Abstract
AbstractHTself is a web-based bioinformatics tool designed to deal with the classification of differential gene expression for low replication microarray studies. It is based on a statistical test that uses self-self experiments to derive intensity-dependent cutoffs. The method was previously described in Vêncio et al, (DNA Res. 12: 211- e 214, 2005). In this work we consider an extension of HTself by calculating p-values instead of using a fixed credibility level α. As before, the statistic used to compute single spots p-values is obtained from the gaussian Kernel Density Estimator method applied to self-self data. Different spots corresponding to the same biological gene (replicas) give rise to a set of independent p-values which can be combined by well known statistical methods. The combined p-value can be used to decide whether a gene can be considered differentially expressed or not. HTself2 is a new version of HTself that uses the idea of p-values combination. It was implemented as a user-friendly desktop application to help laboratories without a bioinformatics infrastructure.
Highlights
The study of gene differential expression in microarray studies plays a central role in bioinformatics today [1, 2]
Feasible in a reasonable amount of time, it is not entirely necessary to obtain all the values if we use the following heuristic: using the standard HTself method with a low credibility level, we may consider that the p-values of those spots that fall under the precomputed cutoffs are uniform from (1 − α)/2 to one
The “configuration menu” lets you setup all parameters to be used in the analysis of your data as well as to select only those spots that should be taken into account
Summary
The study of gene differential expression in microarray studies plays a central role in bioinformatics today [1, 2]. One of such methods is HTself [12], which was designed to deal with analysis of differentially expressed genes in low-replication contexts This means that the ideal setup where one has as many biological and technical replicates as possible can not be fulfilled, either due to financial restrictions or due to shortage of RNA available. Intensity-dependent cutoffs obtained from self-self data are important because they serve as a test for classifying genes of non-self-self experiments by assuming that the same random process that generated the experimental noise in the first is acting on the last. This is the essence of what will be presented here.
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