Fig 1.
Differential miRNA expression between ER positive and negative.
A scatter plot of differential expression p-values (-log10, Wilcoxon Rank-sum) for the unnormalized (x) vs normalized (y) joint dataset. Title contains sample size details and dataset distribution.
Fig 2.
Differential miRNA expression between ER positive and negative.
Volcano plot showing the fold change and corresponding Wilcoxon Rank-sum FDR corrected Q value ratio between the normalized and unnormalized datasets. Dashed arrow connects the unnormalized (gray circles) and normalized (red circles) results on a particular miRNA. High absolute values in X axis correspond to substantial difference in median expression between ER negative over ER positive samples (for a particular miRNA). High values in Y axis correspond to miRNAs that present substantial difference *after* normalization but not before. Low values in Y axis correspond to miRNAs that present substantial difference *before* normalization but not after. Vertical dashed lines represent a Fold change threshold of 2x (log2(2) = 1) and horizontal dashed lines represent a Q-value threshold of 0.05 (-log10(0.05)≅1.3).
Fig 3.
Differential miRNA expression between ER positive and negative.
A CDF plot showing many more substantially differentially expressed miRNAs after normalization (red line) than before normalization (blue line), and substantially more than would be expected at random (compared to 20 random permutations of labels, dashed black lines). Also shown are dashed colored lines corresponding to each appropriate single-dataset Q values exemplifying the advantage of a joint-dataset analysis. Note that at Q = 10−18 we can find 10 miRNAs under AQuN but none under other normalization approaches or per dataset analyses.
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.
Fig 5.
Visualizing expression of hsa-miR-18a across datasets and samples and in regard to estrogen receptor (ER) positive (pos) vs. negative (neg) differential expression.
Caption description matches the one provided in Fig 4.
Table 1.
Top differentially expressed miRNA sorted by fold change.
Fig 6.
Functional experiment results.
Breast cancer cell lines were transfected with miRNA mimics (20nM) and assayed for functional effects 72 hours after transfection. A) Cell viability measured in MCF7 breast cancer cells. B) Apoptosis measured by levels of cleaved PARP (cPARP), HER2 and phosphorylated ERK (pERK) protein levels measured in KPL4 cells. The dashed lines indicate cut-off points that were considered significant (see Materials and Methods). Asterisks denote significant effects. Original data from b) are taken from [32].
Table 2.
Resulting MiTEA matchings on normalized miRNA expression.
Fig 7.
Impact of normalization on the correlation between hsa-miR-29b expression and its in-silico predicted targets according to TargetScan.
(A) AQuN normalized vs Unnormalized (B) miRNA showing normalized is more negatively correlated to the prominent hsa-miR-29b targets in TargetScan as evident in stronger enrichment values.
Fig 8.
Impact of normalization on the correlation between hsa-miR-29b expression and its in-silico predicted targets according to TargetScan.
Scatter plot of spearman correlation on normalized miRNA or unnormalized miRNA expression. If the target mRNA appears in TargetScan it is highlighted in orange. The marginal distributions are shown parallel to the axes and corresponding Kolmogorov-Smirnov test p-values display an overall lowered correlation for TargetScan candidates on normalized data.
Fig 9.
GOrilla enrichment analysis comparison of hsa-miR-29b correlation with gene expression before and after miRNA normalization with AQuN.
Showing scatter of GO term Q-values before and after AQuN. Red dots depicted with “≥P98” are above the 98th percentile of Normalized–Unnormalized Q-values (-log10) and green dots are for Unnormalized–Normalized. Right side panel shows a list of GO terms in the ≥P98 group.
Fig 10.
Volcano plot of per-dataset Differential Expression of ER positive vs ER negative from Fig 1.
Here we include a both joint normalized and per dataset results. We observe an overall increase in statistical significance as dark points tend to be higher on the y-axis than their corresponding-colored points (indicated by dashed lines), as would be expected from the increase in statistical power. In some miRNA this can come at the cost of a lower detected fold-change as compared to some individual datasets.
Fig 11.
Statistical power as a function of sample size and expected effect size (measured in Cohen’s d [66]).
Dotted line plots illustrate an a-priori power analysis for one-tailed Wilcoxon Rank Sum (WRS) test for different effect sizes. Overlaid in squares and triangles are effect sizes, d, for the differential expression of hsa-miR-18a-5p and hsa-miR-29b-3p, accordingly, in ER positive vs ER negative samples as estimated empirically over the joint dataset on non-normalized data. Power values are estimated via (linear, 2D) interpolation on different dataset sizes.
Table 3.
Technical details of platforms used for expression measurements for the four different cohorts.
Fig 12.
Overview of the miRNA coverage in the dataset.
Each row represents one miRNA. Each entry represents the intensity (log10) in a specific sample. Dashed vertical lines separate between samples from the four datasets. Dashed horizontal lines separate between groups of miRNAs by their dataset availability. Blank (white) entries correspond to miRNAs that are missing from a dataset.
Fig 13.
Scatterplot matrix of quantile normalized data showing miRNA expression reproducibility across dataset pairs.
Each subplot depicts a pair of datasets. In the upper-diagonal-subplots, each point corresponds to a single miRNA’s median (across samples) rank (intra-sample) in each dataset. Similarly, the bottom-diagonal shows median log2 expressions in place of ranks. A second degree polynomial curve is fitted and prediction intervals at confidence level 0.8 are plotted as dashed lines. Spearman correlation is given for each subplot. Figures at the diagonal show percentile plotted against expression and a circle represents the dataset colorcode as related to other figures in the paper.
Fig 14.
Visualizing batch effects in the combined cross-tech miRNA dataset considering the unnormalized data.
(A) Dendrogram with edges colored by dataset. Note that the tree root is outside the displayed axis range. (B) Silhouette plot [67] showing that most samples cluster according to the dataset they originate from. (C) Pairwise Euclidean distances showing a block structure that agrees with the sample dataset of origin.