Multiple Testing Technique And Its Application In The Analysis Of Microarray Data

Multiple testing is an important method of data statistics analysis. It has a lot of applications in bioinformatics, genomics and other aspects. Two kinds of problem are considered in this paper. One is the control of false discovery rate control and the other is the ratio of true null hypothesis. Finally they are used for the screening of differently expressed genes in microarray data.This paper first introduces the theoretical basis of multiple testing, and points out that the most important is to control the type I error in this area. This can be solved by controlling FWER and FDR. The old approach to multiplicity problem calls for controlling the family wise error rate(FWER), but it is thought to be too strict. The FDR proposed by Benjanimi&Hochberg(1995) ease strict rule of FWER, and it has more advantages in distinguishing significant difference between two samples. Four classes of algorithms for controlling FDR are listed, while the Bonferroni procedure are set as the reference of others. In the control of false discovery rate, simulated data were used. In the algorithm, we need to optimize the original p value, and compare the efficiency of each method under the new p value set. Simulation results show that the q-value method can maintain the highest power while controlling FDR.How to correctly and effectively estimate the ratio of true null hypothesis m0 is another emphases of our work. Several estimation methods are reviewed and an improved average method is proposed based on Jiang&Doerge(2008). The cubic spline method is used to estimate the interval instead of bootstrap. Meanwhile the slope method is Li Wei(2014) is also compared. In the simulation, we found that the improved mean value method can estimate m0. We apply them in the data of breast cancer in Hendenfalk(2001) and the data of B cell in Feng Pan,Tie-Lin Yang.etl(2009). Methods above are used to screen greens in microarray data. Compared with the methods in Hochberg&Benjamini(2000), Storey&Tibshirani(2002)and the convest decreasing density estimate in Langaas,M.et al.(2005), the improved average method is able to find more genes, or to find the total gene fewer in number when finding the same effective difference genes. Our improved average method is comparable to the method in Li Wei considering the efficacy of the algorithm. Number of distinct gene is the same. This proved the validity of the new average method in the estimation of true null hypothesis.