Statistics clarification August 28, 2011, 02:45:46 PM Hi, I just started using XCMS online. The package has identified numerous features that are up or down regulated in my different lc-ms data sets. However, I am not sure which statistical output is most important for prioritizing identified features. How would you compare p-value vs. q value, vs. t-statistics vs. feature score? Separately, does does the statistics take into account the total intensity of the peaks to come up with the p-score?Thank you in advance, Yevgeniy Quote Selected
Re: Statistics clarification Reply #1 – August 30, 2011, 11:57:16 AM The most important outputs for prioritizing features are p-value and fold change.Typically, you would order by p-value, and filter the feature table based on thresholds like p-value <= 0.001, fold change >= 3.The p-value is calculated from the t-statistics (Welch t-test, unequal variances).The q-value tries to give you an estimate of the false discovery rate and is calculated based on the distribution of the p-values.The feature score takes all statistics and the intensity distribution of the feature into account. However, it is still experimental. Quote Selected