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About Buenrostro 2018 dataset

Reference: Buenrostro, J. D. et al. Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation. Cell 173, 1535–1548.e16 (2018).

Data were downloaded from GEO: GSE96772

RCC directory: /project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/

Prepare data for topic modeling

Using peaks called from bulk ATAC-seq samples

The Buenrostro et al paper called ATAC-seq peaks from the bulk ATAC-seq samples. Downloaded scATAC-seq processed data from GEO: GSE96769

  • Peaks called from bulk ATAC-seq samples GSE96769_PeakFile_20160207.bed.gz
  • scATAC-seq sparse count matrix GSE96769_scATACseq_counts.txt.gz
library(Matrix)
library(tools)
library(readr)
library(data.table)

Load the fragment counts as a 2,953 x 491,437 sparse matrix.

# The first row has the sample names
file_counts <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/GEO_data/GSE96769_scATACseq_counts.txt.gz"
sample_names <- readLines(file_counts,n = 1)
sample_names <- unlist(strsplit(sample_names,"\t",fixed = TRUE))
sample_names <- unlist(strsplit(sample_names,";",fixed = TRUE))
sample_names <- sample_names[-1]

# Load the fragment counts as sparse matrix.
dat <- fread(file_counts,sep = "\t",skip = 1)
class(dat) <- "data.frame"
names(dat) <- c("i","j","x")
counts <- sparseMatrix(i = dat$i,j = dat$j,x = dat$x)
counts <- t(counts)
rownames(counts) <- sample_names
dim(counts)
[1]   2953 491437

peaks

peaks <- fread("/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/GEO_data/GSE96769_PeakFile_20160207.bed.gz")
peak_names <- paste(peaks$V1,peaks$V2,peaks$V3,sep = "_")
rownames(peaks) <- peak_names
cat(sprintf("Number of peaks: %d\n",nrow(peaks)))
Number of peaks: 491437
colnames(counts) <- peak_names

Plot the distribution of fragment counts.

y <- table(summary(counts)$x)
x <- names(y)
y <- as.numeric(y)
plot(x,y,pch = 20,log = "y",cex = 0.65,
     xlab = "number of fragments mapping to peak",
     ylab = "number of peaks")
lines(x,y)

Version Author Date
594fd86 kevinlkx 2020-11-10

The supplementary Table S1 provides more details about these samples. https://ars.els-cdn.com/content/image/1-s2.0-S009286741830446X-mmc1.xlsx

Use the metadata.tsv from Chen et al. Genome Biology 2019 paper https://github.com/pinellolab/scATAC-benchmarking/tree/master/Real_Data/Buenrostro_2018/input/metadata.tsv)

samples_filtered <- read.table('/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/data/input_Chen_2019/metadata.tsv', header = TRUE, stringsAsFactors=FALSE, quote="",row.names=1)
samples_filtered$name <- rownames(samples_filtered)

idx_samples_filtered <- match(samples_filtered$name, sample_names)
sample_names_filtered <- sample_names[idx_samples_filtered]
counts <- counts[idx_samples_filtered,]
samples <- samples_filtered[,c("name", "label")]
dim(counts)
[1]   2034 491437

Remove peaks not exist in any of the cells

j <- which(colSums(counts > 0) >= 1)
peaks <- peaks[j,]
counts <- counts[,j]
cat(length(j), "peaks after filtering.  \n")
465536 peaks after filtering.

Plot the distribution of filtered fragment counts.

y <- table(summary(counts)$x)
x <- names(y)
y <- as.numeric(y)
plot(x,y,pch = 20,log = "y",cex = 0.65,
     xlab = "number of fragments mapping to peak",
     ylab = "number of peaks")
lines(x,y)

Version Author Date
594fd86 kevinlkx 2020-11-10

Binarize counts

binarized_counts <- as.matrix((counts > 0) + 0)
binarized_counts <- Matrix(binarized_counts, sparse = TRUE) 

dim(binarized_counts)
[1]   2034 465536
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data/"
dir.create(data.dir, showWarnings = FALSE, recursive = TRUE)

saveRDS(counts, file.path(data.dir, "counts_Buenrostro_2018.rds"))
saveRDS(binarized_counts, file.path(data.dir, "binarized_counts_Buenrostro_2018.rds"))

save(list = c("samples", "peaks", "counts"), 
     file = file.path(data.dir, "Buenrostro_2018.RData"))

counts <- binarized_counts
save(list = c("samples", "peaks", "counts"), 
     file = file.path(data.dir, "Buenrostro_2018_binarized.RData"))
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data/"
load(file.path(data.dir, "Buenrostro_2018.RData"))
cat(sprintf("Loaded %d x %d counts matrix.\n",nrow(counts),ncol(counts)))
Loaded 2034 x 465536 counts matrix.
cat(sprintf("Number of samples (cells): %d\n",nrow(counts)))
Number of samples (cells): 2034
cat(sprintf("Number of peaks: %d\n",ncol(counts)))
Number of peaks: 465536
cat(sprintf("Proportion of counts that are non-zero: %0.1f%%.\n",
            100*mean(counts > 0)))
Proportion of counts that are non-zero: 1.5%.

Using peaks called from aggregated single-cell ATAC-seq data processed by Chen et al. Genome Biology 2019

Load chromVAR and related packages

# if (!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install("chromVAR")
# BiocManager::install("motifmatchr")
# BiocManager::install("BSgenome.Hsapiens.UCSC.hg19")
# BiocManager::install("JASPAR2016")
library(chromVAR)
library(motifmatchr)
library(Matrix)
library(SummarizedExperiment)
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB
The following object is masked from 'package:Matrix':

    which
The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min
Loading required package: S4Vectors

Attaching package: 'S4Vectors'
The following objects are masked from 'package:data.table':

    first, second
The following object is masked from 'package:Matrix':

    expand
The following object is masked from 'package:base':

    expand.grid
Loading required package: IRanges

Attaching package: 'IRanges'
The following object is masked from 'package:data.table':

    shift
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: DelayedArray
Loading required package: matrixStats

Attaching package: 'matrixStats'
The following objects are masked from 'package:Biobase':

    anyMissing, rowMedians
Loading required package: BiocParallel

Attaching package: 'DelayedArray'
The following objects are masked from 'package:matrixStats':

    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
The following objects are masked from 'package:base':

    aperm, apply, rowsum
library(BiocParallel)
library('JASPAR2016')
library(BSgenome.Hsapiens.UCSC.hg19)
Loading required package: BSgenome
Loading required package: Biostrings
Loading required package: XVector

Attaching package: 'Biostrings'
The following object is masked from 'package:DelayedArray':

    type
The following object is masked from 'package:base':

    strsplit
Loading required package: rtracklayer
register(MulticoreParam(10))

data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data_Chen2019pipeline/chromVAR/"
dir.create(data.dir, showWarnings = FALSE, recursive = TRUE)
samples <- read.table('/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/data/input_Chen_2019/metadata.tsv', header = TRUE, stringsAsFactors=FALSE, quote="",row.names=1)
peakfile <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/data/input_Chen_2019/combined.sorted.merged.bed"
peaks <- getPeaks(peakfile, sort_peaks = TRUE)
Warning in getPeaks(peakfile, sort_peaks = TRUE): Peaks are not equal width!Use
resize(peaks, width = x, fix = "center") to make peaks equal in size, where x is
the desired size of the peaks)
Peaks sorted
peaks <- resize(peaks, width = 500, fix = "center")

cat(length(peaks), "peaks \n")
237450 peaks 
dir_bamfiles <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/data/input_Chen_2019/sc-bams_nodup/"
bamfiles <- list.files(path = dir_bamfiles, pattern = "\\.bam$")
cellnames <- sapply(strsplit(bamfiles,'.',fixed = TRUE), "[[", 1)
sum(cellnames == rownames(samples))
[1] 2034

count scATAC-seq paired-end fragments in the peaks

fragment_counts <- getCounts(file.path(dir_bamfiles,bamfile), 
                             peaks, 
                             paired = TRUE, 
                             by_rg = TRUE, 
                             format = "bam", 
                             colData = data.frame(celltype = cellnames))

saveRDS(fragment_counts, file.path(data.dir, "fragment_counts_scPeaks_chromVAR_Buenrostro_2018.rds"))
fragment_counts <- readRDS(file.path(data.dir, "fragment_counts_scPeaks_chromVAR_Buenrostro_2018.rds"))
fragment_counts
class: RangedSummarizedExperiment 
dim: 237450 2034 
metadata(0):
assays(1): counts
rownames: NULL
rowData names(0):
colnames(2034): BM1077-CLP-Frozen-160106-13 BM1077-CLP-Frozen-160106-14
  ... singles-PB1022-mono-160128-95 singles-PB1022-mono-160128-96
colData names(2): celltype depth
counts <- assay(fragment_counts)
counts <- t(counts)

peaks <- as.data.frame(peaks)[,1:3]
colnames(peaks) <- c("chr", "start", "end")
peak_names <- paste(peaks$chr, peaks$start, peaks$end, sep = "_")
colnames(counts) <- peak_names

Filter peaks using filterPeaks in chromVAR

idx_peaks_filtered <- filterPeaks(fragment_counts, min_fragments_per_peak = 1, non_overlapping = TRUE, ix_return = TRUE)
peaks <- peaks[idx_peaks_filtered,]
counts <- counts[,idx_peaks_filtered]
cat(length(idx_peaks_filtered), "peaks after filtering.  \n")
228965 peaks after filtering.

Plot the distribution of filtered fragment counts.

y <- table(summary(counts)$x)
x <- names(y)
y <- as.numeric(y)
plot(x,y,pch = 20,log = "y",cex = 0.65,
     xlab = "number of fragments mapping to peak",
     ylab = "number of peaks")
lines(x,y)

Version Author Date
d5b411d kevinlkx 2020-11-18

Binarize counts

binarized_counts <- as.matrix((counts > 0) + 0)
binarized_counts <- Matrix(binarized_counts, sparse = TRUE) 
dim(binarized_counts)
[1]   2034 228965
saveRDS(counts, file.path(data.dir, "counts_scPeaks_Buenrostro_2018.rds"))
saveRDS(binarized_counts, file.path(data.dir, "binarized_counts_scPeaks_Buenrostro_2018.rds"))

save(list = c("samples", "peaks", "counts"), 
     file = file.path(data.dir, "Buenrostro_2018_scPeaks.RData"))

counts <- binarized_counts
save(list = c("samples", "peaks", "counts"), 
     file = file.path(data.dir, "Buenrostro_2018_binarized_scPeaks.RData"))
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data_Chen2019pipeline/chromVAR/"

load(file.path(data.dir, "Buenrostro_2018_binarized_scPeaks.RData"))
cat(sprintf("Loaded %d x %d counts matrix.\n",nrow(counts),ncol(counts)))
Loaded 2034 x 228965 counts matrix.
cat(sprintf("Number of samples (cells): %d\n",nrow(counts)))
Number of samples (cells): 2034
cat(sprintf("Number of peaks: %d\n",ncol(counts)))
Number of peaks: 228965
cat(sprintf("Proportion of counts that are non-zero: %0.1f%%.\n",
            100*mean(counts > 0)))
Proportion of counts that are non-zero: 2.6%.

Using peaks called from aggregated single-cell ATAC-seq data and filter peaks with less than 1% samples

Load chromVAR and related packages

# if (!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install("chromVAR")
# BiocManager::install("motifmatchr")
# BiocManager::install("BSgenome.Hsapiens.UCSC.hg19")
# BiocManager::install("JASPAR2016")
library(chromVAR)
library(motifmatchr)
library(Matrix)
library(SummarizedExperiment)
library(BiocParallel)
library('JASPAR2016')
library(BSgenome.Hsapiens.UCSC.hg19)

register(MulticoreParam(10))

data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data_Chen2019pipeline/chromVAR/"
dir.create(data.dir, showWarnings = FALSE, recursive = TRUE)
samples <- read.table('/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/data/input_Chen_2019/metadata.tsv', header = TRUE, stringsAsFactors=FALSE, quote="",row.names=1)
peakfile <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/data/input_Chen_2019/combined.sorted.merged.bed"
peaks <- getPeaks(peakfile, sort_peaks = TRUE)
Warning in getPeaks(peakfile, sort_peaks = TRUE): Peaks are not equal width!Use
resize(peaks, width = x, fix = "center") to make peaks equal in size, where x is
the desired size of the peaks)
Peaks sorted
peaks <- resize(peaks, width = 500, fix = "center")

cat(length(peaks), "peaks \n")
237450 peaks 
dir_bamfiles <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/data/input_Chen_2019/sc-bams_nodup/"
bamfiles <- list.files(path = dir_bamfiles, pattern = "\\.bam$")
cellnames <- sapply(strsplit(bamfiles,'.',fixed = TRUE), "[[", 1)
sum(cellnames == rownames(samples))
[1] 2034

count scATAC-seq paired-end fragments in the peaks

fragment_counts <- getCounts(file.path(dir_bamfiles,bamfile), 
                             peaks, 
                             paired = TRUE, 
                             by_rg = TRUE, 
                             format = "bam", 
                             colData = data.frame(celltype = cellnames))

saveRDS(fragment_counts, file.path(data.dir, "fragment_counts_scPeaks_chromVAR_Buenrostro_2018.rds"))
fragment_counts <- readRDS(file.path(data.dir, "fragment_counts_scPeaks_chromVAR_Buenrostro_2018.rds"))
fragment_counts
class: RangedSummarizedExperiment 
dim: 237450 2034 
metadata(0):
assays(1): counts
rownames: NULL
rowData names(0):
colnames(2034): BM1077-CLP-Frozen-160106-13 BM1077-CLP-Frozen-160106-14
  ... singles-PB1022-mono-160128-95 singles-PB1022-mono-160128-96
colData names(2): celltype depth
counts <- assay(fragment_counts)
counts <- t(counts)

peaks <- as.data.frame(peaks)[,1:3]
colnames(peaks) <- c("chr", "start", "end")
peak_names <- paste(peaks$chr, peaks$start, peaks$end, sep = "_")
colnames(counts) <- peak_names
fragment_binarized_counts <- fragment_counts
assay(fragment_binarized_counts) <- Matrix(as.matrix((assay(fragment_binarized_counts) > 0) + 0), sparse = TRUE)
idx_peaks_filtered <- filterPeaks(fragment_binarized_counts, min_fragments_per_peak = floor(nrow(samples)*0.01), non_overlapping = TRUE, ix_return = TRUE)
cat(length(idx_peaks_filtered), "peaks selected after filtering.  \n")
104502 peaks selected after filtering.  
peaks <- peaks[idx_peaks_filtered,]
counts <- counts[,idx_peaks_filtered]

Plot the distribution of filtered fragment counts.

y <- table(summary(counts)$x)
x <- names(y)
y <- as.numeric(y)
plot(x,y,pch = 20,log = "y",cex = 0.65,
     xlab = "number of fragments mapping to peak",
     ylab = "number of peaks")
lines(x,y)

Binarize counts

binarized_counts <- as.matrix((counts > 0) + 0)
binarized_counts <- Matrix(binarized_counts, sparse = TRUE) 
dim(binarized_counts)
[1]   2034 104502
save(list = c("samples", "peaks", "counts"), 
     file = file.path(data.dir, "Buenrostro_2018_scPeaks_filtered.RData"))

counts <- binarized_counts
save(list = c("samples", "peaks", "counts"), 
     file = file.path(data.dir, "Buenrostro_2018_binarized_scPeaks_filtered.RData"))
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data_Chen2019pipeline/chromVAR/"

load(file.path(data.dir, "Buenrostro_2018_binarized_scPeaks_filtered.RData"))
cat(sprintf("Loaded %d x %d counts matrix.\n",nrow(counts),ncol(counts)))
Loaded 2034 x 104502 counts matrix.
cat(sprintf("Number of samples (cells): %d\n",nrow(counts)))
Number of samples (cells): 2034
cat(sprintf("Number of peaks: %d\n",ncol(counts)))
Number of peaks: 104502
cat(sprintf("Proportion of counts that are non-zero: %0.1f%%.\n",
            100*mean(counts > 0)))
Proportion of counts that are non-zero: 5.3%.

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] parallel  stats4    tools     stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] BSgenome.Hsapiens.UCSC.hg19_1.4.0 BSgenome_1.52.0                  
 [3] rtracklayer_1.44.0                Biostrings_2.52.0                
 [5] XVector_0.24.0                    JASPAR2016_1.12.0                
 [7] SummarizedExperiment_1.14.1       DelayedArray_0.10.0              
 [9] BiocParallel_1.18.0               matrixStats_0.57.0               
[11] Biobase_2.42.0                    GenomicRanges_1.36.0             
[13] GenomeInfoDb_1.20.0               IRanges_2.18.1                   
[15] S4Vectors_0.22.1                  BiocGenerics_0.30.0              
[17] motifmatchr_1.4.0                 chromVAR_1.4.1                   
[19] data.table_1.12.8                 readr_1.3.1                      
[21] Matrix_1.2-18                     workflowr_1.6.2                  

loaded via a namespace (and not attached):
 [1] bitops_1.0-6                fs_1.3.1                   
 [3] DirichletMultinomial_1.26.0 TFBSTools_1.22.0           
 [5] bit64_0.9-7                 httr_1.4.1                 
 [7] rprojroot_1.3-2             backports_1.1.10           
 [9] R6_2.5.0                    DT_0.13                    
[11] lazyeval_0.2.2              seqLogo_1.50.0             
[13] DBI_1.1.0                   colorspace_1.4-1           
[15] tidyselect_1.1.0            bit_1.1-14                 
[17] compiler_3.6.1              git2r_0.27.1               
[19] plotly_4.9.0                caTools_1.17.1.2           
[21] scales_1.1.1                stringr_1.4.0              
[23] digest_0.6.27               Rsamtools_2.0.0            
[25] rmarkdown_2.1               R.utils_2.9.2              
[27] pkgconfig_2.0.3             htmltools_0.4.0            
[29] fastmap_1.0.1               htmlwidgets_1.5.1          
[31] rlang_0.4.8                 VGAM_1.1-1                 
[33] RSQLite_2.1.1               shiny_1.4.0.2              
[35] jsonlite_1.6                gtools_3.8.1               
[37] dplyr_0.8.5                 R.oo_1.23.0                
[39] RCurl_1.98-1.1              magrittr_1.5               
[41] GO.db_3.8.2                 GenomeInfoDbData_1.2.1     
[43] Rcpp_1.0.5                  munsell_0.5.0              
[45] lifecycle_0.2.0             R.methodsS3_1.7.1          
[47] stringi_1.4.6               whisker_0.4                
[49] yaml_2.2.0                  zlibbioc_1.30.0            
[51] plyr_1.8.6                  grid_3.6.1                 
[53] blob_1.2.0                  promises_1.1.0             
[55] crayon_1.3.4                miniUI_0.1.1.1             
[57] CNEr_1.20.0                 lattice_0.20-38            
[59] splines_3.6.1               annotate_1.62.0            
[61] KEGGREST_1.24.0             hms_0.5.3                  
[63] knitr_1.28                  pillar_1.4.6               
[65] reshape2_1.4.3              TFMPvalue_0.0.8            
[67] XML_3.98-1.20               glue_1.4.2                 
[69] evaluate_0.14               png_0.1-7                  
[71] vctrs_0.3.4                 httpuv_1.5.3.1             
[73] tidyr_1.1.0                 gtable_0.3.0               
[75] poweRlaw_0.70.2             purrr_0.3.4                
[77] assertthat_0.2.1            ggplot2_3.3.2              
[79] xfun_0.14                   mime_0.9                   
[81] xtable_1.8-4                later_1.0.0                
[83] viridisLite_0.3.0           tibble_3.0.4               
[85] AnnotationDbi_1.46.0        GenomicAlignments_1.20.1   
[87] memoise_1.1.0               ellipsis_0.3.1