Record Details

Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.)

KRISHI: Publication and Data Inventory Repository

View Archive Info
 
 
Field Value
 
Title Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.)
Not Available
 
Creator Samarendra Das
Prabina Kumar Meher
Anil Rai
Lal Mohan Bhar
Baidya Nath Mandal
 
Subject informative genes
gene expression data
microarray datasets
Aluminum toxic stress
 
Description Not Available
Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.
Not Available
 
Date 2022-08-07T08:13:41Z
2022-08-07T08:13:41Z
2017-01-05
 
Type Research Paper
 
Identifier Das S, Meher PK, Rai A, Bhar LM, Mandal BN (2017) Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.). PLoS ONE 12(1): e0169605. doi:10.1371/journal.pone.0169605
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/73742
 
Language English
 
Relation Not Available;
 
Publisher Not Available