The corrected p-value according to the Benjamini-Hochberg procedure is defined by: It is widely used in differential expression studies. It is therefore adapted to situations where we are looking for a large number of genes which are likely affected by the explanatory variables. The Benjamini-Hochberg correction is poorly conservative ( = not very severe). It is part of the FDR (False Discovery Rate) correction procedure family. XLSTAT proposes three common p-value correction methods:īenjamini-Hochberg: this procedure makes sure that p-values increase both with their number and the proportion of non-significant p-values. Consequently, p-values should be corrected ( = increased = penalized) as their number grow. When working with high-throughput data, we often test the effect of an explanatory variable on the expression of thousands of genes, thus generating thousands of p-values. Considering a significance level alpha of 5%, we would likely find 5 significant p-values by chance over 100 computed p-values. Running a test several times increases the number of computed p-values, and subsequently the risk of detecting significant effects which are not significant in reality. The p-value represents the risk that we take to be wrong when stating that an effect is statistically significant. The statistical tests proposed in the differential expression tool in XLSTAT are traditional parametric or non-parametric tests: Student t-test, ANOVA, Mann-Whitney, Kruskal-Wallis). Those tools must therefore be slightly adapted in order to overcome these problems. However, the size of the data may cause problems in terms of computation time as well as readability and statistical reliability of results. In order to test if features are differentially expressed, we often use traditional statistical tests. At this stage, we may talk about omics data analyses, in reference to analyses performed over the genome (gen omics) or the transcriptome (transcript omics) or the proteome (prote omics) or the metabolome (metabol omics), etc. In this kind of studies, data often have a very important size ( = high-throughput data). These analyses are included in the XLStat-Life Sciences and XLStat-Premium packages.ĭETAILED DESCRIPTIONS Differential expression What is differential expression?ĭifferential expression allows identifying features (genes, proteins, metabolites…) that are significantly affected by explanatory variables. For example, we might be interested in identifying proteins that are differentially expressed between healthy and diseased individuals.
FeaturesĪ trial version of XLSTAT-OMICs is included in the main XLSTAT download. You can do differential expression or regulation, comparison of groups and heat maps within Excel, while using the most advanced technology and fast algorithms (XLSTAT uses parallel programming). As any XLSTAT module, it is integrated in Microsoft Excel and allows you to obtain actionable results with just two or three clicks. Analyzing such data has never been as easy as with XLSTAT-OMICs. XLSTAT-OMICs is a user friendly module that allows to analyse high-throughput OMICs data (genomics, transcriptomics, proteomics, metabolomics).