Sentence examples for based on a hypergeometric from inspiring English sources

Functional enrichment of genes associated with CNEs was carried out using the GREAT software58 with each CNE assigned to the genes with the nearest transcription start site and within 1 Mb in the zebrafish genome, and significantly enriched functional categories identified based on a hypergeometric test of genomic regions (false discovery rate (FDR) q value < 0.05).

To facilitate GO and KO analyses of query genes, we developed online tools to identify the enriched or depleted GO/KO terms within a query gene list based on a hypergeometric distribution.

This is highly significant when compared to a random assignment based on a hypergeometric distribution, which would yield, on average, only 24 out of 48 mice correctly classified.

It is based on a hypergeometric distribution.

An enrichment p value based on a hypergeometric test is calculated together with a p value corrected for multiple testing.

In both cases, a P value for each gene was calculated based on a hypergeometric distribution test (5% FDR).

This intersect between genes predicted by both data sets was highly statistically significant (P < 7.422e-66, calculated based on an hypergeometric distribution analysis [ 31]).

Based on a classical hypergeometric test for Gene Ontology term enrichment, the FATIGO tool provides a t-test for cross-comparison enrichment analyses from two gene lists.

Recently, in myasthenia gravis Cao et al. [ 26] calculated the cross-talk between pathways, identified by pathway-enriched analysis based on a cumulative hypergeometric distribution.

Based on a tailed hypergeometric distribution analysis only pathways over-represented in the group of co-expressed genes (p [hyper] < 0.005) were retained.

The crosstalk between pathways was calculated based on a cumulative hypergeometric distribution using the following formula: (1)   P = 1 − ∑ k = 0 x m k N − m n − k N n, where N is the total number of genes in the human genome, n is the number of genes in one pathway, m is the number of genes in a different pathway, and x is the number of genes that are common to both pathways.