EnsMOD: A Software Program for Omics Sample Outlier Detection

Abstract

Detection of omics sample outliers is important for preventing erroneous biological conclusions, developing robust experimental protocols, and discovering rare biological states. Two recent publications describe robust algorithms for detecting transcriptomic sample outliers, but neither algorithm had been incorporated into a software tool for scientists. Here we describe Ensemble Methods for Outlier Detection (EnsMOD) which incorporates both algorithms. EnsMOD calculates how closely the quantitation variation follows a normal distribution, plots the density curves of each sample to visualize anomalies, performs hierarchical cluster analyses to calculate how closely the samples cluster with each other, and performs robust principal component analyses to statistically test if any sample is an outlier. The probabilistic threshold parameters can be easily adjusted to tighten or loosen the outlier detection stringency. EnsMOD can be used to analyze any omics dataset with normally distributed variance. Here it was used to analyze a simulated proteomics dataset, a multiomic (proteome and transcriptome) dataset, a single-cell proteomics dataset, and a phosphoproteomics dataset. EnsMOD successfully identified all of the simulated outliers, and subsequent removal of a detected outlier improved data quality for downstream statistical analyses.

Keywords: hierarchical cluster analysis; multivariate; omics; outlier detection; proteomics; robust principal component analysis.

FIG. 1.

Outlier detection from analyzing the anthrax phosphoproteomics dataset. Mice were not injected at all, or were injected with vehicle alone, or with either the Sterne or the ΔSterne strain of Bacillus anthracis (A). Fifteen mice were prepared for the Sterne 72 hours experimental condition, but only one reached 72 hours. Spleens were analyzed using LC-MS phosphoproteomics (Manes et al., 2011). Empirical histogram and density curve (black) plotted against the standard normal distribution (red) (B). The R² value was calculated between the empirical density curve and the standard normal distribution. Density curve of the phosphopeptide abundance values for each sample (C). Dendrogram of the samples from the HCA with the largest CCC (D). Silhouette coefficients for each sample (black line is the cutoff, red dashed line is the mean calculated across all of the samples) (E). Distance–Distance plots from the robpca and PcaGrid analyses (F, G). Venn diagram of the differentially abundant phosphopeptides discovered using one-way ANOVAs (q-value ≤0.05), with (left circle) and without (right circle) including the genuine sample outlier (SplnC0_0003) (H). CCC, cophenetic correlation coefficient; HCA, hierarchical cluster analysis; LC-MS, liquid chromatography–mass spectrometry.

FIG. 1.

Outlier detection from analyzing the anthrax phosphoproteomics dataset. Mice were not injected at all, or were injected with vehicle alone, or with either the Sterne or the ΔSterne strain of Bacillus anthracis (A). Fifteen mice were prepared for the Sterne 72 hours experimental condition, but only one reached 72 hours. Spleens were analyzed using LC-MS phosphoproteomics (Manes et al., 2011). Empirical histogram and density curve (black) plotted against the standard normal distribution (red) (B). The R² value was calculated between the empirical density curve and the standard normal distribution. Density curve of the phosphopeptide abundance values for each sample (C). Dendrogram of the samples from the HCA with the largest CCC (D). Silhouette coefficients for each sample (black line is the cutoff, red dashed line is the mean calculated across all of the samples) (E). Distance–Distance plots from the robpca and PcaGrid analyses (F, G). Venn diagram of the differentially abundant phosphopeptides discovered using one-way ANOVAs (q-value ≤0.05), with (left circle) and without (right circle) including the genuine sample outlier (SplnC0_0003) (H). CCC, cophenetic correlation coefficient; HCA, hierarchical cluster analysis; LC-MS, liquid chromatography–mass spectrometry.