InfernoRDN can perform various downstream analyses on large scale datasets from proteomics and microarrays.

Some of the features included with InfernoRDN:

• 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

InfernoRDN is an updated version of Inferno (which used StatconnDCOM). It supersedes all previous DAnTE (Data Analysis Tool Extension), DanteR, and Inferno versions.

InfernoRDN uses R.NET (https://github.com/jmp75/rdotnet) to communicate with R.

1. Download and install the latest version of R 4.x

   • https://cran.r-project.org/bin/windows/base/
   • If you prefer R 3.x, you must have R 3.6 or newer

2. Download the InfernoRDN installer from:

   • https://github.com/PNNL-Comp-Mass-Spec/InfernoRDN/releases

3. Run the installer, InfernoRDNSetup.exe

4. Start InfernoRDN using the Start Menu or desktop shortcut

   • You may need to run InfernoRDN as an administrator; see step 6 below

5. The InfernoRDN splash screen will appear and status messages will be shown

   • If a Dialog box appears asking "Would you like to use a personal library?" you should answer Yes to that question, and Yes to the next question regarding the folder to use for the personal library
   • Following this, several Bioconductor packages will be downloaded

6. Diagnosing Startup or Plotting Errors

   • If you see the error message "R failed to install required packages", try manually installing the packages
     • See section "Manual Package Installation" below
     • After installing the packages, use the commands in the "Package Verification" section
     • Provided you can manually load the packages with the library() command, you can likely ignore the "R failed" message shown when InfernoRDN starts
   • Alternatively, you could try running InfernoRDN as an administrator, but this shouldn't be required
     • Right click the shortcut to InfernoRDN and choose "Run as Administrator"
     • Alternatively, navigate to C:\Program Files\InfernoRDN, right click Inferno.exe and choose "Run as administrator"
   • When diagnosing errors, examine the newest rcmd log file at %AppData%\Inferno
     • For example, C:\Users\d3l243\AppData\Roaming\Inferno\rcmdlog.txt
     • or C:\Users\d3l243\AppData\Roaming\Inferno\rcmdlog5.txt

7. Test loading a data file

   • Choose File, Open, Expression File
   • Navigate to C:\Program Files\InfernoRDN\Sample_Data_Files
   • Select SampleInput4DAnTE.csv and click Open
   • Choose column Mass_Tag_ID then click the ">>" button to the left (and just below) "Unique Row ID"
   • Enable checkbox "Protein ID"
   • Select column MinOfOrf then click the ">>" button to the left (and just below) "Protein ID"
   • Select data columns P10A through P19B then click the ">>" button to the left (and below) "Data Columns"
   • Click OK

8. Test the plotting

   • Choose Plot, Correlation
   • Enable checkbox Toggle All, then click OK

InfernoRDN depends on the following:

1. Windows 7 (or newer) with the .NET 4.6 framework or newer

   • https://www.microsoft.com/en-us/download/details.aspx?id=53344

2. R Statistical Environment, version 3.6 or newer

   • https://cran.r-project.org/bin/windows/base/

3. Bioconductor

   • This should get installed automatically by InfernoRDN
   • You may need to manually upgrade Bioconductor, using the steps outlined at https://bioconductor.org/install/

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

• amap: Another Multidimensional Analysis Package
• car: Companion to Applied Regression
• lattice: Linear and Nonlinear Mixed Effects Models
• 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
• e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
• ggplot2: 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
• Hmisc: Harrell Miscellaneous
• Cairo: R graphics device using cairo graphics library

Legacy packages (not available for R 3.x or 4.x)

• impute: Imputation for microarray data
• qvalue: Q-value estimation for false discovery rate control

The packages will be installed to either the library folder in C:\Program Files\R\R-3.x.x\library or, more likely (due to permissions) to the R\win-library folder in your "Documents" or "My Documents" folder.

Start R by double clicking R.exe, for example at C:\Program Files\R\R-4.1.1\bin\R.exe

Run this command:

install.packages(c("amap", "car", "lattice", "nlme", "outliers", "fpc", "pls", "MASS", "impute", "qvalue", "e1071", "ggplot2", "ellipse", "plotrix", "scatterplot3d", "colorspace", "jpeg", "Hmisc", "Cairo"), repos='https://cran.revolutionanalytics.com/')

Answer "yes" if prompted with:

• Would you like to use a personal library instead?

Answer "yes" if prompted with:

• Would you like to create a personal library?

Answer "No" when prompted with:

• Do you want to install from sources the packages which need compilation?

Note that package impute is available in e1071

Start R.exe, as described above

Run these commands:

# View installed packages
installed.packages()

# Confirm that packages can be loaded
library(amap)
library(car)
library(lattice)
library(nlme)
library(outliers)
library(fpc)
library(pls)
library(MASS)
library(e1071)
library(ggplot2)
library(ellipse)
library(plotrix)
library(scatterplot3d)
library(colorspace)
library(Hmisc)
library(Cairo)

If InfernoRDN has problems connecting to R after you re-install InfernoRDN (for example, when running ANOVA), try the following

• Exit InfernoRDN
• Navigate to %LocalAppData%\Pacific_Northwest_Nationa
  • For example, C:\Users\d3l243\AppData\Local\Pacific_Northwest_Nationa
  • Yes, the directory name is truncated (it does not end in l)
• Delete any directories that you see there, example names:
  • Inferno.exe_Url_2sg0gwzl52pgvsl5ykzc0musjbmtk3m0
  • Inferno.exe_Url_psrpjex41w0dwbl34gci2qitsop3f50e
• Start InfernoRDN
  • The program will likely re-download all of the required R packages, a process that can take several minutes
• Test the problematic function again

Factors can be defined using the GUI, or by loading a text file listing the factors to associate with each dataset (column name) in the expressions table.

A factor definitions file can be a CSV file (comma-separated) or a .txt file (tab-separated)

The first row of the factor definitions file must have a column named Factor, then column names that match the names in the originally loaded Expressions table. Each subsequent row of the factor file is a new factor name, then the factor value for each dataset.

The following shows example rows of a factor definitions file (tab-separated). There are 6 datasets and two factors (Time and Temperature) defined for each dataset.

Inferno saves data as R Session files, with a .dnt extension. These files can be opened with RStudio for custom data analysis.

• Create a .dnt file inside InfernoRDN using File, Save Session
• Rename the .dnt file to have extension .rdata
• Start R Studio
• Choose File, Open File
• Select the .rdata flie
• Answer "Yes" to the question "Do you want to load the R data file "~/Path/DataFile.rdata" into the global environment?"

The environment tab should now show one or more data matrices

• Inside RStudio, choose File, Open File
• Select the desired R script, e.g. Rscripts\Rollup\QRollUp.R
• A new tab should appear with the .R file
  • Click the Source button, which will run a command like this in the Console

source('~/Projects/_CommunityApplications/InfernoRDN/Rscripts/Rollup/QRollUp.R')

• Repeat for any additional required files
  • For example QRollUp.R uses RollupScore.R
• Manually call a method, e.g.

QRollup.proteins(Eset, ProtInfo, 30, 0, 3, FALSE, FALSE)

• View data

View(Eset)
View(oneHitProtNames)

Use the following steps to load every InfernoRDN script into RStudio

• Start RStudio
• Under the "Session" menu choose "Load workspace".
  • Select a .dnt file saved from InfernoRDN
• Use "Load Workspace" two more times
  • Load Inferno.RData
  • Load Inferno_stdplots.RData

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):1556-1558. https://www.ncbi.nlm.nih.gov/pubmed/?term=18453552

Developed by Ashoka Polpitiya for the US Department of Energy and TGen
Includes contributions from Gary Kiebel and Matthew Monroe at PNNL
PNNL, Richland, WA, USA.
TGen, Phoenix, AZ, USA.

Copyright 2007, 2014, Battelle Memorial Institute. All Rights Reserved.
Copyright 2010, Translational Genomics Research Institute. All Rights Reserved.

E-mail: proteomics@pnnl.gov or ashoka@tgen.org
Website: https://github.com/PNNL-Comp-Mass-Spec/InfernoRDN

InfernoRDN 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 result from the use of this software should include the following acknowledgment statement:

Portions of this research were supported by NIH, DOE, and Pacific Northwest National Laboratory (PNNL), in addition to the Center for Proteomics, Translational Genomics Research Institute (TGEN).

However, if the software is extended or modified, any subsequent publications should include a more extensive statement, using this text or a similar variant:

Portions of this research were supported by the National Institute of General Medical Sciences (NIGMS, Large Scale Collaborative Research Grants U54 GM-62119-02), the NIH National Center for Research Resources (RR18522), and the National Institute of Allergy and Infectious Diseases NIH/DHHS (through interagency agreement Y1-AI-4894-01).