A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

doi:10.1186/1748-7188-4-8

Algorithms for Molecular Biology

Volume 4

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A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

Sara C Madeira¹^,2^,3 and Arlindo L Oliveira¹^,2

¹Knowledge Discovery and Bioinformatics (KDBIO) group, INESC-ID, Lisbon, Portugal

²Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal

³University of Beira Interior, Covilhã, Portugal

author email corresponding author email

Algorithms for Molecular Biology 2009, 4:8doi:10.1186/1748-7188-4-8


Published:	4 June 2009

Additional files

Additional file 1:

e-CCC-Biclustering: Related work on biclustering algorithms for time series gene expression data. Supplementary material describing related work on biclustering algorithms for time series gene expression data analysis. We describe in detail three state of the art biclustering approaches specifically designed to identify biclusters in gene expression time series and identify their strengths and weaknesses. We also explain and justify why we decided to compare the performance of e-CCC-Biclustering with that of CCC-Biclustering, but not with that of the q-clustering and CC-TSB algorithms.

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Additional file 2:

e-CCC-Biclustering: Algorithmic and complexity details. Supplementary material describing algorithmic and complexity details of e-CCC-Biclustering.

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Additional file 3:

Highly significant 1-CCC-Biclusters. Table showing a summary of the 47 1-CCC-Biclusters passing the Bonferroni correction for multiple testing at the 1% level when 1-CCC-Biclustering restricted to errors in the 1-neighborhood of the symbols in the alphabet Σ = {D, N, U} was applied to the DiscretizedHeatShock dataset.

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Additional file 4:

Highly significant CCC-Biclusters. Table showing a summary of the 25 CCC-Biclusters passing the Bonferroni correction for multiple testing at the 1% level when CCC-Biclustering was applied to the DiscretizedHeatShock dataset.

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Additional file 5:

Highly significant 1-CCC-Biclusters versus highly significant CCC-Biclusters. Table showing a comparison between the 47 highly significant 1-CCC-Biclusters discovered by 1-CCC-Biclustering restricted to errors in the 1-neighborhood of the symbols in the alphabet Σ = {D, N, U} and the 16 highly significant CCC-Biclusters found by CCC-Biclustering (after the applying the overlapping filter) and analyzed by Madeira et al. [9]. Both sets of biclusters were identified when the algorithm was applied to the DiscretizedHeatShock dataset.

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Additional file 6:

GO terms enriched and transcriptional regulations of the top 16 CCC-Biclusters. Table showing a detailed analysis of the GO terms enriched and transcriptional regulations of the top 16 CCC-Biclusters discovered with CCC-Biclustering. When the set of genes in the CCC-Bicluster have more than 10 transcription factors or more than 10 GO terms enriched, only the top 10 of each are shown. We only show the GO terms passing the Bonferroni correction for multiple testing at either the 1% level (highly significant) or the 5% level (significant). The p-values marked with * only passed the test at the 5% level. The p-values presented in the table are without correction as it is common practice in the literature.

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