Note: Plots displayed depend on analysis options selected
The Pearson correlation coefficient of gene expression is calculated between each set of samples to create a correlation matrix across all samples. Red indicates pairs of samples with highly correlated gene expression profiles, grey indicates correlations close to zero, and blue denotes highly negative correlations.
Heatmap of all normalized gene expression data, scaled to give all genes equal variance. Red indicates low expression; yellow indicates high expression. Genes are arranged by pathway (and duplicated when necessary) in the following order: Notch, Wnt, HH, ChromMod, TXmisReg, DNARepair, TGFB, MAPK, STAT, PI3K, RAS, Apop, CC.
If normalization is performed, each gene's variance in the log-scaled, normalized data is plotted against its mean value across all samples. Highly variable genes are indicated by gene name. Housekeeping genes are color coded according to their use (or disuse) in normalization.
For each covariate included in the analysis, a histogram of p-values testing each gene's univariate association with the chosen covariates is displayed. Covariates with largely flat histograms tend to have little association with gene expression; covariates with histograms with significantly more mass on the left are either associated with the expression of many genes or are confounded with a covariate that is associated with the expression. Low P values indicate strong evidence for an association.
Pairwise comparisons of all covariates in the analysis. The type of plot is dependent on the types of variables compared; A categorical vs. categorical covariate plot is shown as a bar chart of counts (Y axis). Continuous vs. categorical covariates generate a boxplot with whiskers denoting 1.5 IQR. Continuous vs. continuous covariates are compared via a scatter plot. Variables that are correlated with a biological variable of interest are potential confounders that may influence downstream analyses.
Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.
Heatmap of the pathway's normalized gene-scaled data, scaled to give all genes equal variance. Yellow indicates high expression, red low expression.