Analysis of Buenrostro et al (2018) scATAC-seq data (original data, bulk peaks) using fastTopics with K = 10

Last updated: 2022-03-11

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Knit directory: scATACseq-topics/

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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.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 465536 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  83 198 154

Differential accessibility (DA) analysis

DA analysis results with "ash" shrinkage (10000 MCMC iterations)

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_ash_10000iters.rds"))
summary(DA_res)

dim(DA_res$z)
#          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- numeric
# [1] 465536     10

Volcano 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            6          14         17         25         40
# topic 4           59          88        113        152        197
# topic 5           25          27         27         29         32
# topic 6            0           0          0          0          0
# topic 7            1           2          2          5          9
# topic 8            2           3          4          4          4
# topic 9            0           0          0          0          0
# topic 10           2           3          6          8         15

DA analysis results without shrinkage (10000 MCMC iterations)

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_noshrinkage_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
# lpval    4655360 -none- numeric
# svalue         1 -none- numeric
# lfsr           1 -none- numeric
# F        4655360 -none- numeric
# f0        465536 -none- numeric