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Specifically, it tests whether the fraction of over- or under-expressed genes in each profiled sample includes a higher than randomly expected fraction of genes from one or more gene sets under analysis.
To this end, we took advantage of a recently developed computational algorithm, named GENOMICA [19], which makes it possible to quantify and statistically evaluate the enrichment of one or more gene sets in all samples of a given microarray dataset compendium (Figure 3a).
As more gene sets and datasets accumulate, our method always finds out a better gene set than before.
The best performing run of 100 detected almost all module phenotypes (specificity 100% and sensitivity 98%) and more gene sets (specificity 99.7% and sensitivity 72.5%) (Supplementary Table S9).
This decreasing trend is likely due to the fact that in general, clustering tasks become more difficult as more gene sets are mixed.
This procedure gave a corrected p value for enrichment of gene hits in case CNVs for each gene set as well as a test of whether more gene sets than expected are significantly enriched.
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We define two more gene set statistics that are designed to be sensitive to gene sets in which about half of the genes are DE.
Although more gene-sets were found enriched, the enrichment map clusters were globally the same, but characterized by noisier patterns (Text S1).
First, detection of one or more perturbed gene sets should hone in on one or more biological pathways affected by the drug in question.
This might be because planarians face more complex circumstances than schistosomes regarding enemies or climate throughout their lifetime, or because schistosomes construct complicated components using more limited gene sets.
We then compared the performance by testing which method retrieves more positive gene sets for a given significance level, i.e., p-value cutoff.
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