Analysis of Buenrostro et al (2018) scATAC-seq data (original data, bulk peaks) using fastTopics with K = 10
Kaixuan Luo
Last updated: 2022-03-02
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Knit directory: scATACseq-topics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7f5ca24 | kevinlkx | 2022-03-02 | updated with original data with peaks called from bulk samples |
| html | 5c18b15 | kevinlkx | 2022-03-02 | Build site. |
| Rmd | 6779e04 | kevinlkx | 2022-03-02 | updated with original data with peaks called from bulk samples |
| html | fe19ea8 | kevinlkx | 2022-02-24 | Build site. |
| 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 downloaded from original paper.
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data/"
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$label)
# 2953 x 491437 counts matrix.Topic model fit
Load the K = 10 topic model fit to the data downloaded from the original paper.
fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/"
fit <- readRDS(file.path(fit.dir, "/fit-Buenrostro2018-binarized-scd-ex-k=10.rds"))$fit
fit <- poisson2multinom(fit)Structure plot
topic_colors <- c("darkorange","limegreen","magenta","gold","skyblue",
"darkblue","dodgerblue","darkmagenta","red","olivedrab")
set.seed(1)
# labels <- factor(samples$label, levels = c("HSC", "MPP", "CMP", "GMP", "mono", "MEP", "LMPP", "CLP", "pDC", "UNK"))
labels <- factor(samples$label, c("mono","pDC","MEP","HSC","MPP","CLP",
"LMPP","CMP","GMP","UNK"))
structure_plot(fit,grouping = labels,colors = topic_colors,
# topics = 1:10,
gap = 20,perplexity = 50,verbose = FALSE)
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 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)
# 1 2 3 4 5 6 7 8 9 10 11 12
# 303 214 182 159 89 241 156 147 108 83 198 154Differential accessibility (DA) analysis
Load DA analysis results (10000 iterations of MCMC)
postfit_dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/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 4655360 -none- numeric
# est 4655360 -none- numeric
# postmean 4655360 -none- numeric
# lower 4655360 -none- numeric
# upper 4655360 -none- numeric
# z 4655360 -none- numeric
# lfsr 4655360 -none- numeric
# lpval 1 -none- numeric
# svalue 4655360 -none- numeric
# ash 3 ash list
# F 4655360 -none- numeric
# f0 465536 -none- numericVolcano plots for the regions
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$z)))
do.call(plot_grid,plots)
Number of regions selected at different lfsr cutoffs:
sig_regions <- matrix(NA, nrow = 10, ncol = 5)
colnames(sig_regions) <- c("lfsr < 0.01", "lfsr < 0.05", "lfsr < 0.1", "lfsr < 0.2", "lfsr < 0.3")
rownames(sig_regions) <- paste("topic", 1:nrow(sig_regions))
for(k in 1:10){
lfsr <- DA_res$lfsr[,k]
sig_regions[k, ] <- c(length(which(lfsr < 0.01)), length(which(lfsr < 0.05)),
length(which(lfsr < 0.1)), length(which(lfsr < 0.2)),
length(which(lfsr < 0.3)))
}
sig_regions
# lfsr < 0.01 lfsr < 0.05 lfsr < 0.1 lfsr < 0.2 lfsr < 0.3
# topic 1 0 0 0 0 0
# topic 2 0 0 0 0 0
# topic 3 7 12 15 19 34
# topic 4 53 91 109 145 185
# topic 5 30 33 34 35 36
# topic 6 0 0 0 0 0
# topic 7 3 3 5 6 8
# topic 8 2 2 3 4 5
# topic 9 0 0 0 0 0
# topic 10 2 3 6 7 11
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
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# [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
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# [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
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