Work

CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones

Year: 2018

Type: article

Abstract: Motivation: Single-cell RNA-sequencing (scRNA-seq) has enabled studies of tissue composition at unprecedented resolution. However, the application of scRNA-seq to clinical cancer samples has been limited, partly due to a lack of scRNA-seq algorithms that integrate genomic mutation data. Results: To address this, we present. CONICS: COpy-Number analysis In single-Cell RNA-Sequencing. CONICS is a software tool for mapping gene expression from scRNA-seq to tumor clones and phylogenies, with routines enabling: the quantitation of copy-number alterations in scRNA-seq, robust separation of neoplastic cells from tumor-infiltrating stroma, inter-clone differential-expression analysis and intra-clone co-expression analysis. Availability and implementation: CONICS is written in Python and R, and is available from https://github.com/diazlab/CONICS. Supplementary information: Supplementary data are available at Bioinformatics online.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/bty316

Authors: Sören Müller, Ara Cho, Siyuan J Liu, Daniel A Lim, Aaron Diaz

Institution: University of California, San Francisco

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Cancer Institute, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke