Ranking function based on Higher Order Statistics (RF-HOS) for two-sample microarray experiments
Abstract
This paper proposes a novel ranking function, called RFHOS by incorporating higher order cumulants into the ranking function for finding differentially expressed genes. Traditional ranking functions assume a data distribution (e.g., Normal) and use only first two cumulants for statistical significance analysis. Ranking functions based on second order statistics are often inadequate in ranking small sampled data (e.g., Microarray data). Also, relatively small number of samples in the data makes it hard to estimate the parameters accurately causing inaccuracies in ranking of the genes. The proposed ranking function is based on higher order statistics (RFHOS) that account for both the amplitude and the phase information by incorporating the HOS. The incorporation of HOS deviates from implicit symmetry assumed for Gaussian distribution. In this paper the performance of the RFHOS is compared against other well known ranking functions designed for ranking the genes in two sample microarray experiments. © Springer-Verlag Berlin Heidelberg 2007.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Shaik, J., & Yeasin, M. (2007). Ranking function based on Higher Order Statistics (RF-HOS) for two-sample microarray experiments. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 97-108. https://doi.org/10.1007/978-3-540-72031-7_9
