Statistical Tests for Identification of Differentially Expressed Genes in Microarray Data
Biomedical Statistics and Informatics
Volume 2, Issue 4, December 2017, Pages: 166-171
Received: Jul. 29, 2017;
Accepted: Aug. 30, 2017;
Published: Oct. 20, 2017
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Harun or Rashid, Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur, Bangladesh
Arefin Mowla, Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur, Bangladesh
Siddikur Rahman, Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur, Bangladesh
Siraj-Ud-Doulah, Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur, Bangladesh
Bipul Hossen, Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur, Bangladesh
Gene expression assay provide a fast and organic way to identity disease markers relevant to clinical trial in modern age. In microarray experiments, differentially expressed genes, or discriminator genes, are the genes with considerably different expression patterns in two user-defined groups. Typically microarray data consists of huge amount of genes, and which genes are responsible or differentiable for a particular disease. Identification of differentially expressed genes across multiple conditions has become a vigorous statistical problem in analyzing large-scale microarray data. In this perspective, we considered a simulated data and real data sets (Head and Neck cancer). This paper uses some statistical methods: t-test, Wilcoxon signed-rank sum test and renewed approach to detect the differential expression of genes between conditions and finding the required number of differentially expressed genes. Additionally Principal Component Analysis (PCA) and largest difference from mean and data methods are used for visualizing outliers and finding numerical outliers respectively. If introducing some artificial outliers to simulated and real data sets and these outliers are not affected or not related to the differentially expressed genes. Results reveal that 25, 126 and 385 differentially expressed genes are identified by using t-test, Wilcoxon Rank sum test and Renewed Approach respectively. Among the three methods 23 common genes those are may be responsible for cancer disease. This paper shows that the two samples mean test (t-test) is perfectly used to identify the differentially expressed genes in microarray data.
Harun or Rashid,
Statistical Tests for Identification of Differentially Expressed Genes in Microarray Data, Biomedical Statistics and Informatics.
Vol. 2, No. 4,
2017, pp. 166-171.
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