Abstract
This study was aimed to examine the validity of commonly used statistical tests for comparison of expression data from simulated and real gene signatures as well as pathway-characterized gene sets. A novel algorithm based on 10 sub-gradations (5 for up- and 5 for down-regulation) of fold-changes has been designed and testified using an Excel add-in software support. Our findings showed the limitations of conventional statistics for comparing the microarray gene expression data. However, the newly introduced Gene Expression Index (GEI) appeared to be more robust and straightforward for two-group comparison of normalized data. The software automation simplifies the task and the results are displayed in a comprehensive format including a color-coded bar showing the intensity of cumulative gene expression.
► Limitations of conventional statistical tests for comparing gene expression data. ► Validation of a novel algorithm for comparing normalized microarray data. ► A software tool using Microsoft Excel add-in has been developed for automation. ► Gene Expression Index appeared to be more robust and straightforward. ► The software automation simplifies the task.