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Open Access Highly Accessed Methodology article

Digital sorting of complex tissues for cell type-specific gene expression profiles

Yi Zhong12, Ying-Wooi Wan123, Kaifang Pang12, Lionel ML Chow4 and Zhandong Liu12*

Author Affiliations

1 Department of Pediatrics, Neurological Research Institute, Baylor College of Medicine, Houston, Texas, USA

2 Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas, USA

3 Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, Texas, USA

4 Cancer and Blood Diseases Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA

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BMC Bioinformatics 2013, 14:89  doi:10.1186/1471-2105-14-89

Published: 7 March 2013



Cellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvolution algorithms require prior knowledge of the cell type frequencies within a tissue or their in vitro expression profiles. Furthermore, these algorithms tend to report biased estimations.


Here, we describe a Digital Sorting Algorithm (DSA) for extracting cell-type specific gene expression profiles from mixed tissue samples that is unbiased and does not require prior knowledge of cell type frequencies.


The results suggest that DSA is a specific and sensitivity algorithm in gene expression profile deconvolution and will be useful in studying individual cell types of complex tissues.