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Individual Level Differential Expression Analysis for Single Cell RNA-seq data

Mengqi Zhang, Si Liu, Zhen Miao, Fang Han, Raphael Gottardo, Wei Sun
doi: https://doi.org/10.1101/2021.05.10.443350
Mengqi Zhang
1Public Health Science Division, Fred Hutchison Cancer Research Center, Seattle, USA
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Si Liu
1Public Health Science Division, Fred Hutchison Cancer Research Center, Seattle, USA
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Zhen Miao
2Department of Statistics, University of Washington, Seattle, USA
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Fang Han
2Department of Statistics, University of Washington, Seattle, USA
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Raphael Gottardo
3Vaccine and Infectious Disease Division, Fred Hutchison Cancer Research Center, Seattle, USA
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Wei Sun
1Public Health Science Division, Fred Hutchison Cancer Research Center, Seattle, USA
4Department of Biostatistics, University of Washington, Seattle, USA
5Department of Biostatistics, University of North Carolina, Chapel Hill, USA
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  • For correspondence: wsun@fredhutch.org
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Abstract

Bulk RNA-seq data quantify the expression of a gene in an individual by one number (e.g., fragment count). In contrast, single cell RNA-seq (scRNA-seq) data provide much richer information: the distribution of gene expression across many cells. To assess differential expression across individuals using scRNA-seq data, a straightforward solution is to create “pseudo” bulk RNA-seq data by adding up the fragment counts of a gene across cells for each individual, and then apply methods designed for differential expression using bulk RNA-seq data. This pseudo-bulk solution reduces the distribution of gene expression across cells to a single number and thus loses a good amount of information. We propose to assess differential expression using the gene expression distribution measured by cell level data. We find denoising cell level data can substantially improve the power of this approach. We apply our method, named IDEAS (Individual level Differential Expression Analysis for scRNA-seq), to study the gene expression difference between autism subjects and controls. We find neurogranin-expressing neurons harbor a high proportion of differentially expressed genes, and ERBB signals in microglia are associated with autism.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 10, 2021.
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Individual Level Differential Expression Analysis for Single Cell RNA-seq data
Mengqi Zhang, Si Liu, Zhen Miao, Fang Han, Raphael Gottardo, Wei Sun
bioRxiv 2021.05.10.443350; doi: https://doi.org/10.1101/2021.05.10.443350
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Individual Level Differential Expression Analysis for Single Cell RNA-seq data
Mengqi Zhang, Si Liu, Zhen Miao, Fang Han, Raphael Gottardo, Wei Sun
bioRxiv 2021.05.10.443350; doi: https://doi.org/10.1101/2021.05.10.443350

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