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Updated: Jan 21, 2026
Using Fluorescence Activated Cell Sorting to Examine Cell-Type-Specific Gene Expression in Rat Brain Tissue
Published on: May 28, 2015
Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression
Jiebiao Wang1, Bernie Devlin2, Kathryn Roeder1,3
1Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Motivation:
Patterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects.
Results:
Complementing these approaches, we develop a method to estimate subject- and cell-type-specific (CTS) gene expression from tissue using an empirical Bayes method that borrows information across multiple measurements of the same tissue per subject (e.g. multiple regions of the brain). Analyzing expression data from multiple brain regions from the Genotype-Tissue Expression project (GTEx) reveals CTS expression, which then permits downstream analyses, such as identification of CTS expression Quantitative Trait Loci (eQTL).
Availability And Implementation:
We implement this method as an R package MIND, hosted on https://github.com/randel/MIND.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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