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

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. Resu...

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Bibliographic Details
Published in:Bioinformatics 2018-09, Vol.34 (18), p.3217-3219
Main Authors: Müller, Sören, Cho, Ara, Liu, Siyuan J, Lim, Daniel A, Diaz, Aaron
Format: Article
Language:English
Subjects:
Algorithms
Applications Notes
Gene Expression Profiling - methods
Humans
Neoplasms - genetics
RNA, Small Cytoplasmic
Sequence Analysis, DNA - methods
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Software
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Summary: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.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bty316