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First, our method generates significance scores based on the input of a particular molecular profile, such as a set of differentially expressed genes.
The magnitude of the effects for observed diversity and its square are much reduced, and none of the regressions involving either variable generates significance (Table S2).
It generates significance scores by combining overrepresentation, positional bias and the proportion of the co-regulated promoters to contain at least one instance of the motif.
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The negligible limitations of PERMANOVA, flexible design options, and ability to generate significance tests for small sample sizes make the approach powerful.
Here we lay out the details of how we generate significance clusters and alluvial diagrams for mapping change in networks.
From these simulated data sets a distribution of shared genes for each pair wise comparison was used to generate significance levels for the observed overlap in functional gene sets.
It is based on the calculation of the R statistic over the rank similarities between samples, whose values can lie between −1 and 1. ANOSIM uses a Mantel permutation procedure combined with a randomization approach to generate significance levels (Monte Carlo tests).
Joint significance of Z and CS basically means that CS is supporting Z, since large uni-directional D values will generate significance for both tests.
P-values for enriched developmental categories were transformed into their –log values and used to generate significance plots, to visualize prioritization in CALR-variant transcriptomes.
The hypergeometric test generates the significance (P-value) and calculates the fold of enrichment for each miRNA set.
The use of the 'last observation carried forward' imputation method is known to inflate results, often generating statistically significance when adverse event withdrawals are high; in clinical practice terms, this is the wrong answer.
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