Lymphoma type prediction based on microarray data

Created: 2010-05-11 19:04:30 Last updated: 2010-05-11 19:04:32

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Scientific value Using gene-expression patterns associated with DLBCL and FL to predict the lymphoma type of an unknown sample. Using SVM (Support Vector Machine) to classify data, and predicting the tumor types of unknown examples. Steps Querying training data from experiments stored in caArray. Preprocessing, or normalize the microarray data. Adding training and testing data into SVM service to get classification result.

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Authors (1)

Titles (1)

Descriptions (1)

Dependencies (0)

Inputs (1)

Name	Description
EXPID	The experiment ID through which to query microArray data from caArray grid service.

Processors (3)

Name	Type	Description
Extract_Microarray	workflow
Preprocess_Microarray	workflow
Predict_Lymphoma_Type	workflow

Beanshells (4)

Name	Inputs	Outputs
Beanshell	expid	searchHyb
Beanshell2	i	result o
Beanshell3	i	testClass trainingClass testDataString trainingDataString
Result_CSV	in0 in1	out0

Outputs (1)

Name	Description
Result_CSV

Datalinks (8)

Source	Sink
EXPID	Extract_Microarray:EXP_ID
Extract_Microarray:testData	Preprocess_Microarray:testData
Extract_Microarray:trainingData	Preprocess_Microarray:trainingData
Preprocess_Microarray:trainingData	Predict_Lymphoma_Type:trainingData
Preprocess_Microarray:testData	Predict_Lymphoma_Type:testData
Extract_Microarray:trainingClass	Predict_Lymphoma_Type:trainingClass
Extract_Microarray:testClass	Predict_Lymphoma_Type:testClass
Predict_Lymphoma_Type:Result_CSV	Result_CSV

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Information Workflow Type

Taverna 2

Information Uploader

Wei Tan

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Information Version 7 (latest) (of 7)

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Information Statistics

9931 viewings

3888 downloads

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Citations (3)

1. Wei Tan, Ravi Madduri, Alexandra Nenadic, Stian Soiland-Reyes, Dinanath Sulakhe, Ian Foster, Carole A. Goble, caGrid Workflow Toolkit: A Taverna based workflow tool for cancer Grid, BMC Bioinformatics, 02 November 2010, http://www.biomedcentral.com/1471-2105/11/542, Accessed at: 03 September 2011

2. caArray, Experiment data: Diffuse large B-cell lymphoma outcome prediction , 23 April 2009, https://array.nci.nih.gov/caarray/project/golub-00095, Accessed at: 23 April 2009

3. MA Shipp et al, Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning, NATURE MEDICINE, 23 January 2002, http://www.broad.mit.edu/mpr/publications/projects/Lymphoma/Shipp_et_al_2002.pdf, Accessed at: 23 April 2009

Version History

In chronological order:

Lymphoma type prediction based on microarray data

Created by Wei Tan on Thursday 23 April 2009 17:10:37 (UTC)

Last edited by Wei Tan on Thursday 23 April 2009 17:15:00 (UTC)
Lymphoma type prediction based on microarray data

Created by Wei Tan on Friday 15 May 2009 18:13:17 (UTC)

Revision comment:

We added another classification service, KNN into the previous version.
Lymphoma type prediction based on microarray data

Created by Wei Tan on Tuesday 09 June 2009 22:10:13 (UTC)

Revision comment:

The workflow is now organized into three nested workflows.

With more explanations and instructions on input.

Output is more readible as a CSV string.
Lymphoma type prediction based on microarray data

Created by Wei Tan on Tuesday 28 July 2009 17:17:03 (UTC)

Revision comment:

Added the version presented at caBIG annual meeting 09.
Lymphoma type prediction based on microarray data

Created by Wei Tan on Monday 16 November 2009 06:16:23 (UTC)

Revision comment:

updated with the upgrade of caArray service v 2.3.
Lymphoma type prediction based on microarray data

Created by Wei Tan on Monday 16 November 2009 06:21:17 (UTC)

Revision comment:

update the workflow to work with caArray 2.3
Lymphoma type prediction based on microarray data

Created by Wei Tan on Tuesday 11 May 2010 19:04:30 (UTC)

Revision comment:

the genepattern grid services moved to broadinstitute.org

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Other workflows that use similar services (4)

Only the first 2 workflows that use similar services are shown. View all workflows that use these services.

Taverna 2

Uploader

Wei Tan

caArray data retrieving (1)

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Query all the gene expression data in a caArray experiment. Returns a evenly divided gene expression data set with corresponding class information. They ca be later used as training and test data set in many classification algorithms.Query all the gene expression data in a caArray experiment. Returns a evenly divided gene expression data set with corresponding class information. They can be later used as training and test data set in many classification algorithms.

Created: 2009-11-23

Credits: Wei Tan

Taverna 2

Uploader

Wei Tan

genePattern data preprocessing (2)

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preprocess data set using genePattern preProces service, the input should be in genePattern STATML format. Configuration parameters can be adjusted by changing the default preprocess data set using genePattern preProces service, the input should be in genePattern STATML format.preprocess data set using genePattern preProces service, the input should be in genePattern STATML format. Configuration parameters can be adjusted by changing the string constants.

Created: 2010-05-24 | Last updated: 2010-05-24

Credits: Wei Tan