Deconvolving the contributions of cell-type heterogeneity on cortical gene expression
作者： Ellis Patrick , Mariko Taga , Ayla Ergun , Bernard Ng , William Casazza
DOI: 10.1371/JOURNAL.PCBI.1008120
关键词:
摘要: Complexity of cell-type composition has created much skepticism surrounding the interpretation bulk tissue transcriptomic studies. Recent studies have shown that deconvolution algorithms can be applied to computationally estimate proportions from gene expression data blood samples, but their performance when brain is unclear. Here, we generated an immunohistochemistry (IHC) dataset for five major cell-types 70 individuals, who also cortical data. With IHC as benchmark, this resource enables quantitative assessment tissue. We apply existing by using marker sets derived human single cell and cell-sorted RNA-seq show these indeed produce informative estimates constituent proportions. In fact, neuronal subpopulations estimated samples. Further, including proportion confounding factors important reducing false associations between Alzheimer's disease phenotypes expression. Lastly, demonstrate more accurate substantially improve statistical power in detecting specific trait loci (eQTLs).
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