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Single cell RNA-seq and ATAC seq analysis of cardiac progenitor cell transition states and lineage settlement

R session information

For requirements, please see the file RNA-seq_sessionInfo.txt and ATAC-seq_sessionInfo.txt for specific R packages and their versions.

Data download

Data for the original analysis is approximately 500 MB in size and can be downloaded from a bucket of an S3 object store using cloudyr/aws.s3:

From within R, install cloudyr/aws.s3 as follows and set up the connection to our data server:

remotes::install_github("cloudyr/aws.s3")
Sys.setenv("AWS_S3_ENDPOINT" = "mpi-bn.mpg.de",
           "AWS_DEFAULT_REGION" = "s3")

if(!dir.exists("original_analysis/data")) {
    dir.create("original_analysis/data", recursive = TRUE)
}

if(!dir.exists("original_analysis/supplementary_data")) {
    dir.create("original_analysis/supplementary_data", recursive = TRUE)
}

Then, use the following code snipped to download all files:

data_files <- c("Isl1_cardiac_branch_expression.txt", "Isl1_endo_branch_expression.txt", "dev_cluster_specific_2.Rda", "dev_clustered_cluster_specific_2.Rda", "scData_filtered_2.Rda", "variability_cluster_specific_2.Rda", "variability_clustered_cluster_specific_2.Rda")

for(file in data_files) {
    aws.s3::save_object(paste0("original-data/", file),
                        "data-cpc-2018",
                        paste0("original_analysis/data/", file))
}

supplementary_data_files <- c("Isl1-DM.Rdata", "Isl1-diffExprs.Rdata", "Isl1-markers.Rdata", "Nkx2-5-DM.Rdata", "Nkx2-5-diffExprs.Rdata", "Nkx2-5-markers.Rdata", "c1_subset.Rdata", "wu.Rdata")

for(file in supplementary_data_files) {
    aws.s3::save_object(paste0("original-data/", file),
                        "data-cpc-2018",
                        paste0("original_analysis/supplementary_data/", file))
}

or use aws.s3::save_object() with the following parameters to download individual files:

Object Target Call
Isl1_cardiac_branch_expression.txt data aws.s3::save_object("original-data/Isl1_cardiac_branch_expression.txt", "data-cpc-2018", "original_analysis/data/Isl1_cardiac_branch_expression.txt")
Isl1_endo_branch_expression.txt data aws.s3::save_object("original-data/Isl1_endo_branch_expression.txt", "data-cpc-2018", "original_analysis/data/Isl1_endo_branch_expression.txt")
dev_cluster_specific_2.Rda data aws.s3::save_object("original-data/dev_cluster_specific_2.Rda", "data-cpc-2018", "original_analysis/data/dev_cluster_specific_2.Rda")
dev_clustered_cluster_specific_2.Rda data aws.s3::save_object("original-data/dev_clustered_cluster_specific_2.Rda", "data-cpc-2018", "original_analysis/data/dev_clustered_cluster_specific_2.Rda")
scData_filtered_2.Rda data aws.s3::save_object("original-data/scData_filtered_2.Rda", "data-cpc-2018", "original_analysis/data/scData_filtered_2.Rda")
variability_cluster_specific_2.Rda data aws.s3::save_object("original-data/variability_cluster_specific_2.Rda", "data-cpc-2018", "original_analysis/data/variability_cluster_specific_2.Rda")
variability_clustered_cluster_specific_2.Rda data aws.s3::save_object("original-data/variability_clustered_cluster_specific_2.Rda", "data-cpc-2018", "original_analysis/data/variability_clustered_cluster_specific_2.Rda")
Isl1-DM.Rdata supplementary_data aws.s3::save_object("original-data/Isl1-DM.Rdata", "data-cpc-2018", "original_analysis/supplementary_data/Isl1-DM.Rdata")
Isl1-diffExprs.Rdata supplementary_data aws.s3::save_object("original-data/Isl1-diffExprs.Rdata", "data-cpc-2018", "original_analysis/supplementary_data/variability_clustered_cluster_specific_2.Rda")
Isl1-markers.Rdata supplementary_data aws.s3::save_object("original-data/Isl1-markers.Rdata", "data-cpc-2018", "original_analysis/supplementary_data/Isl1-markers.Rdata")
Isl1_cardiac_branch_expression.txt supplementary_data aws.s3::save_object("original-data/Isl1_cardiac_branch_expression.txt", "data-cpc-2018", "original_analysis/supplementary_data/Isl1_cardiac_branch_expression.txt")
Isl1_endo_branch_expression.txt supplementary_data aws.s3::save_object("original-data/Isl1_endo_branch_expression.txt", "data-cpc-2018", "original_analysis/supplementary_data/Isl1_endo_branch_expression.txt")
Nkx2-5-DM.Rdata supplementary_data aws.s3::save_object("original-data/Nkx2-5-DM.Rdata", "data-cpc-2018", "original_analysis/supplementary_data/Nkx2-5-DM.Rdata")
Nkx2-5-diffExprs.Rdata supplementary_data aws.s3::save_object("original-data/Nkx2-5-diffExprs.Rdata", "data-cpc-2018", "original_analysis/supplementary_data/Nkx2-5-diffExprs.Rdata")
Nkx2-5-markers.Rdata supplementary_data aws.s3::save_object("original-data/Nkx2-5-markers.Rdata", "data-cpc-2018", "original_analysis/supplementary_data/Nkx2-5-markers.Rdata")
c1_subset.Rdata supplementary_data aws.s3::save_object("original-data/c1_subset.Rdata", "data-cpc-2018", "original_analysis/supplementary_data/c1_subset.Rdata")
wu.Rdata supplementary_data aws.s3::save_object("original-data/wu.Rdata", "data-cpc-2018", "original_analysis/supplementary_data/wu.Rdata")

Finally, set the working directory to the path original-analysis and continue below:

setwd("original-analysis")

RNA-seq analysis

Preprocessing

R code to preprocess single-cell RNA-seq data from scratch can be found in the src directory. Steps (e.g. quality control, filtering, normalization, etc.) should be run in the order indicated by the leading counter in the filename (e.g. 00_filtering.R before 01_normalisation.R)

Figures

We additionally provide scripts to reproduce most of the main figures. The R code can be found in the figure_src directory and is split into individual files. Figures can be recreated without the need of preprocessing the data.

ATAC-seq analysis

Figures

We provide scripts to reproduce figures from the ATAC-seq data analysis of the manuscript. The R code can be found in the figure_src directory.

Citation

Please refer to the following research article when using data from this repository:

Guangshuai Jia, Jens Preussner, Xi Chen, Stefan Guenther, Xuejun Yuan, Michail Yekelchyk, Carsten Kuenne, Mario Looso, Yonggang Zhou, Sarah Teichmann and Thomas Braun. Single cell RNA-seq and ATAC seq analysis of cardiac progenitor cell transition states and lineage settlement. Nature Communications 9, 4877 (2018), doi: 10.1038/s41467-018-07307-6.