Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer
Summary: 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | STAR Protocols |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166722007572 |
| _version_ | 1811292151044112384 |
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| author | Nighat Noureen Xiaojing Wang Siyuan Zheng |
| author_facet | Nighat Noureen Xiaojing Wang Siyuan Zheng |
| author_sort | Nighat Noureen |
| collection | DOAJ |
| description | Summary: 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 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. |
| first_indexed | 2024-04-13T04:41:05Z |
| format | Article |
| id | doaj.art-ff65730bddc64b15bcc6ff5de9edfd20 |
| institution | Directory Open Access Journal |
| issn | 2666-1667 |
| language | English |
| last_indexed | 2024-04-13T04:41:05Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | STAR Protocols |
| spelling | doaj.art-ff65730bddc64b15bcc6ff5de9edfd202022-12-22T03:02:00ZengElsevierSTAR Protocols2666-16672022-12-0134101877Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancerNighat Noureen0Xiaojing Wang1Siyuan Zheng2Greehey Children’s Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229, USA; Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA; Corresponding authorGreehey Children’s Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229, USA; Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USAGreehey Children’s Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229, USA; Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA; Corresponding authorSummary: 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 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.http://www.sciencedirect.com/science/article/pii/S2666166722007572BioinformaticsCancerRNAseq |
| spellingShingle | Nighat Noureen Xiaojing Wang Siyuan Zheng Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer STAR Protocols Bioinformatics Cancer RNAseq |
| 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 | Bioinformatics Cancer RNAseq |
| url | http://www.sciencedirect.com/science/article/pii/S2666166722007572 |
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