A new statistical approach for the identification of outlier genes in cancer microarray data


  • Md Bipul Hossen Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur, Rangpur-5404, Bangladesh




microarray data; monte carlo simulation; package data; outlier; t-test


The aim of microarrays technology is to discover genes, which are differentially expressed as outliers between two or more groups of patients are an important task in the genomics community. The regular pattern of genes may often breakdown due to the presence of outliers and it is essential to detect those genes whose behavior looks abnormal in experimental and biological conditions. Several statistical techniques - t-statistic, cancer outlier profile analysis (COPA), outlier sums (OS), outlier robust t-statistic (ORT), maximum ordered subset t-statistics (MOST) and least sum of ordered subset square t-statistics (LSOSS) were developed to address the problem of detecting outlier genes in microarray data but these methods are affected by some problems especially if there is an unusual observation in such dataset then the standard assumptions of distribution parameter may be violated and these techniques might not be suitable to detect outliers genes as well. For these consequences, I have developed a new statistical technique that is “Propose t-statistic (PT)”. The performance of the newly proposed method PT statistic compare with the other existing methods applied to the monte carlo simulation data, package data, and real cancer datasets. The result shows that the outlier genes are identified by using the proposed method PT as well and will give the best and identical results than other methods. The performance of the proposed approach significantly improves than the traditional methods and it can extensively contribute to the medical as well as the genomic community.

Asian J. Med. Biol. Res. December 2020, 6(4): 795-801


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How to Cite

Hossen, M. B. (2021). A new statistical approach for the identification of outlier genes in cancer microarray data. Asian Journal of Medical and Biological Research, 6(4), 795–801. https://doi.org/10.3329/ajmbr.v6i4.51248