CanSig: a tool for benchmarking malignant state discovery in single-cell RNA-Seq data Article Swipe

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Josephine Yates , Florian Barkmann , Pawel Piotr Czyz , Agnieszka Kraft , Marc Glettig , Frederieke Lohmann , Elia Saquand , Richard von der Horst , Nicolas Volken , Niko Beerenwinkel , Valentina Boeva ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1101/2022.04.14.488324

Single-cell RNA sequencing (scRNA-seq) facilitates the discovery of gene signatures that define cell states across patients, which could be used in patient stratification and drug discovery. However, the lack of standardization in computational methodologies to analyse these data impedes the reproducibility of signature detection. To address this, we developed CanSig, a comprehensive benchmarking tool that evaluates methods for identifying transcriptional signatures in cancer. CanSig integrates metrics for batch correction and biological signal conservation with a gene signature correlation metric to score according to rediscovery, cross-dataset reproducibility, and clinical relevance. We applied CanSig to ten methods and to ten scRNA-seq datasets from four human cancer types—glioblastoma, breast cancer, lung adenocarcinoma, and cutaneous squamous cell carcinoma— representing 116 patients and 105,000 malignant cells. Our results identify BBKNN as a leading method. We showed that the signatures identified with these methods correlate with clinically relevant outcomes, including patient survival and lymph node metastasis. Thus, CanSig establishes a standardized framework for reproducible cancer transcriptomics analysis.

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Cancer

Computational Biology

Biology

Bioinformatics

Gene Expression

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Cancer Computational biology Biology Transcriptional regulation Gene Genetics Bioinformatics Gene expression

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Type: preprint
Language: en
Landing Page: https://doi.org/10.1101/2022.04.14.488324
PDF: https://www.biorxiv.org/content/biorxiv/early/2022/04/14/2022.04.14.488324.full.pdf
OA Status: green
Cited By: 3
References: 58
Related Works: 10
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DOI: https://doi.org/10.1101/2022.04.14.488324

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Title: CanSig: a tool for benchmarking malignant state discovery in single-cell RNA-Seq data

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-04-14

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Authors: Josephine Yates, Florian Barkmann, Pawel Piotr Czyz, Agnieszka Kraft, Marc Glettig, Frederieke Lohmann, Elia Saquand, Richard von der Horst, Nicolas Volken, Niko Beerenwinkel, Valentina Boeva

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Landing page: https://doi.org/10.1101/2022.04.14.488324

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Concepts: Cancer, Computational biology, Biology, Transcriptional regulation, Gene, Genetics, Bioinformatics, Gene expression

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Cited by: 3

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