Ranking genome-wide correlation measurements improves microarray and RNA-seq based global and targeted co-expression networks.

ABSTRACT: Co-expression networks are essential tools to infer biological associations between gene products and predict gene annotation. Global networks can be analyzed at the transcriptome-wide scale or after querying them with a set of guide genes to capture the transcriptional landscape of a given pathway in a process named Pathway Level Coexpression (PLC). A critical step in network construction remains the definition of gene co-expression. In the present work, we compared how Pearson Correlation Coefficient (PCC), Spearman Correlation Coefficient (SCC), their respective ranked values (Highest Reciprocal Rank (HRR)), Mutual Information (MI) and Partial Correlations (PC) performed on global networks and PLCs. This evaluation was conducted on the model plant Arabidopsis thaliana using microarray a

SUBMITTER: Liesecke F 

PROVIDER: S-EPMC6052111 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature