Limits...
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 flowchart of the proposed SEQ-Marker algorithm. The SEQ-Marker algorithm consists of the following main components: a data-driven feature selection algorithm: NSVA; a “new” DE analysis method: NSVA-DESeq, by integrating our NSVA feature selection with the parametric DESeq analysis; and a novel network marker-oriented biomarker identification search strategy.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4216051&req=5

f1-cin-suppl.1-2014-081: The flowchart of the proposed SEQ-Marker algorithm. The SEQ-Marker algorithm consists of the following main components: a data-driven feature selection algorithm: NSVA; a “new” DE analysis method: NSVA-DESeq, by integrating our NSVA feature selection with the parametric DESeq analysis; and a novel network marker-oriented biomarker identification search strategy.

Mentions: The reason we picked jActiveModule to infer network markers was mainly because it only required the expression data and P-values and did not have specific data distribution assumption, although it was developed for normally distributed gene expression array data, in addition to the fact that it has more robust support from Cytoscape and its related plugins than other peers.25,26 It is noted that we employed jActiveModule 1.8 in Cytoscape 3.02 in our network marker inference. Figure 1 illustrated the flowchart of the proposed SEQ-Marker algorithm, which consisted of the following steps.


Disease Biomarker Query from RNA-Seq Data.

Han H, Jiang X - Cancer Inform (2014)

The flowchart of the proposed SEQ-Marker algorithm. The SEQ-Marker algorithm consists of the following main components: a data-driven feature selection algorithm: NSVA; a “new” DE analysis method: NSVA-DESeq, by integrating our NSVA feature selection with the parametric DESeq analysis; and a novel network marker-oriented biomarker identification search strategy.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1-cin-suppl.1-2014-081: The flowchart of the proposed SEQ-Marker algorithm. The SEQ-Marker algorithm consists of the following main components: a data-driven feature selection algorithm: NSVA; a “new” DE analysis method: NSVA-DESeq, by integrating our NSVA feature selection with the parametric DESeq analysis; and a novel network marker-oriented biomarker identification search strategy.
Mentions: The reason we picked jActiveModule to infer network markers was mainly because it only required the expression data and P-values and did not have specific data distribution assumption, although it was developed for normally distributed gene expression array data, in addition to the fact that it has more robust support from Cytoscape and its related plugins than other peers.25,26 It is noted that we employed jActiveModule 1.8 in Cytoscape 3.02 in our network marker inference. Figure 1 illustrated the flowchart of the proposed SEQ-Marker algorithm, which consisted of the following steps.

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.