Visualize and interpret topics in Cusanovich2018 data
Kaixuan Luo
Last updated: 2020-09-10
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Knit directory: scATACseq-topics/
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Here we examine and compare the topic modeling results for the scATAC-seq dataset from the mouse single-cell atlas paper Cusanovich et al (2018)
Load the packages used in the analysis below, as well as additional functions that will be used to generate some of the plots.
library(tools)
library(dplyr)
library(fastTopics)
library(ggplot2)
library(cowplot)
source("code/plots.R")set.seed(1)Cusanovich 2018 mouse scATAC-seq dataset
Load the data and Poisson NMF model fit
Load the data
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Cusanovich_2018/processed_data/"
load(file.path(data.dir, "Cusanovich_2018.RData"))
cat(sprintf("%d x %d counts matrix.\n",nrow(counts),ncol(counts)))
rm(counts)
# 81173 x 436206 counts matrix.About the samples: The study measured single cell chromatin accessibility for 17 samples spanning 13 different tissues in 8-week old mice.
cat(nrow(samples), "samples (cells). \n")
# 81173 samples (cells).Tissues:
samples$tissue <- as.factor(samples$tissue)
cat(length(levels(samples$tissue)), "tissues. \n")
table(samples$tissue)
# 13 tissues.
#
# BoneMarrow Cerebellum Heart Kidney
# 8403 2278 7650 6431
# LargeIntestine Liver Lung PreFrontalCortex
# 7086 6167 9996 5959
# SmallIntestine Spleen Testes Thymus
# 4077 4020 2723 7617
# WholeBrain
# 8766Cell types labels:
samples$cell_label <- as.factor(samples$cell_label)
cat(length(levels(samples$cell_label)), "cell types \n")
table(samples$cell_label)
# 40 cell types
#
# Activated B cells Alveolar macrophages
# 500 559
# Astrocytes B cells
# 1666 5772
# Cardiomyocytes Cerebellar granule cells
# 4076 4099
# Collecting duct Collisions
# 164 1218
# DCT/CD Dendritic cells
# 506 958
# Distal convoluted tubule Endothelial I (glomerular)
# 319 552
# Endothelial I cells Endothelial II cells
# 952 3019
# Enterocytes Erythroblasts
# 4783 2811
# Ex. neurons CPN Ex. neurons CThPN
# 1832 1540
# Ex. neurons SCPN Hematopoietic progenitors
# 1466 3425
# Hepatocytes Immature B cells
# 5664 571
# Inhibitory neurons Loop of henle
# 1828 815
# Macrophages Microglia
# 711 422
# Monocytes NK cells
# 1173 321
# Oligodendrocytes Podocytes
# 1558 498
# Proximal tubule Proximal tubule S3
# 2570 594
# Purkinje cells Regulatory T cells
# 320 507
# SOM+ Interneurons Sperm
# 553 2089
# T cells Type I pneumocytes
# 8954 1622
# Type II pneumocytes Unknown
# 157 10029Load the results of running fit_poisson_nmf on the Cusanovich2018 data, with different algorithms, and for various choices of \(k\) (the number of “topics”).
out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
load(file.path(out.dir, "/compiled.fits.Cusanovich2018.RData"))Explore the structure of the single-cell data as inferred by the topic model.
Structure plot
The structure plots below summarize the topic proportions in the samples grouped by different tissues.
\(k = 2\):
fit_poisson_nmf <- fits[["fit-Cusanovich2018-scd-ex-k=2"]]
p.structure_plot <- structure_plot(poisson2multinom(fit_poisson_nmf),
grouping = samples[,"tissue"],
n = 2000,gap = 40,num_threads = 4,verbose = FALSE)
print(p.structure_plot)
\(k = 3\):
fit_poisson_nmf <- fits[["fit-Cusanovich2018-scd-ex-k=3"]]
p.structure_plot <- structure_plot(poisson2multinom(fit_poisson_nmf),
grouping = samples[,"tissue"],
n = 2000,gap = 40,num_threads = 4,verbose = FALSE)
print(p.structure_plot)
\(k = 4\):
fit_poisson_nmf <- fits[["fit-Cusanovich2018-scd-ex-k=4"]]
p.structure_plot <- structure_plot(poisson2multinom(fit_poisson_nmf),
grouping = samples[,"tissue"],
n = 2000,gap = 40,num_threads = 4,verbose = FALSE)
print(p.structure_plot)
\(k = 5\):
fit_poisson_nmf <- fits[["fit-Cusanovich2018-scd-ex-k=5"]]
p.structure_plot <- structure_plot(poisson2multinom(fit_poisson_nmf),
grouping = samples[,"tissue"],
n = 2000,gap = 40,num_threads = 4,verbose = FALSE)
print(p.structure_plot)
\(k = 6\):
fit_poisson_nmf <- fits[["fit-Cusanovich2018-scd-ex-k=6"]]
p.structure_plot <- structure_plot(poisson2multinom(fit_poisson_nmf),
grouping = samples[,"tissue"],
n = 2000,gap = 40,num_threads = 4,verbose = FALSE)
print(p.structure_plot)
\(k = 7\):
fit_poisson_nmf <- fits[["fit-Cusanovich2018-scd-ex-k=7"]]
p.structure_plot <- structure_plot(poisson2multinom(fit_poisson_nmf),
grouping = samples[,"tissue"],
n = 2000,gap = 40,num_threads = 4,verbose = FALSE)
print(p.structure_plot)
\(k = 8\):
fit_poisson_nmf <- fits[["fit-Cusanovich2018-scd-ex-k=8"]]
p.structure_plot <- structure_plot(poisson2multinom(fit_poisson_nmf),
grouping = samples[,"tissue"],
n = 2000,gap = 40,num_threads = 4,verbose = FALSE)
print(p.structure_plot)
\(k = 9\):
fit_poisson_nmf <- fits[["fit-Cusanovich2018-scd-ex-k=9"]]
p.structure_plot <- structure_plot(poisson2multinom(fit_poisson_nmf),
grouping = samples[,"tissue"],
n = 2000,gap = 40,num_threads = 4,verbose = FALSE)
print(p.structure_plot)
\(k = 10\):
fit_poisson_nmf <- fits[["fit-Cusanovich2018-scd-ex-k=10"]]
p.structure_plot <- structure_plot(poisson2multinom(fit_poisson_nmf),
grouping = samples[,"tissue"],
n = 2000,gap = 40,num_threads = 4,verbose = FALSE)
print(p.structure_plot)
sessionInfo()
# R version 3.5.1 (2018-07-02)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Scientific Linux 7.4 (Nitrogen)
#
# Matrix products: default
# BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
#
# locale:
# [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
# [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
# [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
# [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
# [9] LC_ADDRESS=C LC_TELEPHONE=C
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#
# attached base packages:
# [1] tools stats graphics grDevices utils datasets methods
# [8] base
#
# other attached packages:
# [1] cowplot_1.0.0 ggplot2_3.3.0 fastTopics_0.3-163 dplyr_0.8.5
# [5] workflowr_1.6.2
#
# loaded via a namespace (and not attached):
# [1] progress_1.2.2 tidyselect_0.2.5 xfun_0.14 purrr_0.3.4
# [5] lattice_0.20-38 colorspace_1.4-1 vctrs_0.3.0 viridisLite_0.3.0
# [9] htmltools_0.4.0 yaml_2.2.0 MCMCpack_1.4-4 plotly_4.8.0
# [13] rlang_0.4.6 later_1.0.0 pillar_1.4.4 withr_2.1.2
# [17] glue_1.4.1 lifecycle_0.2.0 stringr_1.4.0 MatrixModels_0.4-1
# [21] munsell_0.5.0 gtable_0.3.0 htmlwidgets_1.5.1 coda_0.19-2
# [25] evaluate_0.14 labeling_0.3 knitr_1.28 SparseM_1.77
# [29] httpuv_1.5.3.1 quantreg_5.36 irlba_2.3.3 Rcpp_1.0.4.6
# [33] promises_1.1.0 backports_1.1.7 scales_1.1.1 RcppParallel_4.4.3
# [37] jsonlite_1.6 farver_2.0.3 fs_1.3.1 mcmc_0.9-7
# [41] hms_0.4.2 digest_0.6.25 stringi_1.4.6 Rtsne_0.15
# [45] ggrepel_0.8.2 grid_3.5.1 rprojroot_1.3-2 quadprog_1.5-5
# [49] magrittr_1.5 lazyeval_0.2.2 tibble_3.0.1 tidyr_0.8.3
# [53] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3 MASS_7.3-51.6
# [57] ellipsis_0.3.1 Matrix_1.2-15 prettyunits_1.1.1 data.table_1.12.8
# [61] assertthat_0.2.1 rmarkdown_2.1 httr_1.4.1 R6_2.4.1
# [65] git2r_0.27.1 compiler_3.5.1