Name
Title | ||
|---|---|---|
A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis. |
Abstract | ||
|---|---|---|
We developed a novel empirical Bayes based mixture model to identify DE genes in specific study by leveraging the shared information across multiple different disease expression data sets. The effectiveness of joint analysis was demonstrated through comprehensive simulation studies and two real data applications. The simulation results showed that our method consistently outperformed single data set analysis and two other meta-analysis methods in identification power. In real data analysis, overall our method demonstrated better identification power in detecting DE genes and prioritized more disease related genes and disease related pathways than single data set analysis. Over 150% more disease related genes are identified by our method in application to Huntington's disease. We expect that our method would provide researchers a new way of utilizing available data sets from different diseases when sample size of the focused disease is limited. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s13040-018-0163-y | BioData Mining |
Keywords | Field | DocType |
Cross disease transcriptome,Differentially expressed,Gene expression,Public data integration | Data mining,Disease,Data set,Text mining,Gene,Computer science,Statistical power,Sample size determination,Mixture model,Bayes' theorem | Journal |
Volume | Issue | ISSN |
11 | 1 | 1756-0381 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
2 | ||