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SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples.

Yalamanchili HK, Li Z, Wang P, Wong MP, Yao J, Wang J - Nucleic Acids Res. (2014)

Bottom Line: It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets.Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data.SpliceNet can also be applied to exon array data.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, The University of Hong Kong, Hong Kong (SAR), China Department of Pathology, The University of Hong Kong, Hong Kong (SAR), China.

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Illustration of inferring differential cancer co-expression network: isoform-specific co-expression network inferred from (a) Normal samples, (b) cancer samples and (c) differential cancer network. Solid lines in red and blue are the edges lost and gained in cancer samples respectively when compared to normal samples. Dotted lines are the removed common edges.
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Figure 2: Illustration of inferring differential cancer co-expression network: isoform-specific co-expression network inferred from (a) Normal samples, (b) cancer samples and (c) differential cancer network. Solid lines in red and blue are the edges lost and gained in cancer samples respectively when compared to normal samples. Dotted lines are the removed common edges.

Mentions: A differential cancer co-expression network is defined as a network with co-expression edges that are either observed only in cancer or in normal samples. Firstly, two independent co-expression networks are inferred using the proposed methods from tumor-matched and normal-matched RNA-Seq samples respectively. Then, a graph comparison operation is performed to remove all common edges. The remainder, differential co-expression edges can be ranked based on the corresponding P-values. According to Figure 2a, in normal samples isoform I1,1 of gene G1 is co-expressed with isoforms I2,1 and I2,3 of gene G2, and I1,2 of G1 with I2,3 of G2. On the other hand, in cancer samples (Figure 2b), I1,1 of G1 is co-expressed with I2,1 and I2,2 of G2, and I1,2 of G1 with I2,3 of G2. A differential cancer co-expression network in constructed by removing common edges, I1,1– I2,1 and I1,2– I2,3. Thus the resultant differential network (Figure 2c) has two edges, I1,1– I2,2 (blue) and I1,1– I2,3 (red).


SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples.

Yalamanchili HK, Li Z, Wang P, Wong MP, Yao J, Wang J - Nucleic Acids Res. (2014)

Illustration of inferring differential cancer co-expression network: isoform-specific co-expression network inferred from (a) Normal samples, (b) cancer samples and (c) differential cancer network. Solid lines in red and blue are the edges lost and gained in cancer samples respectively when compared to normal samples. Dotted lines are the removed common edges.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150760&req=5

Figure 2: Illustration of inferring differential cancer co-expression network: isoform-specific co-expression network inferred from (a) Normal samples, (b) cancer samples and (c) differential cancer network. Solid lines in red and blue are the edges lost and gained in cancer samples respectively when compared to normal samples. Dotted lines are the removed common edges.
Mentions: A differential cancer co-expression network is defined as a network with co-expression edges that are either observed only in cancer or in normal samples. Firstly, two independent co-expression networks are inferred using the proposed methods from tumor-matched and normal-matched RNA-Seq samples respectively. Then, a graph comparison operation is performed to remove all common edges. The remainder, differential co-expression edges can be ranked based on the corresponding P-values. According to Figure 2a, in normal samples isoform I1,1 of gene G1 is co-expressed with isoforms I2,1 and I2,3 of gene G2, and I1,2 of G1 with I2,3 of G2. On the other hand, in cancer samples (Figure 2b), I1,1 of G1 is co-expressed with I2,1 and I2,2 of G2, and I1,2 of G1 with I2,3 of G2. A differential cancer co-expression network in constructed by removing common edges, I1,1– I2,1 and I1,2– I2,3. Thus the resultant differential network (Figure 2c) has two edges, I1,1– I2,2 (blue) and I1,1– I2,3 (red).

Bottom Line: It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets.Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data.SpliceNet can also be applied to exon array data.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, The University of Hong Kong, Hong Kong (SAR), China Department of Pathology, The University of Hong Kong, Hong Kong (SAR), China.

Show MeSH
Related in: MedlinePlus