| 214 | | Often one is interested in how a treatment or condition alters the gene expression profile in a selected cell type. In the desirable case where multiple biological replicates are present for each condition, K. D. Zimmerman, M. A. Espeland and Carl D. Langefeld have recently [https://www.nature.com/articles/s41467-021-21038-1 highlighted] the importance of properly taking account of the correlation present in such hierarchically structured data. One strategy, the so-called pseudobulk approach, is to aggregate counts across cells from the same biological sample or subject. Mixed-effects modeling, where sample is treated as a random effect, is another strategy. The code below uses mixed effects modeling within [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0844-5 MAST] and has been adapted from [https://github.com/kdzimm/PseudoreplicationPaper/blob/master/Type_1_Error/Type%201%20-%20MAST%20RE.Rmd K. D. Zimmerman et al.]. Note that the additional complexity and potential benefit of these mixed-effects models are accompanied by increased computational expense: fitting these models to thousands of genes in thousands of cells can be slow. A vignette outlining how to use MAST for differential expression in the more traditional fixed-effect mode (i.e. ''without'' including any random effects) can be found [https://www.bioconductor.org/packages/release/bioc/vignettes/MAST/inst/doc/MAITAnalysis.html | here]. |
| | 214 | Often one is interested in how a treatment or condition alters the gene expression profile in a selected cell type. In the desirable case where multiple biological replicates are present for each condition, K. D. Zimmerman, M. A. Espeland and C. D. Langefeld have recently [https://www.nature.com/articles/s41467-021-21038-1 highlighted] the importance of properly taking account of the correlation present in such hierarchically structured data. One strategy, the so-called pseudobulk approach, is to aggregate counts across cells from the same biological sample or subject (for examples of how to implement pseudobulk models see this [https://biocellgen-public.svi.edu.au/mig_2019_scrnaseq-workshop/public/dechapter.html link]). Mixed-effects modeling, where sample is treated as a random effect, is another strategy. The code below uses mixed effects modeling within [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0844-5 MAST] and has been adapted from [https://github.com/kdzimm/PseudoreplicationPaper/blob/master/Type_1_Error/Type%201%20-%20MAST%20RE.Rmd K. D. Zimmerman et al.]. Note that the additional complexity and potential benefit of these mixed-effects models are accompanied by increased computational expense: fitting these models to thousands of genes in thousands of cells can be slow. A vignette outlining how to use MAST for differential expression in the more traditional fixed-effect mode (i.e. ''without'' including any random effects) can be found [https://www.bioconductor.org/packages/release/bioc/vignettes/MAST/inst/doc/MAITAnalysis.html | here]. |
