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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 scatter plots of log2 mean versus log2 fold changes by comparing DESeq and NSVA-DESeq on Kidney–Liver and Prostate data, where 2000, 3000, 5000, and 8000 genes are selected by NSVA from each data, and DESeq analysis is then applied to these selected genes and their original datasets, respectively. It is interesting to see that non-DE genes dropped remarkably when NSVA feature selection is applied to each dataset.
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f2-cin-suppl.1-2014-081: The scatter plots of log2 mean versus log2 fold changes by comparing DESeq and NSVA-DESeq on Kidney–Liver and Prostate data, where 2000, 3000, 5000, and 8000 genes are selected by NSVA from each data, and DESeq analysis is then applied to these selected genes and their original datasets, respectively. It is interesting to see that non-DE genes dropped remarkably when NSVA feature selection is applied to each dataset.

Mentions: Figure 2 answered the query by comparing NSVA-DESeq with DESeq on the two dataset, where NSVA selected 2000, 3000, 5000, and 8000 genes from each data and DESeq analysis was applied to these selected genes and their original datasets, respectively. The FDR cutoff was chosen as 0.001 in all DE analyses. Each horizontal and vertical axis in the sub-plots represents the log2 mean of each gene and the corresponding log2 fold changes under two different conditions. We had the following interesting findings from these results.


Disease Biomarker Query from RNA-Seq Data.

Han H, Jiang X - Cancer Inform (2014)

The scatter plots of log2 mean versus log2 fold changes by comparing DESeq and NSVA-DESeq on Kidney–Liver and Prostate data, where 2000, 3000, 5000, and 8000 genes are selected by NSVA from each data, and DESeq analysis is then applied to these selected genes and their original datasets, respectively. It is interesting to see that non-DE genes dropped remarkably when NSVA feature selection is applied to each dataset.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2-cin-suppl.1-2014-081: The scatter plots of log2 mean versus log2 fold changes by comparing DESeq and NSVA-DESeq on Kidney–Liver and Prostate data, where 2000, 3000, 5000, and 8000 genes are selected by NSVA from each data, and DESeq analysis is then applied to these selected genes and their original datasets, respectively. It is interesting to see that non-DE genes dropped remarkably when NSVA feature selection is applied to each dataset.
Mentions: Figure 2 answered the query by comparing NSVA-DESeq with DESeq on the two dataset, where NSVA selected 2000, 3000, 5000, and 8000 genes from each data and DESeq analysis was applied to these selected genes and their original datasets, respectively. The FDR cutoff was chosen as 0.001 in all DE analyses. Each horizontal and vertical axis in the sub-plots represents the log2 mean of each gene and the corresponding log2 fold changes under two different conditions. We had the following interesting findings from these results.

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.