Identify, quantify and characterize cellular communication from single-cell RNA sequencing data with scSeqComm.
Bioinformatics
; 38(7): 1920-1929, 2022 03 28.
Article
em En
| MEDLINE
| ID: mdl-35043939
ABSTRACT
MOTIVATION Recently, single-cell RNA-seq (scRNA-seq) data have been used to study cellular communication. Most bioinformatics methods infer only the intercellular signaling between groups of cells, mainly exploiting ligand-receptor expression levels. Only few methods consider the entire intercellular + intracellular signaling, mainly inferring lists/networks of signaling involved genes. RESULTS:
Here, we present scSeqComm, a computational method to identify and quantify the evidence of ongoing intercellular and intracellular signaling from scRNA-seq data, and at the same time providing a functional characterization of the inferred cellular communication. The possibility to quantify the evidence of ongoing communication assists the prioritization of the results, while the combined evidence of both intercellular and intracellular signaling increase the reliability of inferred communication. The application to a scRNA-seq dataset of tumor microenvironment, the agreement with independent bioinformatics analysis, the validation using spatial transcriptomics data and the comparison with state-of-the-art intercellular scoring schemes confirmed the robustness and reliability of the proposed method. AVAILABILITY AND IMPLEMENTATION scSeqComm R package is freely available at https//gitlab.com/sysbiobig/scseqcomm and https//sysbiobig.dei.unipd.it/software/#scSeqComm. Submitted software version and test data are available in Zenodo, at https//dx.doi.org/10.5281/zenodo.5833298. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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1
Base de dados:
MEDLINE
Assunto principal:
Software
/
Análise de Célula Única
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article