Differential Gene Expression Analysis of Non-Small Cell Lung Cancer Samples to Classify Candidate Genes
Received: 9 February 2023 | Revised: 25 February 2023 | Accepted: 4 March 2023 | Online: 1 April 2023
Corresponding author: Neelambika B. Hiremath
Abstract
Differential gene expression is an analysis of gene data, in which the RNA sequence data after next-generation sequencing are to be visualized for any quantitative changes in the levels of the experimental data set. This work aims to derive the transcript statistics on a gene transcript file with a fold change of genes on a normalized scale, in order to identify quantitative changes in gene expression of the difference between the reference genome and Non-Small Cell Lung Cancer (NSCLC) samples. This insight makes a clinical impact in assessing and characterizing candidate genes. The pipeline comprises tuxedo protocol and programming language R with the standard ballgown package. The resultant data set and the plot displays depict the candidate genes in their respective location which are significant in expressing their changes in NSCLC samples. The samples are compared with prominent gene labels of NSCLC samples. The results explain the differential expression of particular samples across samples from both genders.
Keywords:
differential gene expression, RNA sequence, machine learning, non-small cell lung cancer, classification, next-generation sequencingDownloads
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