DeepAI AI Chat
Log In Sign Up

The covariance shift (C-SHIFT) algorithm for normalizing biological data

03/29/2020
by   Evgenia Chunikhina, et al.
0

Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. In this paper, we introduce a novel normalization technique, called the covariance shift (C-SHIFT) method. This normalization algorithm uses optimization techniques together with the blessing of dimensionality philosophy and energy minimization hypothesis for covariance matrix recovery under additive noise (in biology, known as the bias). Thus, it is perfectly suited for the analysis of logarithmic gene expression data. Numerical experiments on synthetic data demonstrate the method's advantage over the classical normalization techniques. Namely, the comparison is made with rank, quantile, cyclic LOESS (locally estimated scatterplot smoothing), and MAD (median absolute deviation) normalization methods.

READ FULL TEXT
01/13/2022

Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method

Deep sequencing has become one of the most popular tools for transcripto...
10/04/2018

A statistical normalization method and differential expression analysis for RNA-seq data between different species

Background: High-throughput techniques bring novel tools but also statis...
06/26/2018

Estimation of large block covariance matrices: Application to the analysis of gene expression data

Motivated by an application in molecular biology, we propose a novel, ef...
06/28/2022

Statistical Depth based Normalization and Outlier Detection of Gene Expression Data

Normalization and outlier detection belong to the preprocessing of gene ...
01/17/2013

Non-parametric Bayesian modelling of digital gene expression data

Next-generation sequencing technologies provide a revolutionary tool for...
02/23/2015

Rectified Factor Networks

We propose rectified factor networks (RFNs) to efficiently construct ver...