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Joint analysis of differential gene expression in multiple studies using correlation motifs.

Wei Y, Tenzen T, Ji H - Biostatistics (2014)

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.

View Article: PubMed Central - PubMed

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.

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Results for the SHH data. (a) Motif patterns learned from the SHH data composed of 7 studies. (b) BIC plots for the SHH data. (c) Gene ranking performance for SHH study 1. The genes differentially expressed in dataset 8 (13somites_smo versus 13somites_wt) were obtained using separate limma. They were used as the gold standard. , the number of genes in dataset 1 that are truly differentially expressed among the top  ranked genes by each method, is plotted against the rank cutoff . (d) Differential status claimed by each method for known SHH pathway genes. Dark color indicates differential expression and light color represents non-differential expression.
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KXU038F3: Results for the SHH data. (a) Motif patterns learned from the SHH data composed of 7 studies. (b) BIC plots for the SHH data. (c) Gene ranking performance for SHH study 1. The genes differentially expressed in dataset 8 (13somites_smo versus 13somites_wt) were obtained using separate limma. They were used as the gold standard. , the number of genes in dataset 1 that are truly differentially expressed among the top ranked genes by each method, is plotted against the rank cutoff . (d) Differential status claimed by each method for known SHH pathway genes. Dark color indicates differential expression and light color represents non-differential expression.

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.


Joint analysis of differential gene expression in multiple studies using correlation motifs.

Wei Y, Tenzen T, Ji H - Biostatistics (2014)

Results for the SHH data. (a) Motif patterns learned from the SHH data composed of 7 studies. (b) BIC plots for the SHH data. (c) Gene ranking performance for SHH study 1. The genes differentially expressed in dataset 8 (13somites_smo versus 13somites_wt) were obtained using separate limma. They were used as the gold standard. , the number of genes in dataset 1 that are truly differentially expressed among the top  ranked genes by each method, is plotted against the rank cutoff . (d) Differential status claimed by each method for known SHH pathway genes. Dark color indicates differential expression and light color represents non-differential expression.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263229&req=5

KXU038F3: Results for the SHH data. (a) Motif patterns learned from the SHH data composed of 7 studies. (b) BIC plots for the SHH data. (c) Gene ranking performance for SHH study 1. The genes differentially expressed in dataset 8 (13somites_smo versus 13somites_wt) were obtained using separate limma. They were used as the gold standard. , the number of genes in dataset 1 that are truly differentially expressed among the top ranked genes by each method, is plotted against the rank cutoff . (d) Differential status claimed by each method for known SHH pathway genes. Dark color indicates differential expression and light color represents non-differential expression.
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.

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.

View Article: PubMed Central - PubMed

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