Nonparametric statistical test approaches in genetics data

RAKESH Kumar Saroj, Dr.K.H.H.V.S.S. Narsimha Murthy, Mukesh Kumar


The biggest challenge of genetic research lies in significant and intellectual analysis of the large and complex data sets generated by the cutting edge techniques like massively parallel DNA sequencing and genome wide analysis. Statistical analyses are the most important of such experimental data. When the data are not normally distributed and using non numerical (rank, categorical) data then use the nonparametric test for exact result of research hypothesis. Order statistics are among the most fundamental tools in non-parametric statistics and inference. Non parametric test does not depend upon parameters of the population from which the samples are drawn, no strict assumption about the distribution of the population.
Nonparametric tests are known as distribution free test also because their assumptions are less and weaker than those connected with parametric test. Nonparametric test does not follow probability distribution. To analyze microarrays and genomics data several non-parametric statistical techniques are used like Wilcoxon’s signed rank test (pre-post group),Mann-Whitney U test (two groups) or Kruskal-Wallis test (two or more groups).Importance of this paper is to look at the nonparametric test how to use in genetic research and provide the understanding of these test.


Nonparametric test, Wilcoxon’s signed rank test, Mann-Whitney test, Kruskal-Wallis test

Full Text:



Mount, D.W. (2004). Bioinformatics: Sequence and Genome Analysis. Second edition. Cold Spring Harbor Laboratory Press, New York, USA.

The International HapMap Consortium, 2003 the International HapMap Project. Nature 426: 789-94.

David, H. A.; Nagaraja, H. N. (2003). "Order Statistics". Wiley Series in Probability and Statistics. doi:10.1002/0471722162. ISBN 9780471722168.

Gentle, James E. (2009), Computational Statistics, Springer, p. 63, ISBN 9780387981444

Conover WJ. Practical Nonparametric Statistics, 2nd edition, New York: John Wiley and Sons.

Ikewelugo Cyprian Anaene Oyeka (Apr 2012). "Modified Wilcoxon’s Signed-Rank Test". Open Journal of Statistics: 172–176.

Wilcoxon’s, Frank (Dec 1945). "Individual comparisons by ranking methods" (PDF). Biometrics Bulletin 1 (6): 80–83

Siegel and Castellan. (1988). "Nonparametric Statistics for the Behavioral Sciences," 2nd edition, New York: McGraw-Hill.

Kruskal; Wallis (1952). "Use of ranks in one-criterion variance analysis". Journal of the American Statistical Association 47 (260): 583–621. doi:10.1080/01621459.1952.10483441.

Corder, Gregory W.; Foreman, Dale I. (2009). Nonparametric Statistics for Non-Statisticians. Hoboken: John Wiley & Sons. pp. 99–105. ISBN 9780470454619.

Statistical Analysis of Gene Expression Microarray Data. T. P. Speed (Ed). Chapman & Hall. Collection of essays by microarray authorities.

The Analysis of Gene Expression Data. G. Parmigiani (Ed) et al. Springer. Covers various statistical tools for microarray analysis, including R.

Fisher, R. A. (1922). "On the interpretation of χ2 from contingency tables, and the calculation of P". Journal of the Royal Statistical Society 85 (1): 87–94. Doi: 10.2307/2340521. JSTOR 2340521