Last updated: 2022-03-29

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Rmd 181b83b kevinlkx 2022-03-29 added structure plots for results with k = 8 and 9

Here we explore the structure in the Buenrostro et al (2018) scATAC-seq data inferred from the multinomial topic model.

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.

Original data

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.

Filtered out peaks with accessbility in fewer than 20 cells

i      <- which(colSums(counts) >= 20)
peaks  <- peaks[i,]
counts <- counts[,i]
cat(sprintf("After filtering, we have %d rows and %d columns left. \n",nrow(counts),ncol(counts)))

Load data after filtering peaks with accessbility in fewer than 20 cells

res.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/filtered_peaks"
load(file.path(res.dir, "Buenrostro_2018_binarized_filtered.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 126719 counts matrix.

Topic model fit

K = 8

Load the K = 8 topic model fit

fit <- readRDS(file.path(res.dir, "/fit-Buenrostro2018-binarized-filtered-scd-ex-k=8.rds"))$fit
fit <- poisson2multinom(fit)

Structure plot

topic_colors <- c("darkorange","limegreen","magenta","gold","skyblue",
                  "darkblue","dodgerblue","darkmagenta")

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 = 9

Load the K = 9 topic model fit.

fit <- readRDS(file.path(res.dir, "/fit-Buenrostro2018-binarized-filtered-scd-ex-k=9.rds"))$fit
fit <- poisson2multinom(fit)

Structure plot

topic_colors <- c("darkorange","limegreen","magenta","gold","skyblue",
                  "darkblue","dodgerblue","darkmagenta","red")

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 = 10

Load the K = 10 topic model fit.

fit <- readRDS(file.path(res.dir, "/fit-Buenrostro2018-binarized-filtered-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 
# 262 244 125 132 150 179 180 153  89  72 257 191

Differential accessibility (DA) analysis on topic modeling result with \(K = 10\)

DA analysis results without shrinkage (10000 MCMC iterations)

DA_dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/filtered_peaks/DAanalysis-Buenrostro2018-k=10"

DA_res <- readRDS(file.path(DA_dir, "DA_regions_topics_vsnull_noshrinkage_10000iters.rds"))
summary(DA_res)
#          Length  Class  Mode   
# ar       1267190 -none- numeric
# est      1267190 -none- numeric
# postmean 1267190 -none- numeric
# lower    1267190 -none- numeric
# upper    1267190 -none- numeric
# z        1267190 -none- numeric
# lpval    1267190 -none- numeric
# svalue         1 -none- numeric
# lfsr           1 -none- numeric
# F        1267190 -none- numeric
# f0        126719 -none- numeric

Number of regions selected at different p-value cutoffs:

sig_regions <- matrix(NA, nrow = 10, ncol = 3)
colnames(sig_regions) <- c("p < 0.01", "p < 0.05", "p < 0.1")
rownames(sig_regions) <- paste("topic", 1:nrow(sig_regions))

for(k in 1:10){
  lpval <- DA_res$lpval[,k]
  pval <- 10^(-lpval)
  sig_regions[k, ] <- c(length(which(pval < 0.01)), length(which(pval < 0.05)), length(which(pval < 0.1)))
}

sig_regions
#          p < 0.01 p < 0.05 p < 0.1
# topic 1     15065    21923   28375
# topic 2     12633    20427   27623
# topic 3     16774    23671   29901
# topic 4     15825    20964   25905
# topic 5     15603    21209   24833
# topic 6      6986     9405   11438
# topic 7     14769    20177   24667
# topic 8     12989    20187   26041
# topic 9     15614    20703   24928
# topic 10     9455    14334   17737

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)))
# lfsr is not available, probably because "shrink.method" was not set to "ash"; lfsr in plot should be ignored
# lfsr is not available, probably because "shrink.method" was not set to "ash"; lfsr in plot should be ignored
# lfsr is not available, probably because "shrink.method" was not set to "ash"; lfsr in plot should be ignored
# lfsr is not available, probably because "shrink.method" was not set to "ash"; lfsr in plot should be ignored
# lfsr is not available, probably because "shrink.method" was not set to "ash"; lfsr in plot should be ignored
# lfsr is not available, probably because "shrink.method" was not set to "ash"; lfsr in plot should be ignored
# lfsr is not available, probably because "shrink.method" was not set to "ash"; lfsr in plot should be ignored
# lfsr is not available, probably because "shrink.method" was not set to "ash"; lfsr in plot should be ignored
# lfsr is not available, probably because "shrink.method" was not set to "ash"; lfsr in plot should be ignored
# lfsr is not available, probably because "shrink.method" was not set to "ash"; lfsr in plot should be ignored
do.call(plot_grid,plots)

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

DA_dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/filtered_peaks/DAanalysis-Buenrostro2018-k=10"

DA_res <- readRDS(file.path(DA_dir, "DA_regions_topics_vsnull_ash_10000iters.rds"))
summary(DA_res)

dim(DA_res$z)
#          Length  Class  Mode   
# ar       1267190 -none- numeric
# est      1267190 -none- numeric
# postmean 1267190 -none- numeric
# lower    1267190 -none- numeric
# upper    1267190 -none- numeric
# z        1267190 -none- numeric
# lfsr     1267190 -none- numeric
# lpval          1 -none- numeric
# svalue   1267190 -none- numeric
# ash            3 ash    list   
# F        1267190 -none- numeric
# f0        126719 -none- numeric
# [1] 126719     10

Number of regions selected at different lfsr cutoffs:

sig_regions <- matrix(NA, nrow = 10, ncol = 3)
colnames(sig_regions) <- c("lfsr < 0.01", "lfsr < 0.05", "lfsr < 0.1")
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)))
}

sig_regions
#          lfsr < 0.01 lfsr < 0.05 lfsr < 0.1
# topic 1        15066       21034      25515
# topic 2        11164       18536      24600
# topic 3        15139       20590      25533
# topic 4        14210       19177      23482
# topic 5        15240       22228      26977
# topic 6         6892        9588      11650
# topic 7        13505       19528      24046
# topic 8         9983       17102      22741
# topic 9        13028       18154      22778
# topic 10        6605       10974      14385

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)


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_0.21            RColorBrewer_1.1-2 plyr_1.8.6        
#  [5] cowplot_1.1.1      ggplot2_3.3.5      dplyr_1.0.8        Matrix_1.4-1      
#  [9] fastTopics_0.6-97  workflowr_1.7.0   
# 
# loaded via a namespace (and not attached):
#  [1] mcmc_0.9-7         fs_1.5.2           progress_1.2.2     httr_1.4.2        
#  [5] rprojroot_2.0.2    tools_4.0.4        bslib_0.3.1        utf8_1.2.2        
#  [9] R6_2.5.1           irlba_2.3.5        uwot_0.1.11        DBI_1.1.2         
# [13] lazyeval_0.2.2     colorspace_2.0-3   withr_2.5.0        tidyselect_1.1.2  
# [17] prettyunits_1.1.1  processx_3.5.2     compiler_4.0.4     git2r_0.30.1      
# [21] cli_3.2.0          quantreg_5.88      SparseM_1.81       plotly_4.10.0     
# [25] labeling_0.4.2     sass_0.4.1         scales_1.1.1       SQUAREM_2021.1    
# [29] quadprog_1.5-8     callr_3.7.0        pbapply_1.5-0      mixsqp_0.3-43     
# [33] stringr_1.4.0      digest_0.6.29      rmarkdown_2.13     MCMCpack_1.6-1    
# [37] pkgconfig_2.0.3    htmltools_0.5.2    highr_0.9          fastmap_1.1.0     
# [41] invgamma_1.1       htmlwidgets_1.5.4  rlang_1.0.2        rstudioapi_0.13   
# [45] farver_2.1.0       jquerylib_0.1.4    generics_0.1.2     jsonlite_1.8.0    
# [49] magrittr_2.0.2     Rcpp_1.0.8.3       munsell_0.5.0      fansi_1.0.3       
# [53] lifecycle_1.0.1    stringi_1.7.6      whisker_0.4        yaml_2.3.5        
# [57] MASS_7.3-56        Rtsne_0.15         grid_4.0.4         parallel_4.0.4    
# [61] promises_1.2.0.1   ggrepel_0.9.1      crayon_1.5.0       lattice_0.20-45   
# [65] hms_1.1.1          knitr_1.37         ps_1.6.0           pillar_1.7.0      
# [69] glue_1.6.2         evaluate_0.14      getPass_0.2-2      data.table_1.14.2 
# [73] RcppParallel_5.1.5 vctrs_0.3.8        httpuv_1.6.5       MatrixModels_0.5-0
# [77] gtable_0.3.0       purrr_0.3.4        tidyr_1.2.0        assertthat_0.2.1  
# [81] ashr_2.2-54        xfun_0.30          coda_0.19-4        later_1.3.0       
# [85] viridisLite_0.4.0  truncnorm_1.0-8    tibble_3.1.6       ellipsis_0.3.2