The biological knowledge discovery by PCCF measure and PCA-F projection.

Abstract

In the process of biological knowledge discovery, PCA is commonly used to complement the clustering analysis, but PCA typically gives the poor visualizations for most gene expression data sets. Here, we propose a PCCF measure, and use PCA-F to display clusters of PCCF, where PCCF and PCA-F are modeled from the modified cumulative probabilities of genes. From the analysis of simulated and experimental data sets, we demonstrate that PCCF is more appropriate and reliable for analyzing gene expression data compared to other commonly used distances or similarity measures, and PCA-F is a good visualization technique for identifying clusters of PCCF, where we aim at such data sets that the expression values of genes are collected at different time points.