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CancerDiscover

A data mining suite for cancer classification

CancerDiscover is an open source pipeline tool (released under the GNU General Public License v3) that allow users to efficiently and automatically process large high-throughput datasets by converting data (for example CEL files, etc.), normalizing, and selecting best performing features from multiple feature selection algorithms. The pipeline lets users apply different feature thresholds and various learning algorithms to generate multiple prediction models that distinguish different types and subtypes of cancer.

Note: CancerDiscover is an open-source software, in case if you run across bugs or errors, raise an issue over here.

Table of Contents

• Downloading CancerDiscover
• Dependencies
• System Requirements
• Directory Structure of the Pipeline
• Execution of Pipeline
• Contribution
• License

This README file will serve as a guide for using this software tool. We suggest reading through the entire document at least once, in order to get an idea of the options available, and how to customize the pipeline to fit your needs.

Downloading CancerDiscover

Clone the git repository:

$ git clone https://github.com/akram-mohammed/CancerDiscover.git && cd CancerDiscover

Dependencies

Before downloading CancerDiscover, make sure you have all the necessary software packages installed.

Installing R

From the command line, enter the following commands below:

sudo apt-get update
sudo apt-get install r-base

Installing Bioconductor and R packages

Run R

Once R is installed you’ll need to run the commands from within R. From the commandline enter the following command:

R

Install Bioconductor

Once R has finished loading, enter the following command:

source("http://bioconductor.org/biocLite.R")

If there is a updated bioconductor package is available, run the following command:

biocLite("BiocUpgrade")

Install Affy R module

Enter the following command to install the Affy R package:

biocLite("affy")

Annotation Database Interface

You will also need a package called AnnotationDbi which can be installed with the command below:

biocLite("AnnotationDbi")

It provides user interface and database connection code for annotation data packages using SQLite data storage.

CDF (Chip Definition File)

Command to download the plate HG_U133_Plus2 cdf:

biocLite("hgu133plus2cdf")

It is important to note that not all data must have been derived from affymetrix plates which meet the requirements put in place by the Affy R package. Plates such as HG_U95 and HG_U133 are known to be acceptable as long as their associated cdf has been installed.

Installing WEKA

This project utilizes WEKA 3-6-11. In order to get this version, in a directory outside of the CancerDiscover directory, execute the following command:

wget https://sourceforge.net/projects/weka/files/weka-3-6/3.6.11/weka-3-6-11.zip/download

Next, set the WEKA classpath by entering the following command in .bashrc file under Alias definitions:

export WEKAINSTALL=/absolute/path/to/weka/directory/`
export CLASSPATH=$CLASSPATH:$WEKAINSTALL/weka.jar

For example:

export WEKAINSTALL=/home/general/weka/weka-3-6-11`
export CLASSPATH=$CLASSPATH:$WEKAINSTALL/weka.jar

Note: Since WEKA is Java-based framework, the user needs to install and set the classpath for JAVA.

To install CancerDiscover dependencies right from scratch, check out our exhaustive guides:

• A Hitchhiker's Guide to Installing CancerDiscover on Linux OS
• A Hitchhiker's Guide to Installing CancerDiscover on Mac OS X

System Requirements

You will need current or very recent generations of your operating system: Linux OS, Mac OS X.

Directory Structure of the Pipeline

After downloading CancerDiscover, notice inside the CancerDiscover directory there are several empty directories and one which contains all of the scripts necessary to process data:

• DataFiles will contain raw CEL files and sampleList.txt file;
• Outputs directory contains resultsSummary.txt file which will have the summary of the model accuracies as well as information regarding the context which gave the highest accuracy;
• Scripts directory contains all of the source code;
• Models directory contains all of the classification models;
• Temp directory contains intermediate files that are generated as part of the execution of the pipeline;
• Feature Selection directory contains the feature selection algorithm output files and two nested directories for arff file; generation, namely Chunks and ArffPreprocessing;
• Chunks contains different threshold feature sets;
• ArffPreprocessing directory contains the feature vectors in arff format . Feature vectors made here are split into training and testing datasets in their respective directories;
• Train is the repository of the training data for the modeling;
• Test is the repository of the testing data for model testing;
• SampleData is a directory which contains 10 sample CEL files and their associated sampleList.txt file;
• Logs is a directory which contains the elapsed time in seconds for each leg of the pipeline from initialization through model testing;
• CompletedExperiments When the pipeline has finished running, the above directories which contain experimental data will be moved into this directory. This directory will act as a repository of old experiment files organized by a time-stamp which reads as Year-month-day-hours-minutes-seconds.

Execution of Pipeline

The first step is be to place your raw CEL file data into the DataFiles directory.

In the DataFiles directory you will need to create a two column csv (comma separated file) called "sampleList.txt" where the first column will have the name of each CEL file, and the second column will have the class identifier to be associated with that sample. See an example below:

CL2001031606AA.CEL,squamousCellCarcinoma
CL2001031607AA.CEL,squamousCellCarcinoma
CL2001031608AA.CEL,adenocarcinoma
CL2001031609AA.CEL,squamousCellCarcinoma
CL2001031611AA.CEL,adenocarcioma

If you want to use the Sample data for classification: cp SampleData/* ../DataFiles This command will copy all of the data and sampleList.txt files in the SampleData directory to the DataFiles directory.

1. Initialization

Once you have finished making the sampleList.txt file in the DataFiles directory, please go inside the Scripts directory to execute the next steps of the pipeline.

There are two versions of the pipeline, BASH and SLURM (Simple Linux Utility for Resource Management). SLURM is a computational architecture used to organize user requests into a queue to utilize super-computer resources. SLURM requires no kernel modifications for its operation and is relatively self-contained. Depending on your access to a SLURM scheduler, you will use one or another set of scripts. If you do have access to a SLURM scheduler you will execute the scripts ending in .slurm. Otherwise, you will use the scripts ending in .bash . Due to the complexity of data manipulation, and/or the sheer size of your data, it is recommended to use a high-performance computer.

Now, in the Scripts directory, edit the file called Configuration.txt, to make any changes desired for processing your data including the normalization method, the size of data partitions, and which feature selection and classification algorithms are to be executed . The default settings for normalization and background correction and data partitioning are:

  - Normalization: `normMethod="quantiles"`
  - Background correction: `bgCorrectMethod="rma"`
  - Pm value correction: `pmCorrectMethod="pmonly"`
  - Summary: `summaryMethod="medianpolish"`
  - Number of folds for data partitioning: `foldNumber=2`

The default setting for data partitioning is 50:50.

The default setting for feature selection algorithms will choose all possible feature selection algorithm options. You can find the list of feature selection methods and their associated file names in featureSelectionAlgorithms.lookup under Scripts directory.

The default setting for classification algorithms will generate models using the following algorithms:

  - Decision Tree  
  - IBK   
  - Naive Bayes   
  - Random Forest   
  - Support Vector Machine    

If you wish to use classification algorithms other than the ones provided, please refer to the WEKA resources at http://weka.wikispaces.com/Primer. In the configuration file you will also need to write in the absolute path. This path should end in CancerDiscover; for example a directory path might look like: work/userGroup/userMember/data/CancerDiscover

   cd ../Scripts
   bash initialization.bash

2. Normalization

bash masterScript_1.bash

For SLURM users:

sbatch masterScript_1.slurm

The purpose of the above script is to perform normalization on raw CEL data and generate the Expression set matrix. For other options, refer to https://www.bioconductor.org/packages/devel/bioc/vignettes/affy/inst/doc/builtinMethods.pdf

3. Feature Selection

After normalization is complete, you will have a single file called ExpressionSet.txt in your DataFiles directory. The next step is to build a master feature vector file using the ExpressionSet.txt file. The next command you use will build this master feature vector file for you using the ExpressionSet.txt file, as well as perform data partitioning, or divide the master feature vector file into two parts; training and testing. The program will then perform feature selection using only the training portion of the master feature vector. Additionally, you can find the list of feature selection methods and their associated file names in the Scripts directory in the file named featureSelectionAlgorithms.lookup.

The default setting for data partitioning is 50:50, meaning the master feature vector file will be split evenly into training and testing data sets while retaining approximately even distributions of your sample classes between the two daughter files. To achieve a larger split, such as 80:20 for training/testing, in the configuration file Configuration.txt replace the 2 with a 5. This will tell the program to perform 5 folds, where the training file will retain 4 and the testing file will retain a single fold or 20% of the master feature vector data.

The default setting for feature selection will perform all possible forms of feature selection available unless otherwise specified in the configuration.txt file. If you wish to change these feature selection options, in the Scripts directory you will need to edit the file named configuration.txt. Simply write TRUE next to all of the feature selection methods you wish to perform and FALSE if you do not want that method performed. Additionally, you can find the list of feature selection methods and their associated file names in the Scripts directory in the file named featureSelectionAlgorithms.lookup.

The following commands perform the feature selection from normalized expression matrix:

bash masterScript_2.bash

For SLURM users:

sbatch masterScript_2.slurm

4. Model training and testing

Once feature selection has been completed, new feature vectors are made based on the ranked lists of features. The new feature vectors will be generated based on your threshold selections, and immediately used to build and test classification models using a classification algorithm of your choosing. Lastly, the directories will be reset, and your old directories and files will be placed in the CompletedExperiments followed by a time-stamp.

The following commands perform model training and testing on the feature vectors:

bash masterScript_3.bash

For SLURM users:

sbatch masterScript_3.slurm

The last lines of the masterScript_3 scripts will move the content of the DataFiles to CompletedExperiments, so the new experiment will run in DataFiles directory. You can find all raw data, feature selection outputs, training and testing feature vectors, models, and model results in the CompletedExperiments directory followed by a time-stamp. To run experiments with new data, begin with step 1.

Overall, the users will require to run only 4 scripts.

bash initialization.bash 	# Initialization
bash masterScript_1.bash 	# Normalization
bash masterScript_2.bash	# Feature Selection
bash masterScript_3.bash	# Model Training and Testing

Contribution

Akram Mohammed amohammed3@unl.edu

Greyson Biegert greyson@huskers.unl.edu

Jiri Adamec jadamec2@unl.edu

Tomas Helikar thelikar2@unl.edu

License

This software has been released under the GNU General Public License v3.