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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Missing value imputation in DNA microarrays based on conjugate gradient method.

Fatemeh Dorri¹, Paeiz Azmi, Faezeh Dorri

¹School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada. fdorri@cs.uwaterloo.ca

Computers in Biology and Medicine

|December 14, 2011

Summary

A new conjugate gradient (CG) imputation algorithm (CGimpute) effectively estimates missing gene expression values. CGimpute outperforms existing methods in accuracy across various datasets and missing data rates.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate gene expression profile analysis requires complete data matrices.
Missing values in gene expression data present a significant challenge for downstream analysis.
Existing imputation methods have limitations in accuracy and efficiency.

Purpose of the Study:

To propose a novel imputation algorithm based on the conjugate gradient (CG) method for estimating missing gene expression values.
To evaluate the performance of the proposed CG-based imputation algorithm (CGimpute).
To compare CGimpute against established imputation techniques.

Main Methods:

Selection of k-nearest neighbors based on Pearson correlation coefficient.
Identification of a subset of best similar genes among neighbors.
Utilizing the conjugate gradient (CG) algorithm with the selected subset to estimate missing values.

Main Results:

CGimpute demonstrated superior performance compared to Sequential Local Least Squares (SLLSimpute), Bayesian Principle Component Analysis (BPCAimpute), Local Least Squares Imputation (LLSimpute), Iterated Local Least Squares Imputation (ILLSimpute), and Adaptive k-Nearest Neighbors Imputation (KNNKimpute).
The proposed method achieved lower average Normalized Root Mean Squares Error (NRMSE) and relative NRMSE across diverse datasets.
CGimpute maintained high accuracy even with varying missing data rates.

Conclusions:

The conjugate gradient-based imputation algorithm (CGimpute) is a highly effective method for handling missing gene expression data.
CGimpute offers improved accuracy and reliability over existing imputation techniques.
This method provides a valuable tool for enhancing the analysis of gene expression profiles.