Analysis of Buenrostro et al (2018) scATAC-seq data using fastTopics with K = 10
Kaixuan Luo
Last updated: 2022-02-24
Checks: 7 0
Knit directory: scATACseq-topics/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/analysis_Buenrostro2018_k10.Rmd) and HTML (docs/analysis_Buenrostro2018_k10.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a3b3555 | kevinlkx | 2022-02-24 | structure plot and DA analysis with k=10 |
| html | c4a7131 | kevinlkx | 2022-02-24 | Build site. |
| Rmd | 771b255 | kevinlkx | 2022-02-24 | structure plot and DA regions with k=10 |
Here we explore the structure in the Buenrostro et al (2018) scATAC-seq data inferred from the multinomial topic model with \(k = 10\).
Load packages and some functions used in this analysis.
library(fastTopics)
library(Matrix)
library(dplyr)
library(ggplot2)
library(cowplot)
library(plyr)
library(dplyr)
library(RColorBrewer)
library(DT)
library(reshape)
source("code/plots.R")Load the data.
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data_Chen2019pipeline/"
load(file.path(data.dir, "Buenrostro_2018_binarized_counts.RData"))
cat(sprintf("%d x %d counts matrix.\n",nrow(counts),ncol(counts)))
samples$cell <- rownames(samples)
samples$label <- as.factor(samples$label)
# 2034 x 101172 counts matrix.Load the K = 10 multinomial topic model fit.
fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/"
fit <- readRDS(file.path(fit.dir, "/fit-Buenrostro2018-binarized-scd-k=10.rds"))$fit
fit <- poisson2multinom(fit)Structure plots
The structure plots below summarize the topic proportions in the samples grouped by different tissues.
Visualize by Structure plot grouped by tissues
set.seed(1)
labels <- factor(samples$label, levels = c("HSC", "MPP", "CMP", "GMP", "mono", "MEP", "LMPP", "CLP", "pDC", "UNK"))
topic_colors <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a")
# labels <- factor(samples$label,c("mono","pDC","MEP","HSC","MPP","CLP",
# "LMPP","CMP","GMP","UNK"))
# topic_colors <- c("darkorange","limegreen","magenta","gold","skyblue",
# "darkblue","dodgerblue","darkmagenta","red","olivedrab")
structure_plot(fit,grouping = labels,colors = topic_colors,
topics = 1:10,
gap = 20,perplexity = 50,verbose = FALSE)
| Version | Author | Date |
|---|---|---|
| c4a7131 | kevinlkx | 2022-02-24 |
k-means clustering on topic proportions
Define clusters using k-means, and then create structure plot based on the clusters from k-means.
k-means clustering (using 10 clusters) on topic proportions
set.seed(1)
clusters <- factor(kmeans(fit$L,centers = 10,iter.max = 100)$cluster)
summary(clusters)
structure_plot(fit,grouping = clusters,colors = topic_colors,
gap = 20,perplexity = 50,verbose = FALSE)
| Version | Author | Date |
|---|---|---|
| c4a7131 | kevinlkx | 2022-02-24 |
# 1 2 3 4 5 6 7 8 9 10
# 236 293 277 148 155 190 133 180 352 70k-means clustering (using 12 clusters) on topic proportions
set.seed(1)
clusters <- factor(kmeans(fit$L,centers = 12,iter.max = 100)$cluster)
summary(clusters)
structure_plot(fit,grouping = clusters,colors = topic_colors,
gap = 20,perplexity = 50,verbose = FALSE)
| Version | Author | Date |
|---|---|---|
| c4a7131 | kevinlkx | 2022-02-24 |
# 1 2 3 4 5 6 7 8 9 10 11 12
# 220 251 262 146 68 185 164 63 199 66 280 130Different accessibility (DA) analysis
Load DA analysis results (10000 iterations of MCMC)
postfit_dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2/"
DA_res <- readRDS(file.path(postfit_dir, "DAanalysis-Buenrostro2018-k=10/DA_regions_topics_10000iters.rds"))
summary(DA_res)
# Length Class Mode
# ar 1011720 -none- numeric
# est 1011720 -none- numeric
# postmean 1011720 -none- numeric
# lower 1011720 -none- numeric
# upper 1011720 -none- numeric
# z 1011720 -none- numeric
# lfsr 1011720 -none- numeric
# lpval 1 -none- numeric
# svalue 1011720 -none- numeric
# ash 3 ash list
# F 1011720 -none- numeric
# f0 101172 -none- numericVolcano plot for DA analysis results for topic 2
volcano_plot(DA_res,k = 2,labels = rep("",nrow(DA_res$z)))
| Version | Author | Date |
|---|---|---|
| c4a7131 | kevinlkx | 2022-02-24 |
Volcano plot for DA analysis results for topic 4
volcano_plot(DA_res,k = 4,labels = rep("",nrow(DA_res$z)))
| Version | Author | Date |
|---|---|---|
| c4a7131 | kevinlkx | 2022-02-24 |
all topics
plots <- vector("list",10)
names(plots) <- 1:10
for (k in 1:10)
plots[[k]] <- volcano_plot(DA_res, k, labels = rep("",nrow(DA_res$z)))
do.call(plot_grid,plots)
Number of regions selected at different lfsr cutoffs:
for(k in 1:10){
lfsr <- DA_res$lfsr[,k]
cat("Topic", k, ":\n")
cat(" ", length(which(lfsr < 0.01)), "regions selected with lfsr < 0.01. \n")
cat(" ", length(which(lfsr < 0.05)), "regions selected with lfsr < 0.05. \n")
cat(" ", length(which(lfsr < 0.1)), "regions selected with lfsr < 0.1. \n")
cat(" ", length(which(lfsr < 0.2)), "regions selected with lfsr < 0.2. \n")
}
# Topic 1 :
# 1 regions selected with lfsr < 0.01.
# 4 regions selected with lfsr < 0.05.
# 4 regions selected with lfsr < 0.1.
# 7 regions selected with lfsr < 0.2.
# Topic 2 :
# 23 regions selected with lfsr < 0.01.
# 46 regions selected with lfsr < 0.05.
# 68 regions selected with lfsr < 0.1.
# 104 regions selected with lfsr < 0.2.
# Topic 3 :
# 40 regions selected with lfsr < 0.01.
# 72 regions selected with lfsr < 0.05.
# 99 regions selected with lfsr < 0.1.
# 154 regions selected with lfsr < 0.2.
# Topic 4 :
# 164 regions selected with lfsr < 0.01.
# 259 regions selected with lfsr < 0.05.
# 336 regions selected with lfsr < 0.1.
# 437 regions selected with lfsr < 0.2.
# Topic 5 :
# 38 regions selected with lfsr < 0.01.
# 60 regions selected with lfsr < 0.05.
# 76 regions selected with lfsr < 0.1.
# 103 regions selected with lfsr < 0.2.
# Topic 6 :
# 50 regions selected with lfsr < 0.01.
# 74 regions selected with lfsr < 0.05.
# 95 regions selected with lfsr < 0.1.
# 138 regions selected with lfsr < 0.2.
# Topic 7 :
# 939 regions selected with lfsr < 0.01.
# 1294 regions selected with lfsr < 0.05.
# 1524 regions selected with lfsr < 0.1.
# 1861 regions selected with lfsr < 0.2.
# Topic 8 :
# 2 regions selected with lfsr < 0.01.
# 3 regions selected with lfsr < 0.05.
# 3 regions selected with lfsr < 0.1.
# 4 regions selected with lfsr < 0.2.
# Topic 9 :
# 3 regions selected with lfsr < 0.01.
# 5 regions selected with lfsr < 0.05.
# 5 regions selected with lfsr < 0.1.
# 5 regions selected with lfsr < 0.2.
# Topic 10 :
# 8 regions selected with lfsr < 0.01.
# 10 regions selected with lfsr < 0.05.
# 10 regions selected with lfsr < 0.1.
# 11 regions selected with lfsr < 0.2.
sessionInfo()
# R version 4.0.4 (2021-02-15)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Scientific Linux 7.4 (Nitrogen)
#
# Matrix products: default
# BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.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] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] reshape_0.8.8 DT_0.20 RColorBrewer_1.1-2 plyr_1.8.6
# [5] cowplot_1.1.1 ggplot2_3.3.5 dplyr_1.0.8 Matrix_1.4-0
# [9] fastTopics_0.6-97 workflowr_1.7.0
#
# loaded via a namespace (and not attached):
# [1] Rtsne_0.15 colorspace_2.0-3 ellipsis_0.3.2
# [4] class_7.3-20 rprojroot_2.0.2 fs_1.5.2
# [7] rstudioapi_0.13 farver_2.1.0 listenv_0.8.0
# [10] MatrixModels_0.5-0 ggrepel_0.9.1 prodlim_2019.11.13
# [13] fansi_1.0.2 lubridate_1.8.0 codetools_0.2-18
# [16] splines_4.0.4 knitr_1.37 jsonlite_1.7.3
# [19] pROC_1.18.0 mcmc_0.9-7 caret_6.0-90
# [22] ashr_2.2-47 uwot_0.1.11 compiler_4.0.4
# [25] httr_1.4.2 assertthat_0.2.1 fastmap_1.1.0
# [28] lazyeval_0.2.2 cli_3.2.0 later_1.3.0
# [31] prettyunits_1.1.1 htmltools_0.5.2 quantreg_5.86
# [34] tools_4.0.4 coda_0.19-4 gtable_0.3.0
# [37] glue_1.6.2 reshape2_1.4.4 Rcpp_1.0.8
# [40] jquerylib_0.1.4 vctrs_0.3.8 nlme_3.1-155
# [43] conquer_1.2.1 iterators_1.0.13 timeDate_3043.102
# [46] gower_0.2.2 xfun_0.29 stringr_1.4.0
# [49] globals_0.14.0 ps_1.6.0 lifecycle_1.0.1
# [52] irlba_2.3.5 future_1.23.0 getPass_0.2-2
# [55] MASS_7.3-55 scales_1.1.1 ipred_0.9-12
# [58] hms_1.1.1 promises_1.2.0.1 parallel_4.0.4
# [61] SparseM_1.81 yaml_2.2.2 pbapply_1.5-0
# [64] sass_0.4.0 rpart_4.1-15 stringi_1.7.6
# [67] SQUAREM_2021.1 highr_0.9 foreach_1.5.1
# [70] lava_1.6.10 truncnorm_1.0-8 rlang_1.0.1
# [73] pkgconfig_2.0.3 matrixStats_0.61.0 evaluate_0.14
# [76] lattice_0.20-45 invgamma_1.1 purrr_0.3.4
# [79] labeling_0.4.2 recipes_0.1.17 htmlwidgets_1.5.4
# [82] processx_3.5.2 tidyselect_1.1.2 parallelly_1.30.0
# [85] magrittr_2.0.2 R6_2.5.1 generics_0.1.2
# [88] DBI_1.1.2 pillar_1.7.0 whisker_0.4
# [91] withr_2.4.3 survival_3.2-13 mixsqp_0.3-43
# [94] nnet_7.3-17 tibble_3.1.6 future.apply_1.8.1
# [97] crayon_1.5.0 utf8_1.2.2 plotly_4.10.0
# [100] rmarkdown_2.11 progress_1.2.2 grid_4.0.4
# [103] data.table_1.14.2 callr_3.7.0 git2r_0.29.0
# [106] ModelMetrics_1.2.2.2 digest_0.6.29 tidyr_1.1.4
# [109] httpuv_1.6.5 MCMCpack_1.6-0 RcppParallel_5.1.5
# [112] stats4_4.0.4 munsell_0.5.0 viridisLite_0.4.0
# [115] bslib_0.3.1 quadprog_1.5-8