There is a large interest in the scientific community to use brain expression studies to try to identity novel pathogenic mechanisms in AD and to identify novel therapeutic targets. These efforts are generating a large amount of bulk RNA-seq data, as single-cell RNA (scRNA-seq) from human brain tissue in large sample sizes is not feasible. Single-cell sorting needs to be performed with fresh tissue [74], which restrains the analysis of highly characterized fresh-frozen brains collected by AD research centers. Our results indicate that digital deconvolution methods can accurately infer relative cell distributions from brain bulk RNA-seq data, but we recognize the importance of obtaining traditional neuropathological measures to validate the results we observed. Having this approach validated for AD can have an important impact in the community, because digital deconvolution analyses can: (1) reveal distinct cellular composition patterns underlying different disease etiologies; (2) provide additional insights about the overall pathologic mechanisms underlying different mutations carriers for variants as in genes such as TREM2, APOE, APP, PSEN1, and PSEN2; (3) correct the effect that altered cell composition and genetic statuses have
