In the first case, gene pairs {1,2} and {3,4} are highly correlated but there is no correlation between genes in different pairs.
Highly correlated control genes are combined into an index, and the pair-wise correlation between genes and the correlation between each gene and the index are also calculated.
While scientists have found a correlation between genes and autism, they have not pinpointed which genes are responsible.
From column 2-5 p-values for correlation between genes in follicular phase and in luteal phase are shown; columns 7-10 show p-values for correlation between cases and controls.
Based upon microarray data, we first evaluate the correlation between genes and the phenotype response.
Another explanation for weaker correlation between genes is the definitions of function provided by GO terms.
We hypothesize that correlation between genes can be used to infer the function when the function of some genes in a correlated set is known.
Analysis of correlation between genes showed that Loess methods decrease high values, although patterns between genes that are known to interact are preserved.
Context based correlation between genes is based on (a) collocation in the same chromosomal neighborhood, (b) fusion events, and (c) co-occurrence of genes across genomes.
Based on simulation studies, they showed that bias due to PS could be large for studies of interactions, especially when strong correlation between genes (or between genetic and environmental factors) takes place.
These data encourage speculation that there may be correlation between genes identified by the microarray studies and the binding sites for specific gene products on the UCH L1 promoter.
