TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell–cell correlation
Year: 2022
Type: article
Abstract: We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the gene-level in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared with other methods. Application to two datasets identified HIV-associated interferon pathways in xenograft mice and pathways associated with Alzheimer's disease progression in humans.
Source: Briefings in Bioinformatics
DOI: 10.1093/bib/bbac084
Authors: Eric Van Buren, Ming Hu, Liang Cheng, John Wrobel, Kirk Wilhelmsen, Lishan Su, Yun Li, Di Wu
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funders: National Institutes of Health, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Dental and Craniofacial Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Modeling immune impairments and pathogenesis in novel humanized mice for HBV-HIV co-infection, Investigating the microbial basis of early childhood caries via metagenomics and metatranscriptomics analyses, Clinical Translational Research Center for Neurodevelopmental Disorders, Genetic Studies of Blood Cell Traits in Multi-Ethnic Cohorts, Blocking type I interferon signaling to reverse T cell exhaustion and control HIV-1 reservoirs, Center for Integrated Multi-modal and Multi-scale Nucleome Research, HIV-1 Vpr disrupts the IFN-TET-ISG pathway to promote HIV-1 infection and persistence, Statistical Methods for RNA-seq Data Analysis
OpenAlex ID: W4213164285