Correlation between Gene Expression and GO Semantic Similarity

Abstract

This research analyzes some aspects of the relationship between gene expression, gene function, and gene annotation. Many recent studies are implicitly based on the assumption that gene products that are biologically and functionally related would maintain this similarity both in their expression profiles as well as in their Gene Ontology (GO) annotation. We analyze how accurate this assumption proves to be using real publicly available data. We also aim to validate a measure of semantic similarity for GO annotation. We use the Pearson correlation coefficient and its absolute value as a measure of similarity between expression profiles of gene products. We explore a number of semantic similarity measures (Resnik, Jiang, and Lin) and compute the similarity between gene products annotated using the GO. Finally, we compute correlation coefficients to compare gene expression similarity against GO semantic similarity. Our results suggest that the Resnik similarity measure outperforms the others and seems better suited for use in Gene Ontology. We also deduce that there seems to be correlation between semantic similarity in the GO annotation and gene expression for the three GO ontologies. We show that this correlation is negligible up to a certain semantic similarity value; then, for higher similarity values, the relationship trend becomes almost linear. These results can be used to augment the knowledge provided by clustering algorithms and in the development of bioinformatic tools for finding and characterizing gene products.

Towards integrative gene functional similarity measurement

“…Recent studies used the correlation coefficient of gene expression correlations or gene sequence similarities to evaluate the MF based gene similarities [22]. However, it is not always correlated between gene functional similarities and gene expression correlation or sequence similarities [12].…”

“…Among the earlier developed methods, an IC based measure called the Resnik measure has showed strong correlations between its results and gene expression similarities on yeast [16,22]. Mathematically, given a GO term t , its IC is defined as a negative log likelihood IC ( t ) = − log( |G t |/|G root | ), where G t and G root are the sets of genes annotated to term t and the root term (including all of its descendants) respectively.…”

“…In the Resnik measure, the similarity between term t 1 and t 2 is defined as the IC of LCA: TermSim Resnik ( t 1 , t 2 ) = IC ( LCA 12 ). Although the Resnik measure strongly correlated with the gene expression data [22], terms sharing the same LCA have the same semantic similarity, even if they are at very different levels of GO. Consequently, it cannot differentiate term pairs that are far from LCA with term pairs close to the same LCA.…”

A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

“…They ignore the levels of edges in the ontology by considering all edges equal. These measures also have the "shallow annotation" drawback [6][7][8]: two terms with a certain distance near the root have equal semantic similarity with two terms with the same distance but far from the root. Other edge-based measures [2,9] have attempted to overcome this limitation by assigning different weights to the edges at different graph levels using network density, but they still ignored one fact: GO terms at the same level do not always share same specificity because two terms in the same level can have different gene properties.…”

“…Node-based measures like Resnik suffer from "shallow annotation" problem [6][7][8] if they ignore the term levels in an ontology graph. With respect to IC definition, MICA [10] is the least common ancestor (LCA) of two given terms.…”