A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis

Similarity Measures Based on the Overlap of Ranked Genes Are Effective for Comparison and Classification of Microarray Data

Similarity (or conversely distance) measures are at the heart of most bioinformatic applications. When the similarity involves only a small subset of features out of many, global similarity measures may be significantly affected by noise. Selecting only a subset of (putatively relevant) features for comparison is a widespread solution to the problem albeit affected by arbitrariness and manual intervention. The problem is becoming more and more important due to the increasing amount of experimental data available. In recent years measures based on ranking similarities between two datasets have been proposed. Here, we use one of the proposed rank similarity measures, sharing some aspects with the fraction enrichment score used for protein structure prediction and the gene set enrichment analysis, and test its performance in classifying experiments. The discrimination ability of the similarity measures based on the overlap of ranked genes tested here compares well or better with standard measures of similarity. This conclusion supports the use of rank-based proximity measures to gain further insight in dataset comparisons, particularly on expression data obtained by different techonologies (e.g., RNA-seq and microarrays).

ValWorkBench: an open source Java library for cluster validation, with applications to microarray data analysis

The prediction of the number of clusters in a dataset, in particular microarrays, is a fundamental task in biological data analysis, usually performed via validation measures. Unfortunately, it has received very little attention and in fact there is a growing need for software tools/libraries dedicated to it. Here we present ValWorkBench, a software library consisting of eleven well known validation measures, together with novel heuristic approximations for some of them. The main objective of this paper is to provide the interested researcher with the full software documentation of an open source cluster validation platform having the main features of being easily extendible in a homogeneous way and of offering software components that can be readily re-used. Consequently, the focus of the presentation is on the architecture of the library, since it provides an essential map that can be used to access the full software documentation, which is available at the supplementary material website [1]. The mentioned main features of ValWorkBench are also discussed and exemplified, with emphasis on software abstraction design and re-usability. A comparison with existing cluster validation software libraries, mainly in terms of the mentioned features, is also offered. It suggests that ValWorkBench is a much needed contribution to the microarray software development/algorithm engineering community. For completeness, it is important to mention that previous accurate algorithmic experimental analysis of the relative merits of each of the implemented measures [19,23,25], carried out specifically on microarray data, gives useful insights on the effectiveness of ValWorkBench for cluster validation to researchers in the microarray community interested in its use for the mentioned task.