Deconvolving the contributions of cell-type heterogeneity on cortical gene expression

Ellis Patrick, Mariko Taga, Ayla Ergün, Bernard Ng, William Casazza, Maria Cimpean, Christina Yung, Julie A. Schneider, David A. Bennett, Chris Gaiteri, Philip L. De Jager, Elizabeth M. Bradshaw, Sara Mostafavi

Abstract: Author summary Gene expression data generated from a tissue sample reflects an average gene expression profile across heterogeneous populations of cells. Because composition of constituent cell-types can vary across individuals (due to technical or biological factors), differential gene expression analysis requires estimating and adjusting for such cellular heterogeneity. While many deconvolution algorithms for estimating cellular composition from tissue gene expression data have been tested extensively in blood, their performance when applied to brain tissue is unclear. To address this gap, we generated an immunohistochemistry (IHC) dataset for five major cell-types from brain, in order to apply and then assess deconvolution algorithms for application to brain gene expression datasets. We show that these algorithms can indeed produce informative estimates of constituent cell-type proportions. Further, we show that adjusting for estimated cell-type proportions across individuals when conducting differential gene expression analysis is important in reducing false associations.