Abstract

Complexity of cell-type composition has created much skepticism surrounding the interpretation of brain bulk-tissue transcriptomic studies. We generated paired tissue genome-wide gene expression data and immunohistochemistry data, enabling us to assess statistical methods for modeling and estimating cellular heterogeneity in the brain. We demonstrate that several algorithms that rely on single-cell and cell-sorted data to define cell marker gene sets yield accurate relative and absolute estimates of constituent cell-type proportions.