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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\u2019s disease progression in humans.<\/jats:p>","DOI":"10.1093\/bib\/bbac084","type":"journal-article","created":{"date-parts":[[2022,2,20]],"date-time":"2022-02-20T07:06:41Z","timestamp":1645340801000},"source":"Crossref","is-referenced-by-count":5,"title":["TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell\u2013cell correlation"],"prefix":"10.1093","volume":"23","author":[{"given":"Eric","family":"Van Buren","sequence":"first","affiliation":[{"name":"Department of Biostatistics , Harvard T.H. 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