Cargando…
iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRN...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463720/ https://www.ncbi.nlm.nih.gov/pubmed/37608264 http://dx.doi.org/10.1186/s12859-023-05432-8 |
_version_ | 1785098298202259456 |
---|---|
author | Liu, Yunqing Zhao, Jiayi Adams, Taylor S. Wang, Ningya Schupp, Jonas C. Wu, Weimiao McDonough, John E. Chupp, Geoffrey L. Kaminski, Naftali Wang, Zuoheng Yan, Xiting |
author_facet | Liu, Yunqing Zhao, Jiayi Adams, Taylor S. Wang, Ningya Schupp, Jonas C. Wu, Weimiao McDonough, John E. Chupp, Geoffrey L. Kaminski, Naftali Wang, Zuoheng Yan, Xiting |
author_sort | Liu, Yunqing |
collection | PubMed |
description | BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. RESULTS: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. CONCLUSIONS: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05432-8. |
format | Online Article Text |
id | pubmed-10463720 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104637202023-08-30 iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects Liu, Yunqing Zhao, Jiayi Adams, Taylor S. Wang, Ningya Schupp, Jonas C. Wu, Weimiao McDonough, John E. Chupp, Geoffrey L. Kaminski, Naftali Wang, Zuoheng Yan, Xiting BMC Bioinformatics Research Article BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. RESULTS: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. CONCLUSIONS: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05432-8. BioMed Central 2023-08-22 /pmc/articles/PMC10463720/ /pubmed/37608264 http://dx.doi.org/10.1186/s12859-023-05432-8 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Liu, Yunqing Zhao, Jiayi Adams, Taylor S. Wang, Ningya Schupp, Jonas C. Wu, Weimiao McDonough, John E. Chupp, Geoffrey L. Kaminski, Naftali Wang, Zuoheng Yan, Xiting iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects |
title | iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects |
title_full | iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects |
title_fullStr | iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects |
title_full_unstemmed | iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects |
title_short | iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects |
title_sort | idesc: identifying differential expression in single-cell rna sequencing data with multiple subjects |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463720/ https://www.ncbi.nlm.nih.gov/pubmed/37608264 http://dx.doi.org/10.1186/s12859-023-05432-8 |
work_keys_str_mv | AT liuyunqing idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects AT zhaojiayi idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects AT adamstaylors idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects AT wangningya idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects AT schuppjonasc idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects AT wuweimiao idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects AT mcdonoughjohne idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects AT chuppgeoffreyl idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects AT kaminskinaftali idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects AT wangzuoheng idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects AT yanxiting idescidentifyingdifferentialexpressioninsinglecellrnasequencingdatawithmultiplesubjects |