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This involved selecting only genes detected as enriched in body parts from Tiassalé versus N'Gousso via both the limma and GaGa methods with an adjusted p value ≤0.001 and setting a cut off of 1.4-fold differential expression (see Methods section).
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We retrieved similar protein sequences, setting a cut-off of the expect value at <10−10.
Genes with abundant 22G reads in N2 wild-type worms were further selected by setting a cut-off of at least five reads in at least one of the N2 wild-type samples and duplicate or overlapping annotations were removed.
For both waves 1 and 5, mean GHQ scores were approximately 2, setting a cut-off of 3 or more as a case ('1') compared to not being considered a case ('0'0.
For cognitive performances, equivalent scores for each subtest of BACS were calculated, based on normative data from an Italian sample and the mean used as a measure of general cognitive ability, setting a cut-off of 1. RSWGcr, PANSS-PNScr and PANSS-TScr were compared on their ability to identify patients with better outcomes, using sensitivity, specificity and predictive value analysis.
We set a cut-off of p = 0.01 to determine whether a probe was "significantly detected".
They used this estimate to set a cut-off of ≤5 SNPs for cases less than three years apart.
To reduce the number of mRNA changes to be further interrogated to a manageable number, we arbitrarily set a cut-off of 5-fold change.
We therefore set a cut-off of 40 years and focused on the older schizophrenic patients with our further PGAS analysis.
Thus, for each gene we calculated its correlation with SPPR and after correction for multiple testing, set a cut-off of absolute correlation 0.8, which corresponds to a false discovery rate of 3.3%.
Prior to calculating the FDR, we calculated the probability of each peptide match from both real and reversed database based on the product of XCorr and DeltaCn and set a cut-off of P ≤ 0.05 for individual peptide identifications.
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