Joint analysis of differential gene expression in multiple studies using correlation motifs.
Bottom Line: The motifs provide the basis for sharing information among studies and genes.The approach has flexibility to handle all possible study-specific differential patterns.It improves detection of differential expression and overcomes the barrier of exponential model complexity.
Affiliation: Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USADepartment of Statistics, The Chinese University of Hong Kong, Shatin NT, Hong Kong.Show MeSH
Mentions: Five motifs were discovered (Figure 3(a) and (b)). Motif 1 mainly represents background. Motif 2 contains genes that have high probability to be differential in all studies. Genes in motif 3 tend to be differential in most studies except for the two involving PTCH1 mutant (i.e. studies 2 and 3). Most genes in motif 4 are not differential in the two studies involving the SHH mutant (i.e. studies 4 and 5) but tend to be differential in all other studies. Motif 5 mainly represents genes differential in tumors (i.e. studies 6 and 7) but not in embryonic development (i.e. studies 1–5). In general, looking at the columns in Figure 3(a), the two studies involving tumors (6,7) are more similar to each other compared with other studies. The two PTCH1 mutant studies (2,3) are also relatively similar, and the same trend holds true for the two SHH mutant studies (4,5).Fig. 3.
Affiliation: Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USADepartment of Statistics, The Chinese University of Hong Kong, Shatin NT, Hong Kong.