Open AccessA Complete Summary of Non-Parametric Statistical Methods Used For Biological Microarray Data
Author(s) -
Meenu Sharma,
Rabea Parveen
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8127.118419
Subject(s) - microarray analysis techniques , microarray databases , microarray , gene chip analysis , parametric statistics , data mining , dna microarray , computer science , computational biology , bioinformatics , biology , gene , gene expression , genetics , statistics , mathematics
Microarray technology is developed as a new powerful biotechnology tool, to analyze the expression profile of more than thousands of genes simultaneously. In recent times, Microarray is the most popular research topic. For extracting the differentially expressed genes from microarray data, numerous types of statistical tests are developed. The focus of microarray analysis is to predict genes that show different expression patterns under two different experimental conditions. The aim of this research paper is to explore various types of non-parametric methods proposed to analyze microarray expression data for predicting those genes which are differentially expressed, and a comparative analysis of various methods has been done. Besides, we also predicted the best condition for each method where they perform better and to investigate the disease development mechanism. Many types of statistical tests have been studied for identifying the differentially expressed genes, only very few studies have compared the performance of these methods. In our study, we extensively study and compare the different types of non-parametric methods.