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Knit directory: scATACseq-topics/
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| Rmd | bd24884 | kevinlkx | 2022-03-11 | wflow_rename("analysis/process_data_Buenrostro2018.Rmd", "analysis/process_data_Buenrostro2018_different_options.Rmd") |
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| Rmd | 97c421d | kevinlkx | 2021-01-06 | put all four data processing options together |
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| Rmd | 4b7d5fa | kevinlkx | 2020-11-19 | process data with peaks in less than 1% samples filtered out |
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| Rmd | ee608fd | kevinlkx | 2020-11-18 | process counts for aggregated single-cell peaks using chromVAR |
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| Rmd | 614814b | kevinlkx | 2020-11-10 | process Buenrostro2018 data using Downloaded scATAC-seq processed data from GEO |
| Rmd | c113844 | kevinlkx | 2020-11-10 | wflow_rename("analysis/process_data_Buenrostro2018.Rmd", "analysis/process_data_Buenrostro2018_Chen2019pipeline.Rmd") |
| html | c113844 | kevinlkx | 2020-11-10 | wflow_rename("analysis/process_data_Buenrostro2018.Rmd", "analysis/process_data_Buenrostro2018_Chen2019pipeline.Rmd") |
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| Rmd | 1da32f8 | kevinlkx | 2020-11-05 | set binarized counts as sparse matrix |
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| Rmd | 857e1e8 | kevinlkx | 2020-11-04 | process data from Buenrostro 2018 paper |
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| Rmd | 12bf4b3 | kevinlkx | 2020-11-04 | process data from Buenrostro 2018 paper |
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
More details about the samples: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2540299
RCC directory: /project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/
We used the processed counts and peaks from Buenrostro et al (2018) paper.
They called ATAC-seq peaks from the bulk ATAC-seq samples.
Using the previously described approach (Corces et al., 2016), we defined a peak list using all bulk hematopoietic data analyzed here, resulting in 491,437 500bp non-overlapping peaks which we use for the remainder of this study.
Downloaded scATAC-seq processed data from GEO: GSE96769
GSE96769_PeakFile_20160207.bed.gzGSE96769_scATACseq_counts.txt.gzlibrary(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$x)
counts <- t(counts)
rownames(counts) <- sample_names
dim(counts)# [1] 2953 491437Peaks (from bulk ATAC-seq)
peaks <- fread("/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/GEO_data/GSE96769_PeakFile_20160207.bed.gz")
peak_names <- paste(peaks$V1,peaks$V2,peaks$V3,sep = "_")
rownames(peaks) <- peak_names
cat(sprintf("Number of peaks: %d\n",nrow(peaks)))
colnames(counts) <- peak_names# Number of peaks: 491437The supplementary Table S1 provides more details about these samples. https://ars.els-cdn.com/content/image/1-s2.0-S009286741830446X-mmc1.xlsx
Select the 2,034 samples (cells) passing quality filtering
Single-cell profiles were of consistent high-quality with 2,034 cells passing stringent quality filtering, yielding a median of 8,268 fragments per cell with 76% of those fragments mapping to peaks, resulting in a median of 6,442 fragments in peaks per cell (figure 1E).
Single-cells were filtered for quality requiring at least 60% of fragments in peaks and requiring greater than 1,000 fragments passing quality filters, quality filters are previously described (Corces et al., 2016) which includes removal of mitochondrial reads and low alignment quality (Q30).
The 2,034 samples (pass filter) were included in the metadata.tsv file on the scATAC-benchmarking website from Chen et al.
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 491437Remove peaks not exist in any of the cells
j <- which(colSums(counts > 0) >= 1)
peaks <- peaks[j,]
counts <- counts[,j]
cat(length(j), "peaks left after filtering. \n")
dim(counts)
sum(colSums(counts) == 0)
sum(rowSums(counts) == 0)# 465536 peaks left after filtering.
# [1] 2034 465536
# [1] 0
# [1] 0Binarize counts
binarized_counts <- as.matrix((counts > 0) + 0)
binarized_counts <- Matrix(binarized_counts, sparse = TRUE)
cat(sprintf("Matrix dimension after filtering: %d x %d.\n",nrow(binarized_counts),ncol(binarized_counts)))# Matrix dimension after filtering: 2034 x 465536.Save processed data: /project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data/
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data/"
dir.create(data.dir, showWarnings = FALSE, recursive = TRUE)
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_binarized.RData"))
cat(sprintf("Loaded %d x %d counts matrix.\n",nrow(counts),ncol(counts)))
cat(sprintf("Number of samples (cells): %d\n",nrow(counts)))
cat(sprintf("Number of peaks: %d\n",ncol(counts)))
cat(sprintf("Proportion of counts that are non-zero: %0.1f%%.\n",
100*mean(counts > 0)))# Loaded 2034 x 465536 counts matrix.
# Number of samples (cells): 2034
# Number of peaks: 465536
# Proportion of counts that are non-zero: 1.5%.
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] tools stats graphics grDevices utils datasets methods
# [8] base
#
# other attached packages:
# [1] data.table_1.14.2 readr_2.1.1 Matrix_1.4-0 workflowr_1.7.0
#
# loaded via a namespace (and not attached):
# [1] Rcpp_1.0.8 highr_0.9 bslib_0.3.1 compiler_4.0.4
# [5] pillar_1.7.0 later_1.3.0 git2r_0.29.0 jquerylib_0.1.4
# [9] R.methodsS3_1.8.1 R.utils_2.11.0 getPass_0.2-2 digest_0.6.29
# [13] lattice_0.20-45 jsonlite_1.7.3 evaluate_0.14 tibble_3.1.6
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# [37] fansi_1.0.2 rmarkdown_2.11 tzdb_0.2.0 callr_3.7.0
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