A unified framework for finding differentially expressed genes from microarray experiments |
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Authors: | Jahangheer S Shaik " target="_blank">Mohammed Yeasin |
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Institution: | (1) Department of Electrical and Computer Engineering, CVPIA Lab, University of Memphis, Memphis, TN-38152, USA;(2) Bioinformatics Program, CVPIA Lab, University of Memphis, Memphis, TN-38152, USA;(3) Biomedical Engineering, CVPIA Lab, University of Memphis, Memphis, TN-38152, USA;(4) 4Center for Advanced Robotics, CVPIA Lab, University of Memphis, Memphis, TN-38152, USA;(5) Software Testing and Excellence Program University of Memphis, Memphis, TN-38152, USA |
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Abstract: | Background This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed
framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first
module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes.
The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's
omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained
DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis.
In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm
on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained
using the unified framework. |
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Keywords: | |
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