We used WGCNA analysis to further understand the gene expression of the two near-isogenic lines rice in different treatments and to screen out the characteristic genes related to cold tolerance. After filtering raw data in materials and methods, 19 894 genes were retained for WGCNA analysis. The ...
Gene ontology (GO) and KEGG pathway enrichment analysis were performed to gain insights into the biological functions of the hub genes. The integrated Differential Expression and Pathway analysis (iDEP) tool (http://bioinformatics.sdstate.edu/idep/; South Dakota State University, Brookings, USA) ...
WGCNA (weighted gene co-expression network analysis) is a very useful tool for identifying co-expressed gene modules and detecting their correlations to phenotypic traits. Here, we explored more possibilities about it and developed the R... Y Liu - 《Nar Genomics & Bioinformatics》 被引量: 0...
Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because th... U Ala,RM Piro,E Grassi,... - 《Plos Computational Biol...
Gene co-expression analysis reveals conserved and distinct gene networks between resistant and susceptibleLens ervoideschallenged by hemibiotrophic and necrotrophic pathogens ArticleOpen access23 October 2024 A time-course transcriptomic analysis reveals the key responses of a resistant rice cultivar to brown...
Moreover, all of the previous researches have solely focused on differentially expressed genes identification, wherease connectivity analysis has not yet been considered. In contrast to focusing on differentially expressed genes, co-expression module-based network analysis provides new insight into the ...
logFinder [8]), phenome data, or even the combination of both [9]. Irrespective, generating expression data remains a cost-effective approach and co-expression analysis remains a prominent tool for exploratory systemic evaluation, largely because it is capable of considering gene co-expression ...
The advent of high-throughput DNA microarray technology has rendered genome-wide transcriptional analysis a standard practice in biological research. We describe here a simple classifier that reliably discriminates between binary phenotypic states with just two gene expression measurements. Rather than summin...
The above analysis established that PGCNA provided an effective tool for analysis of gene co-expression providing advantages relative to pre-existing methods; producing tractable networks that allow contextualization of all nodes and edges used in network generation and that clusters these into a small...
Gene co-expression network analysis For the construction of a weighted and signed co-expression network, we used the R (version 3.5.1) package CEMiTool51 (version 1.9.3). This offers an implemented unsupervised gene filtering method and more automated parameter selection than the widely used pac...