Sentence examples for measures between genes from inspiring English sources

Similarity measures between genes were computed using a Pearson correlation.

To apply the network-based approaches, we evaluated several similarity measures between genes based on a protein-protein interaction network (STRING, version 9).

Generally, once coexpression measures between genes (e.g. correlation coefficients) have been estimated, subsequent coexpression analysis steps include visualization of the coexpression relationships, identification of densely correlated gene groups, and interpretation of biological relevance.

However, this scheme, which relies heavily on correlation measures between genes, is more suitable for mapping rare diseases than common complex diseases, which often involve multiple common variants (Collins et al., 1997) and complex mechanisms (Gu et al., 2002).

Comparing these measures between gene categories shows that the transcriptional regulator genes as a whole have a much higher density of total SNPs and non-synonymous SNPs than all other categories, as well as a higher density of synonymous SNPs (dS).

In the first step, clusters were defined by superparamagnetic clustering [39], with the absolute value of Spearman rank correlation as a distance measure between genes.

The anti-Pearson correlation between log-intensities was used as a dissimilarity measure between genes.

Average linkage hierarchical clustering was performed in this study using Pearson distance as the measure between genes and samples.

In Steps 1 and 2, the adjacency measure between genes is defined using the power transformation of correlation coefficients.

However as transcript length bias is a relative measure between genes it will not affect the presented results.

A heat map was generated using R software (http://www.R-project.org) using Euclidean distance as a distance measure between genes.