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Totally four gene sets of size 20 are constructed from 278 breast tumor gene expression data (expO database) by applying monotonically expressed gene analysis (MEGA).
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Clinical cancer expression array data sets (ExpO) covering 1786 multi-tissue tumour specimens were retrieved from the NCBI Gene Expression Omnibus (accession number GSE2109).
All the biclusters and the enrichment analysis can be found in Additional file 7. DeBi algorithm is capable of analyzing yeast data size 6100 × 300) in 6 minutes, ExpO data (size 40223 × 2158) in 12 minutes, MSigDB data (size 18205 × 3272) in 11 minutes, DLBCL data (size 610 × 180) in 11 seconds, CMap data (size 22283 × 6100) in 3 hours 45 minutes.
Gene expression and annotation data from the ExpO consortium (http://www.intgen.org/expo/) were downloaded from GEO (GSE2109) in December 2008, including batches 1 16.
Additionally, we selected 40 genes from expO data using two-way ANOVA and multiple regression models.
Most bladder tumors in the expO data set are transitional cell carcinoma (TCC).
It illustrates that DeBi performs better than QUBIC in ExpO data.
It should be mentioned that all lobular carcinomas in the expO data set are of the classical, non-pleomorphic type.
We also found there are four experiments which used expO data set in the ONCOMINE test set (Bittner et al).
We used 278 breast tumor gene expression data from the expO (expression project for Oncology) database (http://www.intgen.org/expo.cfm, International Genomics Consortium).
In the expO data set these problems are not present such that we can assume that our results are not confounded with the above mentioned issues.
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