ChromSCape_scChIP_scATAC_compiled_datasets

This folder contains three analysis of one scChIP-seq and one scATAC-seq datasets for the purpose of being visualised by ChromSCape (https://github.com/vallotlab/ChromSCape).

The samples correspond to mouse cells from patient-derived xenograft (PDX) originating from two different human donors (Grosselin et al., 2019). Raw FASTQ reads were processed using the latest version of our scChIP-seq data engineering pipeline that allowed a more precise removal of PCR and RT duplicates (code available at https://github.com/vallotlab/scChIPseq_DataEngineering) to produce 50kbp binned count matrices given as input to ChromSCape (matrices available at https://figshare.com/projects/Single-Cell_ChIP-seq_of_Mouse_Stromal_Cells_in_PDX_tumour_models_of_resistance/66419).

scATAC-seq datasets:

The single-cell ATAC-seq dataset is composed of two cell types derived from two acute myeloid leukaemia (AML) patient (blastocytes (blast) and leukemic stem cells (LSC) produced in (Corces et al., 2016)) as well as multiple cell lines : GM12878, TF1, BJ, H1, HL60, K562 (3 replicates) produced in (Buenrostro et al., 2015), K562 (3 replicates) produced in (Schep et al., 2017); monocytes (Mono) and lymphoid primed multipotent progenitor (LMPP) produced in (Corces et al., 2016)). The count matrix of reads in peaks was downloaded from GEO accession number GSE99172, split into distinct matrices for each sample and formatted to be accepted as input by ChromSCape.

Source code, guidelines for installation and use of the application are provided at https://github.com/vallotlab/ChromSCape. A Docker environment containing all libraries required to launch the application is also available on DockerHub at https://hub.docker.com/repository/docker/pacomito/chromscape, with all set-up steps clearly described on the GitHub repository.