Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

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

For all author emails, please log on.

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

Published: 7 March 2013

Additional files

Additional file 1: Table S1:

Marker genes for liver, brain and lung

Format: TXT Size: 4KB Download file

Open Data

Additional file 2: Figure S1:

Correlation and mean absolute difference between DSA estimation and original cell specific expression using various number of marker genes. The experiment was repeated 100 times on each number of marker genes. Result show that DSA was robust to the number of marker genes used, even with small number of marker genes.

Format: PDF Size: 171KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 3: Table S2:

Marker genes for cells of the immune system.

Format: TXT Size: 202KB Download file

Open Data

Additional file 4: Figure S2:

DSA estimation of T-cells, B-cells, and monocytes. Cell type specific markers were extracted from Immune Response In Silico database. Using these markers, DSA was able to faithfully identify the gene expression profile of B-cells, T-cells, and monocytes from mixture samples.

Format: PDF Size: 1.1MB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 5: Figure S3:

AUC analysis for differential gene analysis. Differential gene expression analysis using estimated pure cell gene expression profiles was able to accurately identify genes that are differentially expressed between different cell types.

Format: PDF Size: 856KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 6: Table S3:

Cell type proportions for simulated blood samples.

Format: TXT Size: 2KB Download file

Open Data

Additional file 7: Table S4:

Marker genes for Macrophages.

Format: TXT Size: 1KB Download file

Open Data