Identification of Culprit Genes for Different Diseases by Analyzing Microarray Data.
| Autor: | Banerjee AK; Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, West Bengal, Haringhata, West Bengal, India., Ghosh S; Amity Institute of Biotechnology, Amity University Kolkata, Kolkata, West Bengal, India., Mal C; Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, West Bengal, Haringhata, West Bengal, India. |
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| Jazyk: | angličtina |
| Zdroj: | Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2024; Vol. 2719, pp. 167-180. |
| DOI: | 10.1007/978-1-0716-3461-5_10 |
| Abstrakt: | The identification of disease-causing genes is the first and most important step toward understanding the biological mechanisms underlying a disease. Microarray analysis is one such powerful method that is widely used to identify genes that are expressed differently in two or more conditions (disease vs. normal). Because of its large library of statistical R packages and user-friendly interface, the R programming language provides a platform for microarray analysis. In this chapter, we will go over how to identify disease-causing culprit genes from the raw microarray data, using various packages of R programming. The pipeline overviews the steps in microarray analysis, such as data pre-processing, normalization, and statistical analysis using visualization techniques such as heatmaps, box plots, and so on. To better understand the function of the altered genes, gene ontology and pathway analysis are performed. (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.) |
| Databáze: | MEDLINE |
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