Inferno

Tags: Inferno, DAnTE, Genomics, Proteomics, Software, Statistics, Analysis, Analytics, Proteins, Peptides, Microarray, Tool

Inferno (formally known as DAnTE: Data Analysis Tool Extension) can perform various downstream analyses on large scale datasets from proteomics and genomics experiments.

Source code for Inferno is hosted on Github here: https://github.com/ashokapol/inferno4proteomics *** Developed by Ashoka Polpitiya (ashoka.pol @ gmail.com)

Installing Inferno

Install the latest version of R as administrator (as of 03/21/2016 it is version 3.2.4). To run R as administrator, right click on the downloaded R installation file and select ‘Run as administrator’ from the menu. R needs to be installed with the SDI display option (you are prompted for using either SDI or MDI during the installation of R. Select the customized option to install). And you also need to tell R to write its registry entries during installation (if you are prompted for that).

Then start R as administrator (right click on R icon/application and select ‘Run as administrator’) and type the following commands at the R prompt (you can cut and paste):

options(install.packages.check.source = "no")
install.packages(c("rscproxy","rcom"),repos="http://rcom.univie.ac.at/download",lib=.Library,type="win.binary")
library(rcom)
comRegisterRegistry()
installstatconnDCOM()

This will install statconnDCOM and set the R environment for Inferno.

Then download the Inferno executable (see the top-left of this page for the zip file) and install it (again as administrator). The first time you run Inferno, it will install all the necessary R packages and may take few minutes to complete. ***

Among many features of Inferno are:

• A set of diagnostic plots (Histograms, boxplots, correlation plots, qq-plots, peptide-protein rollup plots, MA plots, PCA plots, etc).
• Log transforming.
• Rolling up to proteins (3 methods are available).
• LOESS normalization
• Linear Regression Normalization
• Mean Centering
• Median Absolute Deviation (MAD) Adjustment across datasets
• Quantile Normalization
• Principal Component Analysis
• Partial Least Squares Analysis
• ANOVA (multi-way, unbalanced, random effects)
• Heatmaps with Hierarchical and K-means cluster options

Dependencies:

Inferno depends on the following:

1. MS Windows OS with .NET 2.0 framework
2. R Statistical Environment (http://www.r-project.org/)
3. statconnDCOM server (http://rcom.univie.ac.at)

Inferno uses the following R packages (from http://cran.r-project.org/):

• amap: Another Multidimensional Analysis Package
• car: Companion to Applied Regression
• nlme: Linear and Nonlinear Mixed Effects Models
• outliers: Tests for outliers
• fpc: Fixed point clusters, clusterwise regression and discriminant plots
• pls: Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR)
• MASS: Main Package of Venables and Ripley’s MASS
• impute: Imputation for microarray data
• qvalue: Q-value estimation for false discovery rate control
• e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
• gplots: Various R programming tools for plotting data
• ellipse: Functions for drawing ellipses and ellipse-like confidence regions
• plotrix: Various plotting functions
• scatterplot3d: 3D Scatter Plot
• colorspace: Colorspace Manipulation

License Agreement

Inferno is licensed under the Apache License, Version 2.0; you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

All publications that utilize this software should provide the citation below:

Polpitiya AD, Qian WJ, Jaitly N, Petyuk VA, Adkins JN, Camp DG 2nd, Anderson GA, Smith RD., DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics. 2008 Jul 1;24(13):1556-8. (PMID: 18453552)