Non-Gaussian Distributions Affect Identification of Expression Patterns, Functional Annotation, and Prospective Classification in Human Cancer Genomes
Figure 3
Single-Gene Expression Distributions are not Gaussian.
These graphs illustrate the wide range of potential skewness (A) and kurtosis (B) that exist in the expression distributions of individual genes comprising the cancer expression datasets. This refutes the assumption that the expression data for individual genes follow an approximately Gaussian distribution around the gene's mean expression level. Data for these graphs was taken from the log2-subtracted, RMA-normalized glioblastoma expression data. For the skewness comparison, five genes with comparable means, standard deviations, and kurtosis were selected from subsets of genes representing approximately the 10th, 25th, 50th, 75th and 90th percentiles for per-gene skewness contained in the dataset. Similarly, for the kurtosis comparison, five genes with comparable means, standard deviations, and skewness were selected from subsets of genes representing approximately the 10th, 25th, 50th, 75th and 90th percentiles for per-gene kurtosis contained in the dataset. The identities of the genes are not germane for comparative purposes.