U.S. Department of Energy

Pacific Northwest National Laboratory

DanteR

Used to perform various downstream data analysis, data reduction, and data comparison steps including normalization, hypothesis testing and clustering.  Note that InfernoRDN is the suggested replacement for DanteR (due to installation issues with DanteR).

Area of Research: 
Full Description: 

Although this software is available for download, it is not under active development and we therefore cannot provide support for its installation or use.  Instead, please use InfernoRDN.  If you have problems installing DanteR, please contact Tom Taverner at t.taverner@gmail.com

DanteR provides a graphical front-end to R for common data analysis tasks in "omics", with an emphasis on proteomics. It is a platform independent variant of InfernoRDN.

Key features include

  • Improved statistical and data manipulation functions, eg Normalization, Protein-Level ANOVA
  • Deep, interactive data exploration, eg Row Plotting, Volcano Plots, PCA Plots
  • Graphical output allows creation and export of publication-quality figures
  • Relationship tracking between datasets using metadata, for example factor information
  • Written as a stand-alone R package using RGtk2
  • Code is completely modular, so all functions can be used as stand-alone scripts
  • Full power of R is always available to the user via the command line
  • User written functions and packages can be integrated as add-ons with easily specified dialogs
  • Runs on Windows, Linux and Mac OS-X platforms
  • Supports R 2.15.1
Downloads: 
AttachmentSize
Application File238.16 KB
DanteR installation instructions137.35 KB
Software Instructions: 

Installation instructions can be found in file DanteR_Installation.pdf, available above.  In brief, you need R 2.15.1, Rtools, package RGtk2, plus several other required packages.

Supported file formats

Excel, Excel 2007, SQLite DB, Access, CSV, tab-delimited text

Supported locales

US and European delimiters and decimals

Data types

Crosstabulated numeric data(peptide intensity, expression ratios, spectral counts, gene expressions)

Metadata types

Row metadata (proteins, pathways, biological functions)
Column metadata/Factors (Experiment categories)

Basic data operations

Merge, sort, filter, apply arbitrary R commands

Metadata operations

Define row/column metadata, plot metadata linkages, create/apply aliases

Preprocessing

Normalization via eigenvalues, linear regression, LOESS, quantiles

Imputation

KNN, row means, model-based ANOVA, many others

Peptide-to-protein rollup

Via medians, means or quantiles

Parametric statistical operations

N-way ANOVA and robust ANOVA, two-factor protein-level ANOVA, Fisher test, model-based imputation/ANOVA

Nonparametric statistics

Shapiro-Wilks, Kruskal-Wallis

Other statistical operations

Interactive volcano plots, significance summaries

Plotting

Matrix/Scatterplot, 3D plots using RGL, histogram, QQ, boxplots, ellipses, Venn diagram

Data exploration

K-means and hierarchical clustering, pattern search, 2D and 3D PCA plot, dynamic metadata-linked row plotting

Addons

Users can include their own R-based addons to the program

Addon dialogs

A simple markup language allows dialogs to be created with no GUI experience

 Note that InfernoRDN is the suggested replacement for DanteR (due to installation issues with DanteR).

 

Instructional Downloads: 

Acknowledgment

All publications that utilize this software should provide appropriate acknowledgement to PNNL and the OMICS.PNL.GOV website. However, if the software is extended or modified, then any subsequent publications should include a more extensive statement, as shown in the Readme file for the given application or on the website that more fully describes the application.

 

Disclaimer

These programs are primarily designed to run on Windows machines. Please use them at your own risk. This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.

Portions of this research were supported by the NIH National Center for Research Resources (Grant RR018522), the W.R. Wiley Environmental Molecular Science Laboratory (a national scientific user facility sponsored by the U.S. Department of Energy's Office of Biological and Environmental Research and located at PNNL), and the National Institute of Allergy and Infectious Diseases (NIH/DHHS through interagency agreement Y1-AI-4894-01). PNNL is operated by Battelle Memorial Institute for the U.S. Department of Energy under contract DE-AC05-76RL0 1830.

We would like your feedback about the usefulness of the tools and information provided by the Resource. Your suggestions on how to increase their value to you will be appreciated. Please e-mail any comments to proteomics@pnl.gov

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