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To identify bona fide responses, we used cell lines from 15 individuals and a rigorous statistical method, Significance Analysis of Microarrays (SAM).
Given computational restrictions required by genome-wide permutation, we could only perform 1000 permutations and therefore based on the first method, significance can be reported only up to a threshold P>10 3.
We describe a method, Significance Analysis of Microarrays (SAM), that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements.
Differences between tests were checked by χ method, significance being set at P<0.05.
The first method, Significance Analysis of Microarrays (SAM) [ 15], identified genes whose expression consistently differed between infected and mock-infected mice over the entire time course.
With the Hochberg method, significance could be declared for both of the primary haemodynamic variables if both had two-sided P ≤ 0.050.
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Only codon 7 was found to be positively selected under the three methods (significance threshold of p = 0.1 for SLAC and FEL algorithms; Bayes factor = 50 for REL).
Section 'Methods: Significance level correction' presents the different methods of correction of the Type-I error.
Differential expression was assessed using two methods: Significance Analysis of Microarrays (SAM) [ 48] and LIMMA [ 46].
Based on statistical methods, significance levels were calculated that describe the overrepresentation of functional categories by a list of genes – the differentially regulated genes in our case.
To calculate differences in the transcript levels, the platform supplies two methods: Significance Analysis of Microarray (SAM) and T-test statistical analysis.
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