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

Last updated: 2022-03-03

Checks: 7 0

Knit directory: scATACseq-topics/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it's best to always run the code in an empty environment.

Seed: set.seed(20200729)

The command set.seed(20200729) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 5c64c9d

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 5c64c9d. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/motif_analysis_Buenrostro2018_v2.Rmd
    Untracked:  output/clustering-Cusanovich2018.rds
    Untracked:  paper/

Unstaged changes:
    Modified:   analysis/assess_fits_Buenrostro2018_Chen2019pipeline.Rmd
    Modified:   analysis/clusters_Cusanovich2018_k13.Rmd
    Modified:   analysis/gene_analysis_Buenrostro2018_Chen2019pipeline.Rmd
    Modified:   analysis/gene_analysis_Cusanovich2018.Rmd
    Modified:   analysis/motif_analysis_Buenrostro2018_Chen2019pipeline.Rmd
    Modified:   analysis/motif_analysis_Cusanovich2018.Rmd
    Modified:   analysis/plots_Cusanovich2018.Rmd
    Modified:   scripts/postfit_Buenrostro2018_v2.sh
    Modified:   scripts/postfit_motif_analysis.R

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
html	5c64c9d	kevinlkx	2022-03-03	Build site.
Rmd	e32503a	kevinlkx	2022-03-03	added results without shrinkage
html	9164260	kevinlkx	2022-03-02	Build site.
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)

Version	Author	Date
9164260	kevinlkx	2022-03-02
5c18b15	kevinlkx	2022-03-02
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 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
9164260	kevinlkx	2022-03-02
5c18b15	kevinlkx	2022-03-02
c4a7131	kevinlkx	2022-02-24

#   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)
#          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

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)

Version	Author	Date
5c64c9d	kevinlkx	2022-03-03

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