The estimation of statistical significance revealed a significant overlap between the two studies (One-tailed Chi-square test p-value = 1.17×10−2), providing further evidence that many of the significant genes that are common to both studies may be implicated in breast cancer metastasis – as they were detected by independent groups, using slightly different experimental approaches.
We used an exact binomial test to examine the significance of overlap between LRES or LREA regions and earlier or later domains.
However, the significance of overlap between studies was highly dependent on traditional experimental variables (i.e. dose, time).
The significance of overlap between genes identified in our analysis and in a paper by D'Hertog et al. [17] was calculated using hypergeometric statistics.
We performed a chi-square test to determine the significance of overlap between down-regulated genes in demethylated hBECs and in lung cancer.
For each scenario, we determined the correlation coefficient and used paired students t-tests to determine the significance of overlap between the two death indices.
For each 500 bp window (shifted by 100 bp) spanning these sequences, we calculated the significance of overlap between its corresponding WPH set and the NRSF-bound sequences.
We calculated the significance of overlap between these HSN sets and two CRM test sets, the REDfly CRMs (Supplementary File S6) and the well-studied stripe subset of REDfly (Supplementary File S1).
Significance of overlap between the modules and pathways from Panther, Kegg and MSigDB [89], [90], [91] was calculated with an hypergeometric test, P-values were corrected for multiple comparisons by calculating the false discovery rate using the Benjamini and Hochberg procedure [92].
Hypergeometric testing was used to establish the significance of overlap between two gene lists.
The significance of overlap between pathways is inferred using hypergeometric test as following.
