Statistical expression deconvolution from mixed tissue samples

Abstract

Motivation: Global expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately, tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. However, statistical analysis can be used to identify differentially expressed genes that are related to the biological question being studied.

Results: We propose a statistical approach to expression deconvolution from mixed tissue samples in which the proportion of each component cell type is unknown. Our method estimates the proportion of each component in a mixed tissue sample; this estimate can be used to provide estimates of gene expression from each component. We demonstrate our technique on xenograft samples from breast cancer research and publicly available experimental datasets found in the National Center for Biotechnology Information Gene Expression Omnibus repository.

Availability: R code (http://www.r-project.org/) for estimating sample proportions is freely available to non-commercial users and available at http://www.med.miami.edu/medicine/x2691.xml.

Fig. 1.

Rank-sorted ratios (R_i) from ‘electronic’ data across values of p_A

Fig. 2.

Rank-sorted ratios (R_i) from ‘electronic’ titration data (dark) and observed titration data (light) for proportion values p_A = 0.25, 0.5 and 0.75. Note the qualitatively different curves caused by noise in the observed data.

Fig. 3.

Values of and md(tR(α)) as functions of α for (a) MDA231/mouse titration data at p_A=0.5 and (b) MAQC human titration data at p_A=0.75. The vertical line indicates the correct value of α.

Fig. 4.

Bootstrap estimates of p_A with 90% confidence intervals. Boxes indicate the point estimates of p_A; light grey vertical lines indicate the true values of p_A. (a) MDA231 qspline-normalized data; (b) MCF7 quantile-normalized data; (c) MAQC ILM cubic spline-normalized data; (d) MAQC Affymetrix MAS5 data; (e) BIIB 500 Affymetrix MAS5 data; and (f) BIIB 100 Affymetrix MAS5 data.