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Open Access Highly Accessed Methodology article

Combining multiple hypothesis testing and affinity propagation clustering leads to accurate, robust and sample size independent classification on gene expression data

Argiris Sakellariou12, Despina Sanoudou3 and George Spyrou1*

Author Affiliations

1 Biomedical Informatics Unit, Biomedical Research Foundation of the Academy of Athens, Athens, Greece

2 Department of Informatics and Telecommunications, National & Kapodistrian Univ. of Athens, Athens, Greece

3 Pharmacology Department, Medical School, National & Kapodistrian Univ. of Athens, Athens, Greece

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BMC Bioinformatics 2012, 13:270  doi:10.1186/1471-2105-13-270

Published: 17 October 2012

Additional files

Additional file 1:

This function implements in R-code, the mAP-KL’s functionality.

Format: R Size: 6KB Download file

Open Data

Additional file 2:

In this file, we present the 5-CV classification results for all real microarray data, when using three different classifiers (SVM-linear, KNN, and RF).

Format: XLSX Size: 35KB Download file

Open Data

Additional file 3:

In this file, we present the Hold-out validation results for all real microarray data, when using three different classifiers (SVM-linear, KNN, and RF).

Format: XLSX Size: 24KB Download file

Open Data

Additional file 4:

In this file, we have cited the subsets of genes according to the mAP-KL method.

Format: XLSX Size: 14KB Download file

Open Data

Additional file 5:

Contains the microarray data used in this experiment. For each disease, we provide the ‘class_labels.csv’, ‘train.csv’ and ‘test.csv’ files, which represent the analogy of samples as described in table 1. The intensity values are unprocessed.

Format: ZIP Size: 15.3MB Download file

Open Data

Additional file 6:

In this file, we have cited the clustering setup parameters, the DEGs position per simulation dataset, as well as the DEGs identified per method.

Format: XLS Size: 100KB Download file

This file can be viewed with: Microsoft Excel Viewer

Open Data

Additional file 7:

In this file, we present the classification results of (mAP-KL, eBayes, maxT, RF-MDA) in the first simulation setup, where the clustering identification was under investigation. We employed three classifiers (SVM-linear, KNN, and RF).

Format: XLSX Size: 31KB Download file

Open Data

Additional file 8:

In this file, we present the classification results in the ‘choedata’ when using two different mAP-KL’s subsets, stemming from two different ranking approaches. We used the SVM-linear, KNN, and RF classifiers to assess their performance.

Format: XLSX Size: 12KB Download file

Open Data

Additional file 9:

This file contains the relevant scripts and functions for generating the simulated data. The ‘clusterSim’ r-package is required.

Format: ZIP Size: 5KB Download file

Open Data