Annotate topics and clusters in mixture of FACS-purified PBMC data
Peter Carbonetto
Last updated: 2021-01-06
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
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| 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. |
Add text here.
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"))$fit
fit <- poisson2multinom(fit)
samples <- readRDS("../output/pbmc-purified/clustering-pbmc-purified.rds")Code used temporarily for testing:
rows <- sample(1:94655,9000)
counts <- counts[rows,]
samples <- samples[rows,]
fit <- select_loadings(fit,loadings = rows)Perform differential expression analysis using the FACS labeling.
celltype <- as.character(samples$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 7.92 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 14.43 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 10.25 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 7.02 seconds.Add text here.
p1 <- volcano_plot_better(diff_count_topics,"k3",genes$symbol,0.998,
"topic 3 (B cells)",show_legend = TRUE)
print(p1)
| Version | Author | Date |
|---|---|---|
| fad8e3d | Peter Carbonetto | 2021-01-05 |
Add text here.
p2 <- volcano_plot_better(diff_count_topics,"k3",genes$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$symbol,0.995,
"topic 1 (CD4+/CD8+)")
p8 <- volcano_plot_better(diff_count_clusters,"CD8+",genes$symbol,0.998,
"CD8+ cluster") + xlim(-11,11)
p9 <- volcano_plot_better(diff_count_clusters,"dendritic",genes$symbol,0.997,
"dendritic cells cluster")
plot_grid(p2,p3,p4,p5,p6,p7,p8,p9,nrow = 3,ncol = 3)
| Version | Author | Date |
|---|---|---|
| fad8e3d | Peter Carbonetto | 2021-01-05 |
Add text here.
p10 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD19+ B","k3",
genes$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)
TO DO:
Save images to files.
Create interactive volcano plots.
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):
# [1] Rcpp_1.0.5 lattice_0.20-38 tidyr_1.0.0
# [4] prettyunits_1.1.1 assertthat_0.2.1 zeallot_0.1.0
# [7] rprojroot_1.3-2 digest_0.6.23 R6_2.4.1
# [10] backports_1.1.5 MatrixModels_0.4-1 evaluate_0.14
# [13] coda_0.19-3 httr_1.4.2 pillar_1.4.3
# [16] rlang_0.4.5 progress_1.2.2 lazyeval_0.2.2
# [19] data.table_1.12.8 irlba_2.3.3 SparseM_1.78
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# [28] munsell_0.5.0 compiler_3.6.2 httpuv_1.5.2
# [31] xfun_0.11 pkgconfig_2.0.3 mcmc_0.9-6
# [34] htmltools_0.4.0 tidyselect_0.2.5 tibble_2.1.3
# [37] workflowr_1.6.2.9000 quadprog_1.5-8 viridisLite_0.3.0
# [40] crayon_1.3.4 dplyr_0.8.3 withr_2.1.2
# [43] later_1.0.0 MASS_7.3-51.4 grid_3.6.2
# [46] jsonlite_1.6 gtable_0.3.0 lifecycle_0.1.0
# [49] git2r_0.26.1 magrittr_1.5 scales_1.1.0
# [52] RcppParallel_4.4.2 stringi_1.4.3 farver_2.0.1
# [55] fs_1.3.1 promises_1.1.0 vctrs_0.2.1
# [58] tools_3.6.2 glue_1.3.1 purrr_0.3.3
# [61] hms_0.5.2 yaml_2.2.0 colorspace_1.4-1
# [64] plotly_4.9.2 knitr_1.26 quantreg_5.54
# [67] MCMCpack_1.4-5