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Disease Biomarker Query from RNA-Seq Data.

Han H, Jiang X - Cancer Inform (2014)

Bottom Line: Although there were many biomarker discovery algorithms available in traditional omics communities, they cannot be applied to RNA-Seq count data to seek biomarkers directly for its special characteristics.In this work, we have presented a biomarker discovery algorithm, SEQ-Marker for RNA-Seq data, which is built on a novel data-driven feature selection algorithm, nonnegative singular value approximation (NSVA), which contributes to the robustness and sensitivity of the following DE analysis by taking advantages of the built-in characteristics of RNA-Seq count data.As a biomarker discovery algorithm built on network marker topology, the proposed SEQ-Marker not only bridges transcriptomics and systems biology but also contributes to clinical diagnostics.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Information Science, Fordham University, New York, NY, USA. ; Quantitative Proteomics Center, Columbia University, New York, NY, USA.

ABSTRACT
As a revolutionary way to unveil transcription, RNA-Seq technologies are challenging bioinformatics for its large data volumes and complexities. A large number of computational models have been proposed for differential expression (DE) analysis and normalization from different standing points. However, there were no studies available yet to conduct disease biomarker discovery for this type of high-resolution digital gene expression data, which will actually be essential to explore its potential in clinical bioinformatics. Although there were many biomarker discovery algorithms available in traditional omics communities, they cannot be applied to RNA-Seq count data to seek biomarkers directly for its special characteristics. In this work, we have presented a biomarker discovery algorithm, SEQ-Marker for RNA-Seq data, which is built on a novel data-driven feature selection algorithm, nonnegative singular value approximation (NSVA), which contributes to the robustness and sensitivity of the following DE analysis by taking advantages of the built-in characteristics of RNA-Seq count data. As a biomarker discovery algorithm built on network marker topology, the proposed SEQ-Marker not only bridges transcriptomics and systems biology but also contributes to clinical diagnostics.

No MeSH data available.


The plots of 1801 DE genes and 199 non-DE genes of 2000 genes selected by NSVA for the Kidney–Liver data. Unlike the DE genes, the non-DE genes have fold changes in a quite small range.
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f5-cin-suppl.1-2014-081: The plots of 1801 DE genes and 199 non-DE genes of 2000 genes selected by NSVA for the Kidney–Liver data. Unlike the DE genes, the non-DE genes have fold changes in a quite small range.

Mentions: We applied our SEQ-Marker algorithm to seek biomarkers for the Kidney–Liver data. At first, we applied DESeq analysis to 2000 genes selected by NSVA, which consisted of 1801 DE genes and 199 non-DE genes. Figure 5 illustrated those non-DE genes, DE genes, and all 2000 genes in the left, middle, and right plots, respectively. It was interesting to see that the non-DE genes had fold changes in a much smaller range than the DE genes, which guaranteed that 90% genes in network marker inference were DE genes and leaded to more meaningful network markers.


Disease Biomarker Query from RNA-Seq Data.

Han H, Jiang X - Cancer Inform (2014)

The plots of 1801 DE genes and 199 non-DE genes of 2000 genes selected by NSVA for the Kidney–Liver data. Unlike the DE genes, the non-DE genes have fold changes in a quite small range.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4216051&req=5

f5-cin-suppl.1-2014-081: The plots of 1801 DE genes and 199 non-DE genes of 2000 genes selected by NSVA for the Kidney–Liver data. Unlike the DE genes, the non-DE genes have fold changes in a quite small range.
Mentions: We applied our SEQ-Marker algorithm to seek biomarkers for the Kidney–Liver data. At first, we applied DESeq analysis to 2000 genes selected by NSVA, which consisted of 1801 DE genes and 199 non-DE genes. Figure 5 illustrated those non-DE genes, DE genes, and all 2000 genes in the left, middle, and right plots, respectively. It was interesting to see that the non-DE genes had fold changes in a much smaller range than the DE genes, which guaranteed that 90% genes in network marker inference were DE genes and leaded to more meaningful network markers.

Bottom Line: Although there were many biomarker discovery algorithms available in traditional omics communities, they cannot be applied to RNA-Seq count data to seek biomarkers directly for its special characteristics.In this work, we have presented a biomarker discovery algorithm, SEQ-Marker for RNA-Seq data, which is built on a novel data-driven feature selection algorithm, nonnegative singular value approximation (NSVA), which contributes to the robustness and sensitivity of the following DE analysis by taking advantages of the built-in characteristics of RNA-Seq count data.As a biomarker discovery algorithm built on network marker topology, the proposed SEQ-Marker not only bridges transcriptomics and systems biology but also contributes to clinical diagnostics.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Information Science, Fordham University, New York, NY, USA. ; Quantitative Proteomics Center, Columbia University, New York, NY, USA.

ABSTRACT
As a revolutionary way to unveil transcription, RNA-Seq technologies are challenging bioinformatics for its large data volumes and complexities. A large number of computational models have been proposed for differential expression (DE) analysis and normalization from different standing points. However, there were no studies available yet to conduct disease biomarker discovery for this type of high-resolution digital gene expression data, which will actually be essential to explore its potential in clinical bioinformatics. Although there were many biomarker discovery algorithms available in traditional omics communities, they cannot be applied to RNA-Seq count data to seek biomarkers directly for its special characteristics. In this work, we have presented a biomarker discovery algorithm, SEQ-Marker for RNA-Seq data, which is built on a novel data-driven feature selection algorithm, nonnegative singular value approximation (NSVA), which contributes to the robustness and sensitivity of the following DE analysis by taking advantages of the built-in characteristics of RNA-Seq count data. As a biomarker discovery algorithm built on network marker topology, the proposed SEQ-Marker not only bridges transcriptomics and systems biology but also contributes to clinical diagnostics.

No MeSH data available.