
# Stabilizing log-fold change estimates using adaptive shrinkage.
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$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.
These volcano plots summarize the results of the differential expression analysis using the clusters:
p7 <- volcano_plot(diff_count_clusters,"B",genes$symbol,betamax = 10,
label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("B cells")
p8 <- volcano_plot(diff_count_clusters,"CD14+",genes$symbol,betamax = 10,
label_above_quantile = 0.993,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("CD14+ cells")
p9 <- volcano_plot(diff_count_clusters,"CD34+",genes$symbol,betamax = 10,
label_above_lfc = 4,label_above_quantile = 0.99,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("CD34+ cells")
p10 <- volcano_plot(diff_count_clusters,"dendritic",genes$symbol,betamax = 10,
label_above_lfc = 4,label_above_quantile = 0.98,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("dendritic cells")
p11 <- volcano_plot(diff_count_clusters,"NK",genes$symbol,betamax = 10,
label_above_lfc = 2,label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("NK cells")
p12 <- volcano_plot(diff_count_clusters,"CD8+",genes$symbol,betamax = 10,
label_above_lfc = 2,label_above_quantile = 0.98,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("CD8+ T cells")
p13 <- volcano_plot(diff_count_clusters,"T",genes$symbol,betamax = 10,
label_above_lfc = 2,label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("T cells")
plot_grid(p7,p8,p9,p10,p11,p12,p13,nrow = 3,ncol = 3)
| Version | Author | Date |
|---|---|---|
| b84788d | Peter Carbonetto | 2021-03-01 |
| dac053f | Peter Carbonetto | 2021-02-28 |
| 5773c6c | Peter Carbonetto | 2021-02-25 |
| ced5127 | Peter Carbonetto | 2021-02-25 |
| d21aab5 | Peter Carbonetto | 2021-02-24 |
| 7be2e95 | Peter Carbonetto | 2021-02-24 |
| 6e2b53a | Peter Carbonetto | 2021-02-10 |
| 9afb462 | Peter Carbonetto | 2021-02-10 |
| f8e1d99 | Peter Carbonetto | 2021-02-10 |
| d1d4185 | Peter Carbonetto | 2021-02-10 |
| f596d8a | Peter Carbonetto | 2021-02-09 |
| d0775d2 | Peter Carbonetto | 2021-02-09 |
| ab3eeb8 | Peter Carbonetto | 2021-01-29 |
| 42ebc62 | Peter Carbonetto | 2021-01-29 |
| a4bc59b | Peter Carbonetto | 2021-01-06 |
| a0b6c2b | Peter Carbonetto | 2021-01-06 |
| 0411340 | Peter Carbonetto | 2021-01-06 |
| fad8e3d | Peter Carbonetto | 2021-01-05 |
Perform differential expression analysis using the multinomial topic model, after removing the dendritic cells cluster:
rows <- which(samples$cluster != "dendritic")
fit_no_dendritic <- select_loadings(fit,loadings = rows)
diff_count_topics <- diff_count_analysis(fit_no_dendritic,counts[rows,])
# Fitting 21952 x 6 = 131712 univariate Poisson models.
# Computing log-fold change statistics.
# Stabilizing log-fold change estimates using adaptive shrinkage.
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$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
These volcano plots summarize the results of the differential expression analysis using the topic model:
p14 <- volcano_plot(diff_count_topics,"k3",genes$symbol,
label_above_lfc = 4,label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("topic 3 (B cells)")
p15 <- volcano_plot(diff_count_topics,"k2",genes$symbol,
label_above_quantile = 0.994,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("topic 2 (CD14+ cells)")
p16 <- volcano_plot(diff_count_topics,"k5",genes$symbol,
label_above_lfc = 2,label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("topic 5 (CD34+ cells)")
p17 <- volcano_plot(diff_count_topics,"k4",genes$symbol,
label_above_lfc = 4,label_above_quantile = 0.995,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("topic 4 (NK cells)")
p18 <- volcano_plot(diff_count_topics,"k1",genes$symbol,
label_above_quantile = 0.998,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("topic 1 (T cells)")
p19 <- volcano_plot(diff_count_topics,"k6",genes$symbol,
label_above_quantile = 0.998,
subsample_below_quantile = 0.5) +
guides(fill = "none") +
ggtitle("topic 6 (ribosomal proteins)")
plot_grid(p14,p15,p16,p17,p18,p19,nrow = 2,ncol = 3)
| Version | Author | Date |
|---|---|---|
| b84788d | Peter Carbonetto | 2021-03-01 |
| dac053f | Peter Carbonetto | 2021-02-28 |
| 5773c6c | Peter Carbonetto | 2021-02-25 |
| ced5127 | Peter Carbonetto | 2021-02-25 |
| d21aab5 | Peter Carbonetto | 2021-02-24 |
| 7be2e95 | Peter Carbonetto | 2021-02-24 |
| 6e2b53a | Peter Carbonetto | 2021-02-10 |
| 9afb462 | Peter Carbonetto | 2021-02-10 |
| f8e1d99 | Peter Carbonetto | 2021-02-10 |
| d1d4185 | Peter Carbonetto | 2021-02-10 |
| f596d8a | Peter Carbonetto | 2021-02-09 |
| d0775d2 | Peter Carbonetto | 2021-02-09 |
The results of the differential expression analysis can also be browsed in interactive volcano plots:
volcano_plotly(diff_count_topics,"k3",
"volcano_plot_purified_pbmc_bcells.html",
genes$symbol,title = "topic 3 (B cells)",
subsample_below_quantile = 0.5,
width = 450,height = 500)
volcano_plotly(diff_count_topics,"k2",
"volcano_plot_purified_pbmc_cd14.html",
genes$symbol,title = "topic 2 (CD14+)",
subsample_below_quantile = 0.5,
width = 450,height = 500)
volcano_plotly(diff_count_topics,"k5",
"volcano_plot_purified_pbmc_cd34.html",
genes$symbol,title = "topic 5 (CD34+)",
subsample_below_quantile = 0.5,
width = 450,height = 500)
volcano_plotly(diff_count_topics,"k4",
"volcano_plot_purified_pbmc_nk.html",
genes$symbol,title = "topic 4 (NK cells)",
subsample_below_quantile = 0.5,
width = 450,height = 500)
volcano_plotly(diff_count_topics,"k1",
"volcano_plot_purified_pbmc_tcells.html",
genes$symbol,title = "topic 1 (T cells)",
subsample_below_quantile = 0.5,
width = 450,height = 500)
volcano_plotly(diff_count_topics,"k6",
"volcano_plot_purified_pbmc_ribosomal_proteins.html",
genes$symbol,title = "topic 6 (ribosomal proteins)",
subsample_below_quantile = 0.5,
width = 450,height = 500)
volcano_plotly(diff_count_clusters,"CD8+",
"volcano_plot_purified_pbmc_cd8.html",
genes$symbol,title = "CD8+ cluster",
subsample_below_quantile = 0.5,
width = 450,height = 500)
volcano_plotly(diff_count_clusters,"dendritic",
"volcano_plot_purified_pbmc_dendritic.html",
genes$symbol,title = "dendritic cells cluster",
subsample_below_quantile = 0.5,
width = 450,height = 500)The interactive volcano plots may also be viewed by clicking these links: topic 3 (B cells), topic 4 (NK cells), topic 2 (CD14+), topic 5 (CD34+), topics 1 (T cells), topics 6 (T cells), CD8+ cluster and dendritic cells cluster.
The volcano plot for topic 6 suggests an enrichment of ribosomal protein genes. Indeed, there is a very strong correlation between the topic 6 mixture proportion and the fraction of total expression attributed to ribosomal genes:
rpgenes <- c("RPS2","RPS3","RPS3A","RPS4X","RPS6","RPS7","RPS8","RPS9",
"RPS10","RPS11","RPS12","RPS13","RPS14","RPS15","RPS15A",
"RPS16","RPS17","RPS18","RPS19","RPS20","RPS21","RPS23",
"RPS24","RPS25","RPS26","RPS27","RPS27A","RPS28","RPS29",
"RPL3","RPL4","RPL5","RPL6","RPL7A","RPL8","RPL9","RPL10",
"RPL10A","RPL12","RPL13A","RPL14","RPL15","RPL17","RPL18",
"RPL18A","RPL19","RPL21","RPL22","RPL23","RPL23A","RPL24",
"RPL26","RPL27A","RPL30","RPL31","RPL32","RPL34","RPL35",
"RPL36","RPL36A","RPL37","RPL39","RPL41")
rgscatterplot <- function (i, title = NULL) {
j <- which(is.element(genes$symbol,rpgenes))
pdat <- data.frame(x = fit$L[i,6],
y = rowSums(counts[i,j])/rowSums(counts[i,]))
return(ggplot(pdat,aes(x = x,y = y)) +
geom_point() +
geom_smooth(method = "lm",se = FALSE,color = "dodgerblue",
size = 0.5,linetype = "dashed") +
ylim(0,0.6) +
labs(x = "topic 6 proportion",
y = "ribosomal expression",
title = title) +
theme_cowplot(font_size = 10) +
theme(plot.title = element_text(size = 10,face = "plain")))
}
p20 <- rgscatterplot(which(celltype == "CD19+ B"),"B cells")
p21 <- rgscatterplot(which(celltype == "CD34+"),"CD34+ cells")
p22 <- rgscatterplot(which(!(celltype == "CD19+ B" | celltype == "CD34+")),
"all other cells")
plot_grid(p20,p21,p22,nrow = 1,ncol = 3)
The list of ribosomal protein genes comes from Yoshihama et al (2002).
Four out of the 6topics closely correspond to the FACS cell populations: B cells, T cells, NK cells and CD14+ cells. We would expect then that the differential expression in these 4 topics is similar to differential expression in the corresponding FACS populations. Let’s check this.
celltype <- as.character(celltype)
celltype[celltype == "CD8+ Cytotoxic T"] <- "T cell"
celltype <- factor(celltype)
table(celltype)
diff_count_facs <- diff_count_clusters(celltype,counts)
# celltype
# CD14+ Monocyte CD19+ B CD34+ CD56+ NK T cell
# 2612 10085 9232 8385 64341
# Fitting 21952 x 5 = 109760 univariate Poisson models.
# Computing log-fold change statistics.
# Stabilizing log-fold change estimates using adaptive shrinkage.
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$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.
These scatterplots compare log-fold change estimated using the FACS labeling and using the topic model. Some of the top genes in each topic are highlighted.
p23 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD19+ B","k3",
genes$symbol,label_above_lfc = 6,
label_above_quantile = 0.99,
xlab = "B cells FACS subpopulation",ylab = "topic 3")
p24 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD14+ Monocyte",
"k2",genes$symbol,label_above_lfc = 6,
label_above_quantile = 0.98,
xlab = "CD14+ FACS subpopulation",ylab = "topic 2")
p25 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD56+ NK","k4",
genes$symbol,label_above_lfc = 5,
label_above_quantile = 0.99,
xlab = "NK cells FACS subpopulation",ylab = "topic 4")
p26 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"T cell","k1",
genes$symbol,label_above_lfc = 5,
label_above_quantile = 0.99,
xlab = "T cells FACS subpopulation",
ylab = "topic 1")
plot_grid(p23,p24,p25,p26,nrow = 2,ncol = 2)
Overall, the LFC estimates are strongly correlated. But the differential expression in the topic is much higher for many of the top genes. In particular, the topic model seems to be more adept at picking out cell-type-specific genes—that is, genes that are uniquely expressed in a cell type.
For each topic, we also perform a gene-set enrichment analysis. Here we only consider gene sets with at least 4 genes and no more than 400 genes.
data(gene_sets_human)
i <- which(with(gene_sets_human,
colSums(gene_sets) >= 4 &
colSums(gene_sets) <= 400))
gene_set_info <- gene_sets_human$gene_set_info[i,]
gene_sets <- gene_sets_human$gene_sets[,i]
gsea_res <- perform_gsea(gene_sets,diff_count_topics$Z,nproc = 4)
# Using statistics from 17348 genes for gene set enrichment analysis.
# Converting gene set data for fgsea.
# Computing enrichment statistics for 35084 gene sets and
# 6 collections of gene-level statistics:
# k1, k2, k3, k4, k5, k6
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$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.
The results of the gene set enrichment analysis can be explored in these interactive plots.
gsea_plotly(gsea_res,gene_set_info,"k1",file = "gsea_plot_tcells.html",title = "T cells")
gsea_plotly(gsea_res,gene_set_info,"k2",file = "gsea_plot_cd14.html",title = "CD14+ cells")
gsea_plotly(gsea_res,gene_set_info,"k3",file = "gsea_plot_bcells.html",title = "B cells")
gsea_plotly(gsea_res,gene_set_info,"k4",file = "gsea_plot_nk.html",title = "NK cells")
gsea_plotly(gsea_res,gene_set_info,"k5",file = "gsea_plot_cd34.html",title = "CD34+ cells")
gsea_plotly(gsea_res,gene_set_info,"k6",file = "gsea_plot_rp.html",title = "Ribosomal proteins")The interactive volcano plots may also be viewed by clicking these links: topic 1 (T cells), topic 2 (CD14+ cells), topic 3 (B cells), topic 4 (NK cells), topic 5 (CD34+ cells) and topic 6 (ribosomal proteins).
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] ggrepel_0.9.0 cowplot_1.0.0 ggplot2_3.3.0 pathways_0.1-20
# [5] fastTopics_0.5-20 Matrix_1.2-18
#
# loaded via a namespace (and not attached):
# [1] httr_1.4.2 tidyr_1.0.0 splines_3.6.2
# [4] jsonlite_1.6 viridisLite_0.3.0 RcppParallel_4.4.2
# [7] shiny_1.4.0 assertthat_0.2.1 mixsqp_0.3-44
# [10] yaml_2.2.0 progress_1.2.2 pillar_1.4.3
# [13] backports_1.1.5 lattice_0.20-38 quantreg_5.54
# [16] glue_1.3.1 quadprog_1.5-8 digest_0.6.23
# [19] promises_1.1.0 colorspace_1.4-1 htmltools_0.4.0
# [22] httpuv_1.5.2 pkgconfig_2.0.3 invgamma_1.1
# [25] SparseM_1.78 xtable_1.8-4 purrr_0.3.3
# [28] scales_1.1.0 whisker_0.4 later_1.0.0
# [31] Rtsne_0.15 BiocParallel_1.18.1 MatrixModels_0.4-1
# [34] git2r_0.26.1 tibble_2.1.3 mgcv_1.8-31
# [37] farver_2.0.1 withr_2.1.2 ashr_2.2-51
# [40] lazyeval_0.2.2 mime_0.8 magrittr_1.5
# [43] crayon_1.3.4 mcmc_0.9-6 evaluate_0.14
# [46] fs_1.3.1 nlme_3.1-142 MASS_7.3-51.4
# [49] truncnorm_1.0-8 tools_3.6.2 data.table_1.12.8
# [52] prettyunits_1.1.1 hms_0.5.2 lifecycle_0.1.0
# [55] stringr_1.4.0 MCMCpack_1.4-5 plotly_4.9.2
# [58] munsell_0.5.0 irlba_2.3.3 compiler_3.6.2
# [61] rlang_0.4.5 grid_3.6.2 htmlwidgets_1.5.1
# [64] crosstalk_1.0.0 labeling_0.3 rmarkdown_2.3
# [67] gtable_0.3.0 R6_2.4.1 gridExtra_2.3
# [70] knitr_1.26 dplyr_0.8.3 fastmap_1.0.1
# [73] zeallot_0.1.0 fastmatch_1.1-0 workflowr_1.6.2.9000
# [76] fgsea_1.15.1 rprojroot_1.3-2 stringi_1.4.3
# [79] parallel_3.6.2 SQUAREM_2017.10-1 Rcpp_1.0.5
# [82] vctrs_0.2.1 tidyselect_0.2.5 xfun_0.11
# [85] coda_0.19-3