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Here we compared three types of t-tests (Crawford-Howell, one-sample and Weisberg t-tests) for their ability to identify known regions of differential methylation by single sample analysis while predicting less variability using a randomly selected control group size of 50 which was batch-matched, that is derived from the same batch of 450 k analyses as the patient DNA.
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In addition, some of these new techniques are time-consuming and characterised by expensive instruments, high analysis costs, the need for specialised technical personnel and are, in some cases, limited by single-sample analysis.
Since the approach introduced by Segal et al. does not explicitly assign a single p-value to an input gene set, we reformulated it and called it SSA (Single Sample Analysis).
As a representative of single sample-targeting methods, we employed this reformulated method termed SSA (Single Sample Analysis) for the benchmark test.
Detection at the 2-Mb level identified 7 of the 10 mosaic events that had been detected in single-sample analysis by MAD.
Tests of parameter differences between males and females were carried out using a single-sample analysis, stratified by gender.
We pooled pellets from individual visits into a single sample for analysis.
Subtypes were annotated by applying single sample gene set enrichment analysis (ss-GSEA) (Verhaak et al, 2010) with literature-curated gene signatures for epithelial, mesenchymal and stem cells (Supporting Information Text), and confirmed this characterization with the use of appropriate markers.
A central African S-form cluster was detected by structure analysis but appears to be mostly represented by a single sample (Libreville).
We restricted our analysis to the 10 cultivated V. vinifera samples, as each of the wild Vitis species was represented by a single sample and there may be significant differences in LD decay between species.
Most accessions were represented by a single sample.
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