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DepthTools: an R package for a robust analysis of gene expression data.

Torrente A, López-Pintado S, Romo J - BMC Bioinformatics (2013)

Bottom Line: Therefore, there is a need for developing new, robust statistical techniques to analyze these data. depthTools 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Functional Genomics Team, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. aurora@ebi.ac.uk

ABSTRACT

Background: 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. Therefore, there is a need for developing new, robust statistical techniques to analyze these data.

Results: depthTools 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.

Conclusion: We 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.

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Related in: MedlinePlus

Trimmed mean plots. (a) Representation in parallel coordinates of the 0.25-trimmed mean of the normal prostate samples, in black. The trimmed out 25% most external points are depicted in gray; the remaining samples, used to compute the trimmed mean, are drawn in blue. (b) Trimmed means for different proportions of trimmed out points, corresponding to the normal (blue-gray) and cancer (red-gray) samples, for a subset of genes.
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Figure 2: Trimmed mean plots. (a) Representation in parallel coordinates of the 0.25-trimmed mean of the normal prostate samples, in black. The trimmed out 25% most external points are depicted in gray; the remaining samples, used to compute the trimmed mean, are drawn in blue. (b) Trimmed means for different proportions of trimmed out points, corresponding to the normal (blue-gray) and cancer (red-gray) samples, for a subset of genes.

Mentions: we obtain a plot like that in Figure2(a), where the 0.25-trimmed mean is visualized as a black line; additionally, the 0.25-trimmed sample, that is, the collection of samples remaining after removing the proportion 0.25 of less deep points are represented as blue lines, whereas the discarded samples appear as gray lines. These three colours can be modified with the parameter cols. Note that the samples removed in the plot are those defining the darkest region in Figure1(b).


DepthTools: an R package for a robust analysis of gene expression data.

Torrente A, López-Pintado S, Romo J - BMC Bioinformatics (2013)

Trimmed mean plots. (a) Representation in parallel coordinates of the 0.25-trimmed mean of the normal prostate samples, in black. The trimmed out 25% most external points are depicted in gray; the remaining samples, used to compute the trimmed mean, are drawn in blue. (b) Trimmed means for different proportions of trimmed out points, corresponding to the normal (blue-gray) and cancer (red-gray) samples, for a subset of genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750619&req=5

Figure 2: Trimmed mean plots. (a) Representation in parallel coordinates of the 0.25-trimmed mean of the normal prostate samples, in black. The trimmed out 25% most external points are depicted in gray; the remaining samples, used to compute the trimmed mean, are drawn in blue. (b) Trimmed means for different proportions of trimmed out points, corresponding to the normal (blue-gray) and cancer (red-gray) samples, for a subset of genes.
Mentions: we obtain a plot like that in Figure2(a), where the 0.25-trimmed mean is visualized as a black line; additionally, the 0.25-trimmed sample, that is, the collection of samples remaining after removing the proportion 0.25 of less deep points are represented as blue lines, whereas the discarded samples appear as gray lines. These three colours can be modified with the parameter cols. Note that the samples removed in the plot are those defining the darkest region in Figure1(b).

Bottom Line: Therefore, there is a need for developing new, robust statistical techniques to analyze these data. depthTools 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Functional Genomics Team, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. aurora@ebi.ac.uk

ABSTRACT

Background: 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. Therefore, there is a need for developing new, robust statistical techniques to analyze these data.

Results: depthTools 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.

Conclusion: We 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.

Show MeSH
Related in: MedlinePlus