miRNA normalization enables joint analysis of several datasets to increase sensitivity and to reveal novel miRNAs differentially expressed in breast cancer
Fig 4
Visualizing expression of hsa-miR-190b across datasets and samples and in regard to estrogen receptor (ER) positive (pos) vs. negative (neg) differential expression.
(A, D) Expression values (log2) of each sample before quantile normalization. Samples are ranked by ER status label, then by dataset and finally by ascending expression value. (A, B)-Unnormalized joint dataset. (C, D)-Normalized joint dataset. (B, C) Actual vs expected (via a uniform null model) rank distribution of ER negative (neg) vs positive (pos). Diagonal straight lines bounding a polygon represent a null uniform distribution of positive and negative samples (when ranked by expression value). The colored surface area represents the magnitude of deviation from a uniform distribution. The boundary of the surface is calculated by the cumulative number of ER negative (x axis) vs ER positive (y axis) samples in the ranked (descending) expression vector. Top-illustrating the rank distribution per-dataset (without normalization). Bottom-comparing the joint-dataset distributions when ranking before or after normalization.