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Figure 1 | BMC Bioinformatics

Figure 1

From: Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis

Figure 1

Schematic representation of GSECA algorithm. A. The individual steps of functional clustering are demonstrated. For each functional gene set prepared from public gene database (left), all pairs of gene members are calculated for Pearson correlation coefficient (PCC). The distribution of individual PCC is shown as histogram indicating how closely gene members are correlated with each other (middle). The mean of PCC values is calculated as expression coherence (EC) and the significance level is determined using gene permutation tests. Functional gene sets with significantly high expression coherences are then selected and grouped into respective functional clusters with similar expression patterns (right). B. Mean expression values of all genes belonging to the functional cluster are calculated as seed values of the corresponding cluster (left). The entire genes in the array are calculated for their similarity or Pearson correlation coefficient (PCC) with the seed values and ordered according to the similarity. The ordered gene list is then matched with regulatory motif gene sets and the extent of enrichment (enrichment score or ES) is determined by GSEA method (right).

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