Differential expression analysis
Differential gene expression analyses were performed in R using limma (v.3.48.3), and a paired sample design was implemented in those datasets where applicable. Differentially-expressed genes were identified using p-values with Benjamini-Hochberg correction for a false discovery rate (FDR) at a significance level of 0.05.
Inputs
Results table
Gene expression meta-analysis
Meta-analysis has been performed following Dersimonian-Laird methond with the R package 'metafor'.
Inputs
Results table
Forest plot
Over-representation analysis
This page shows the results of over representation analysis using clusterProfiler. The followin filters have been applied: adjusted p-value lower than 0.05, functions are mapped to at least 11 genes and less than 200 genes. This last filter avoids too specific ontologies and too wide ones.
Gene Ontology: Biological Process
Survival analysis
This page shows the results of survival analysis. Samples are divided in two groups computed gene-wise. The low expression group has expression z-scores under the 25th percentile for a given gene. The high expression group has expression z-scores over the 75th percentile.