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Design and analysis of a single cell RNA-seq benchmarking dataset to compare protocols and methods. Ritchie, Matt
Description
Authors: Matthew Ritchie [1], Luyi Tian [1], Xueyi Dong [1], Saskia Freytag [1], Shian Su [1], Daniela Amann-Zalcenstein [1], Tom Weber [1], Azadeh Seidi [2], Kim-Anh Lê Cao [3], Shalin Naik [1] Affiliations: 1. Walter and Eliza Hall Institute of Medical Research, Parkville, Australia. 2. Australian Genome Research Facility, Parkville, Australia. 3. Melbourne Integrative Genomics, The University of Melbourne, Parkville, Australia. Abstract: Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and has brought with it new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions, from cell type identification, to marker gene discovery and trajectory analysis. The current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many different methods available in a systematic manner. To address this problem, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms as well as varying levels of biological noise. We use this dataset to compare different protocols, examine popular assumptions made in scRNA-seq analyses and compare methods for tasks ranging from normalization to trajectory analysis. We found significant differences in the results from the methods compared and have identified a few that performed well across protocols in high and low variability scenarios. Our dataset and analysis provide a valuable resource for understanding the nature of scRNA-seq data and can be used to guide algorithm selection in different biological settings.
Item Metadata
| Title |
Design and analysis of a single cell RNA-seq benchmarking dataset to compare protocols and methods.
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| Creator | |
| Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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| Date Issued |
2018-11-06T17:41
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| Description |
Authors: Matthew Ritchie [1], Luyi Tian [1], Xueyi Dong [1], Saskia Freytag [1], Shian Su [1], Daniela Amann-Zalcenstein [1], Tom Weber [1], Azadeh Seidi [2], Kim-Anh Lê Cao [3], Shalin Naik [1]
Affiliations: 1. Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
2. Australian Genome Research Facility, Parkville, Australia.
3. Melbourne Integrative Genomics, The University of Melbourne, Parkville, Australia.
Abstract: Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and has brought with it new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions, from cell type identification, to marker gene discovery and trajectory analysis. The current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many different methods available in a systematic manner.
To address this problem, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms as well as varying levels of biological noise. We use this dataset to compare different protocols, examine popular assumptions made in scRNA-seq analyses and compare methods for tasks ranging from normalization to trajectory analysis. We found significant differences in the results from the methods compared and have identified a few that performed well across protocols in high and low variability scenarios. Our dataset and analysis provide a valuable resource for understanding the nature of scRNA-seq data and can be used to guide algorithm selection in different biological settings.
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| Extent |
25.0
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| Type | |
| File Format |
video/mp4
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| Language |
eng
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| Notes |
Author affiliation: The Walter and Eliza Hall Institute of Medical Research
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| Series | |
| Date Available |
2019-05-06
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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| DOI |
10.14288/1.0378596
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| URI | |
| Affiliation | |
| Peer Review Status |
Unreviewed
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| Scholarly Level |
Other
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International