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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 comparisons of DE ratios and DE gene median counts for NSVA, PCA, and NFS feature selection methods under DESeq analysis on the Kidney–Liver and Prostate data. The proposed NSVA feature selection demonstrated strong advantages in selecting potential DE genes than the two competing methods. The DE gene median counts from NSVA are generally lower than those of PCA and NFS.
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f3-cin-suppl.1-2014-081: The comparisons of DE ratios and DE gene median counts for NSVA, PCA, and NFS feature selection methods under DESeq analysis on the Kidney–Liver and Prostate data. The proposed NSVA feature selection demonstrated strong advantages in selecting potential DE genes than the two competing methods. The DE gene median counts from NSVA are generally lower than those of PCA and NFS.

Mentions: The northeast and northwest plots in Figure 3 compared the DE ratios from the three feature selection methods: NSVA, PCA, and NFS under DESeq analysis on corresponding 2000, 3000, 5000, and 8000 selected genes from the two original RNA-Seq datasets. The DE ratio was defined as the ratio of DE genes among all the genes of input data. It was interesting to see that the DE ratios from NSVA feature selection were much higher than those of NFS and PCA feature selection for all selection cases of two datasets. Since we only employed DESeq for DE analysis for all datasets, it was clear that the proposed NSVA feature selection demonstrated its advantage in selecting potential DE genes than the NFS and PCA feature selection.


Disease Biomarker Query from RNA-Seq Data.

Han H, Jiang X - Cancer Inform (2014)

The comparisons of DE ratios and DE gene median counts for NSVA, PCA, and NFS feature selection methods under DESeq analysis on the Kidney–Liver and Prostate data. The proposed NSVA feature selection demonstrated strong advantages in selecting potential DE genes than the two competing methods. The DE gene median counts from NSVA are generally lower than those of PCA and NFS.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3-cin-suppl.1-2014-081: The comparisons of DE ratios and DE gene median counts for NSVA, PCA, and NFS feature selection methods under DESeq analysis on the Kidney–Liver and Prostate data. The proposed NSVA feature selection demonstrated strong advantages in selecting potential DE genes than the two competing methods. The DE gene median counts from NSVA are generally lower than those of PCA and NFS.
Mentions: The northeast and northwest plots in Figure 3 compared the DE ratios from the three feature selection methods: NSVA, PCA, and NFS under DESeq analysis on corresponding 2000, 3000, 5000, and 8000 selected genes from the two original RNA-Seq datasets. The DE ratio was defined as the ratio of DE genes among all the genes of input data. It was interesting to see that the DE ratios from NSVA feature selection were much higher than those of NFS and PCA feature selection for all selection cases of two datasets. Since we only employed DESeq for DE analysis for all datasets, it was clear that the proposed NSVA feature selection demonstrated its advantage in selecting potential DE genes than the NFS and PCA feature selection.

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.