Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1186/1471-2105-14-89 | BMC Bioinformatics |
Keywords | Field | DocType |
microarrays,bioinformatics,gene expression profiling,algorithms | Biology,In vitro,Transcriptome,Deconvolution,Gene expression,Sorting,Cell type,Bioinformatics,Genetics,Gene expression profiling,DNA microarray | Journal |
Volume | Issue | ISSN |
14 | 1 | 1471-2105 |
Citations | PageRank | References |
20 | 1.17 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Zhong | 1 | 20 | 1.17 |
| Ying-Wooi Wan | 2 | 32 | 3.24 |
| Kaifang Pang | 3 | 38 | 3.36 |
| Lionel M L Chow | 4 | 20 | 1.17 |
| Zhandong Liu | 5 | 50 | 7.60 |