Abstract
BackgroundThe use of DNA microarrays and oligonucleotide chips of high density in modern biomedical research provides complex, high dimensional data which have been proven to convey crucial information about gene expression levels and to play an important role in disease diagnosis. Therefore, there is a need for developing new, robust statistical techniques to analyze these data.ResultsdepthTools is an R package for a robust statistical analysis of gene expression data, based on an efficient implementation of a feasible notion of depth, the Modified Band Depth. This software includes several visualization and inference tools successfully applied to high dimensional gene expression data. A user-friendly interface is also provided via an R-commander plugin.ConclusionWe illustrate the utility of the depthTools package, that could be used, for instance, to achieve a better understanding of genome-level variation between tumors and to facilitate the development of personalized treatments.
Highlights
The use of DNA microarrays and oligonucleotide chips of high density in modern biomedical research provides complex, high dimensional data which have been proven to convey crucial information about gene expression levels and to play an important role in disease diagnosis
The DNA microarrays and the oligonucleotide chips of high density are broadly used in modern biomedical research and in the study of numerous diseases like cancer or diabetes; in the last few years, the validity of this technology has become common in differentiation and development studies and in prenatal diagnostic testing for syndromes that involve small changes on chromosomes which are not seen through a microscope [1,2,3,4,5,6]
We have developed depthTools, an R package that implements different robust statistical tools for the visualization and analysis of high dimensional gene expression data as illustrated with the prostate dataset
Summary
The use of DNA microarrays and oligonucleotide chips of high density in modern biomedical research provides complex, high dimensional data which have been proven to convey crucial information about gene expression levels and to play an important role in disease diagnosis. A few recent studies have shown the application of Microarray gene expression data are complex and high dimensional (with usually small sample size) and suggest numerous statistical problems; to fully take advantage of the information conveyed by this technology and its impact in the understanding of living processes sound analyses of these data are needed In this direction, a cohort of techniques, like for instance classifier algorithms, have been developed for gene expression data. The Modified Band Depth (MBD) proposed by [14] is computationally feasible for very high dimensions, what makes it specially appropriate for analyzing gene expression data With this depth notion, it is possible, for instance, to define the most representative (or deepest) sample within a collection of observations which measure the expression (level) of a large set of genes in a group of individuals affected by a particular tumor type. The basic idea in these methods is to classify a new sample to the group having the representative (deepest sample) that is the most similar to the new observation
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