Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data.
Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell hete...
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Public Library of Science (PLoS)
2021-03-01
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| Series: | PLoS Genetics |
| Online Access: | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1009080&type=printable |
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| author | Jiaxin Fan Xuran Wang Rui Xiao Mingyao Li |
| author_facet | Jiaxin Fan Xuran Wang Rui Xiao Mingyao Li |
| author_sort | Jiaxin Fan |
| collection | DOAJ |
| description | Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provides a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases. |
| format | Article |
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| institution | DOAJ |
| issn | 1553-7390 1553-7404 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS Genetics |
| spelling | doaj-art-a63fa24dfdc94d4099bc592971dc71a72025-08-20T03:22:21ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042021-03-01173e100908010.1371/journal.pgen.1009080Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data.Jiaxin FanXuran WangRui XiaoMingyao LiAllelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provides a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1009080&type=printable |
| spellingShingle | Jiaxin Fan Xuran Wang Rui Xiao Mingyao Li Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data. PLoS Genetics |
| title | Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data. |
| title_full | Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data. |
| title_fullStr | Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data. |
| title_full_unstemmed | Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data. |
| title_short | Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data. |
| title_sort | detecting cell type specific allelic expression imbalance by integrative analysis of bulk and single cell rna sequencing data |
| url | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1009080&type=printable |
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