Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis

One of the computational challenges in plant systems biology is to accurately infer the transcriptional regulation relationships based on the correlation analyses of gene expression patterns. Despite the several correlation methods that have been applied in biology to analyze microarray data, concerns regarding the compatibility of these methods to the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution property of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and if necessary, introduction of novel methods into biology have been expected.

In this study, researchers at the University of Arizona compared four existing correlation methods used in microarray analysis and one novel method called Gini correlation coefficient, on previously published microarray-based and sequencing-based gene expression data in Arabidopsis and maize. The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. The analyses pinpointed the strengths and weaknesses of each method, and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation and the Tukey’s biweight correlation.

The Gini correlation method, along with the other four evaluated methods in this study, was implemented as an R package named “rsgcc” that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses.

The rsgcc package is available at:

  • Ma C, Wang X. (2012) Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis. Plant Physiol [Epub ahead of print]. [abstract]