iSHARC: Integrating scMultiome data for heterogeneity and regulatory analysis in cancer
Zeng, Y.; Bahl, S.; Xu, X.; Ci, X.; Keshavarzian, T.; Yang, L.; Lee, H. S.; Kossinna, P.; Gaiti, F.; Schwartz, G. W.; He, H. H.; Lupien, M.
Show 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.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- FIRM: Flexible Integration of single-cell RNA-sequencing data for large-scale Multi-tissue cell atlas datasets 94%
- Sincast: a computational framework to predict cell identities in single cell transcriptomes using bulk atlases as references 94%
- STEAM: Spatial Transcriptomics Evaluation Algorithm and Metric for clustering performance 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.