Provided to `msVolcano` which can then perform the data transformation, normalization and imputation, enrichment tests, motif identification and visualization as volcano plots. ItĪlso consists of meta-information about the quantity and quality of the recovered protein selected by the user with calculated statistics. MaxQuant analyses the raw MS data and outputs a user modulated tabulated file, which contains profiles of LFQ intensities per replicate per protein identified. LFQ intensity profiles readily quantify as a measure for the recovered absolute protein abundance (Cox et al., 2014). While marked deviations from linearity provide evidence against this hypothesis. If the points on a normal Q-Q plot are reasonably well approximated by a straight line, the popular Gaussian data hypothesis is plausible, Ī Quantile-Quantile (QQ) plot is generlly used to inspect is the data sequence is normally distributed graphically. Overlayed with the distribution of imputed values (in red) per LFQ column selected.īottom row displayes 2x2 scatter plots between the chosen LFQ columns with local regression(lowess) displayed as a red line along with pearson's correlations coefficient.įor the visual aestheticity, the number of scatter plots are restricted to the number of histograms displayed above them. Top row displays the histogram visualizing the distribution of the raw values (LFQ intensites - in blue) The distribution of the missing values and the behavior of the population of the imputed values.Ī series of histogram and scatter plots are displayed for each LFQ column clicked by the user (minimum two). These plots are helpful in assessing the correlation between the replicates, examining
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