bioRxivBioinformaticsPreprint
iSHARC: Integrating scMultiome data for heterogeneity and regulatory analysis in cancer
Yong ZengiD, Shalini Bahl, Xu XiiD, Xinpei Ci, Tina KeshavarzianiD, Lin Yang, Ho Seok LeeiD, Pathum KossinnaiD, Federico GaitiiD, Gregory W. SchwartziD, Housheng Hansen HeiD, Mathieu LupieniD
Abstract
SummaryThe 10x Genomics single cell Multiome (scMultiome) assay enables the simultaneous profiling of chromatin accessibility and gene expression from the same nucleus, and has increasingly been utilized in revealing cellular heterogeneity and gene regulation in cancers. However, a dedicated bioinformatics pipeline specifically designed for this type of data is still lacking. Here we present iSHARC, a streamlined pipeline for quality control, modality integration, clustering, cell type annotation, and regulatory mechanism analysis of individual scMultiome data, as well as for integrating multiple samples. The main advantages of iSHARC are: 1) easy implementation, execution and extension through a modular Snakemake workflow management system; 2) flexible analysis and parameters customization via a single configuration file; and 3) comprehensive accessibility by providing different access points to results and detailed summary reports from a single run. Availability and implementationThis pipeline is an open-source software under the MIT license and it is freely available at https://github.com/yzeng-lol/iSHARC. Contactyong.zeng@uhn.ca or hansen.he@uhn.ca or mathieu.lupien@uhn.ca Supplementary informationSupplementary data are appended.
- assay: scRNA-seq
- assay: scATAC-seq
- research focus: clustering
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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