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. 2013;9(8):e1003189.
doi: 10.1371/journal.pcbi.1003189. Epub 2013 Aug 22.

A self-directed method for cell-type identification and separation of gene expression microarrays

Neta S Zuckerman  1 , Yair Noam, Andrea J Goldsmith, Peter P Lee
Affiliations

A self-directed method for cell-type identification and separation of gene expression microarrays

Neta S Zuckerman et al. PLoS Comput Biol. 2013.

Abstract

Gene expression analysis is generally performed on heterogeneous tissue samples consisting of multiple cell types. Current methods developed to separate heterogeneous gene expression rely on prior knowledge of the cell-type composition and/or signatures--these are not available in most public datasets. We present a novel method to identify the cell-type composition, signatures and proportions per sample without need for a-priori information. The method was successfully tested on controlled and semi-controlled datasets and performed as accurately as current methods that do require additional information. As such, this method enables the analysis of cell-type specific gene expression using existing large pools of publically available microarray datasets.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Blind separation yields accurate estimations of separated cell-type gene-expression.
Gene expression measurements of each separated cell-type plotted against the gene expression of its corresponding purified cell type from the same study, in (A) liver-brain-lung dataset , (B) heart-brain dataset , and (C) T-B-Monocytes dataset . Correlation coefficients between the separated and purified gene expressions are denoted for each cell type. CT = cell-type, r = correlation coefficient.
Figure 2
Figure 2. Blind separation yields accurate estimations of separated cell-type proportions.
Known cell-type proportions plotted against the estimated cell-type proportions in (A) liver-brain-lung dataset , (B) heart-brain dataset , (C) T-B-Monocytes dataset , and (D) prostate cancer dataset . Correlation coefficients between the known and estimated proportions are denoted for each cell-type.
Figure 3
Figure 3. Blind separation yields accurate estimates of cell-type proportions per sample.
Comparisons between the known cell-type proportions and the estimated proportions per-sample for cell-types in the controlled datasets. (A) The liver-brain-lung dataset included 33 samples, (B) The heart-brain dataset included 27 samples, and (C) The T-B-Monocytes dataset included 12 samples. Known proportions are shown in the grey columns, denoted by “s” and were measured as triplicates; estimates yielded by the blind separation algorithm are shown in the white columns and are denoted by “e”.
See this image and copyright information in PMC

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