Statistical Depth based Normalization and Outlier Detection of Gene Expression Data

TL;DR

This paper introduces a statistical depth-based normalization method for gene expression data that preserves properties of the median and proposes analytical outlier detection techniques for genes and samples, addressing high-dimensional challenges.

Contribution

It presents a novel normalization procedure using statistical data depth and analytical outlier detection methods tailored for high-dimensional gene expression data.

Findings

Effective normalization preserving median properties

Analytical outlier detection for genes and samples

Application to four gene expression datasets

Abstract

Normalization and outlier detection belong to the preprocessing of gene expression data. We propose a natural normalization procedure based on statistical data depth which normalizes to the distribution of gene expressions of the most representative gene expression of the group. This differ from the standard method of quantile normalization, based on the coordinate-wise median array that lacks of the well-known properties of the one-dimensional median. The statistical data depth maintains those good properties. Gene expression data are known for containing outliers. Although detecting outlier genes in a given gene expression dataset has been broadly studied, these methodologies do not apply for detecting outlier samples, given the difficulties posed by the high dimensionality but low sample size structure of the data. The standard procedures used for detecting outlier samples are visual and based on dimension reduction techniques; instances are multidimensional scaling and spectral map plots. For detecting outlier genes in a given gene expression dataset, we propose an analytical procedure and based on the Tukey's concept of outlier and the notion of statistical depth, as previous methodologies lead to unassertive and wrongful outliers. We reveal the outliers of four datasets; as a necessary step for further research.