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Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer

Scoring gene signatures is common for bulk and single-cell RNA sequencing (scRNAseq) data. Here, using cancer as a data model, we describe steps to benchmark signature scoring techniques for scRNAseq data in the context of uneven gene dropouts. These steps include identifying and comparing deregulat...

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Detalles Bibliográficos
Autores principales: Noureen, Nighat, Wang, Xiaojing, Zheng, Siyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9706629/
https://www.ncbi.nlm.nih.gov/pubmed/36595948
http://dx.doi.org/10.1016/j.xpro.2022.101877
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author Noureen, Nighat
Wang, Xiaojing
Zheng, Siyuan
author_facet Noureen, Nighat
Wang, Xiaojing
Zheng, Siyuan
author_sort Noureen, Nighat
collection PubMed
description Scoring gene signatures is common for bulk and single-cell RNA sequencing (scRNAseq) data. Here, using cancer as a data model, we describe steps to benchmark signature scoring techniques for scRNAseq data in the context of uneven gene dropouts. These steps include identifying and comparing deregulated signatures, generating gold standard signatures for specificity and sensitivity tests, and simulating the impact of dropouts using down sampling. The protocol provides a framework for benchmarking scRNAseq algorithms in such context. For complete details on the use and execution of this protocol, please refer to Noureen et al. (2022).(1)
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spelling pubmed-97066292022-11-30 Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer Noureen, Nighat Wang, Xiaojing Zheng, Siyuan STAR Protoc Protocol Scoring gene signatures is common for bulk and single-cell RNA sequencing (scRNAseq) data. Here, using cancer as a data model, we describe steps to benchmark signature scoring techniques for scRNAseq data in the context of uneven gene dropouts. These steps include identifying and comparing deregulated signatures, generating gold standard signatures for specificity and sensitivity tests, and simulating the impact of dropouts using down sampling. The protocol provides a framework for benchmarking scRNAseq algorithms in such context. For complete details on the use and execution of this protocol, please refer to Noureen et al. (2022).(1) Elsevier 2022-11-24 /pmc/articles/PMC9706629/ /pubmed/36595948 http://dx.doi.org/10.1016/j.xpro.2022.101877 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Noureen, Nighat
Wang, Xiaojing
Zheng, Siyuan
Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer
title Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer
title_full Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer
title_fullStr Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer
title_full_unstemmed Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer
title_short Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer
title_sort protocol to benchmark gene expression signature scoring techniques for single-cell rna sequencing data in cancer
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9706629/
https://www.ncbi.nlm.nih.gov/pubmed/36595948
http://dx.doi.org/10.1016/j.xpro.2022.101877
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