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Combining immune cell repertoire sequencing and functional expression with applications to autoimmune disease. McDavid, Andrew
Description
The cells of the adaptive immune system have the capacity to respond to a lifetime of diverse pathogens, in part due to somatic recombination of genes comprising the T and B cell receptors ([TB]CR). Several protocols now exist for simultaneously resolving [TB]CR identity and scRNAseq expression of all other polyadenalated mRNAs. I describe a pipeline for inferring *clonal* subpopulations of cells, that is, cells that share a [TB]CR from a common ancestor. This pipeline can scale to tens of thousands of cells. A series of statistical models are proposed for testing for expansion of specific clones in covariate groups, as well the omnibus propensity for clonal expansion. Lastly, I consider how [TB]CR identity can be combined with scRNAseq expression for unsupervised clustering using methods from multiview learning. I apply these methods to a data set of B cells isolated from the synovium of rheumatoid arthritis patients.
Item Metadata
Title |
Combining immune cell repertoire sequencing and functional expression with applications to autoimmune disease.
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2018-11-06T11:03
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Description |
The cells of the adaptive immune system have the capacity to respond to a lifetime of diverse pathogens, in part due to somatic recombination of genes comprising the T and B cell receptors ([TB]CR). Several protocols now exist for simultaneously resolving [TB]CR identity and scRNAseq expression of all other polyadenalated mRNAs. I describe a pipeline for inferring *clonal* subpopulations of cells, that is, cells that share a [TB]CR from a common ancestor. This pipeline can scale to tens of thousands of cells. A series of statistical models are proposed for testing for expansion of specific clones in covariate groups, as well the omnibus propensity for clonal expansion. Lastly, I consider how [TB]CR identity can be combined with scRNAseq expression for unsupervised clustering using methods from multiview learning. I apply these methods to a data set of B cells isolated from the synovium of rheumatoid arthritis patients.
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Extent |
25.0
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: University of Rochester
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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 |
10.14288/1.0378592
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Researcher
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International