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Open Access Highly Accessed Research article

Gene expression meta-analysis supports existence of molecular apocrine breast cancer with a role for androgen receptor and implies interactions with ErbB family

Sandeep Sanga12, Bradley M Broom3, Vittorio Cristini12 and Mary E Edgerton124*

Author Affiliations

1 Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA

2 School of Health Information Sciences, The University of Texas Health Science Center, Houston, TX, USA

3 Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA

4 Department of Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA

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BMC Medical Genomics 2009, 2:59  doi:10.1186/1755-8794-2-59

Published: 11 September 2009

Additional files

Additional File 1:

Supplementary Figures. A file containing the supplementary figures S1-S10 referred to in the manuscript.

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Additional File 2:

The 100-probeset signatures for differentiating molecular apocrine and non-molecular apocrine phenotypes derived from Doane et al. and Farmer et al. cohorts individually. These gene signatures were derived from the Doane et al. and Farmer et al. samples separately following normalization using log transformation, quantile normalization, XPN processing and updated probeset definitions. There are 76 overlapping genes between the two signatures. The signatures were derived using Significance Analysis of Microarrays.

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Additional File 3:

The 346-probeset signature for differentiating molecular apocrine and non-molecular apocrine phenotypes. The signature was identified using Significance Analysis of Microarrays software on the combined Doane et al. and Farmer et al. cohorts at a false discovery rate of 0%. All data was normalized using log transformation, quantile normalization, XPN processing and updated probeset definitions. There are 76 overlapping genes between the two signatures.

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Additional File 4:

200 Gene Clusters Identified by Unsupervised Gene Shaving. A list of the genes in each of the top 200 gene clusters identified by unsupervised Gene Shaving. Analysis was performed on all samples (Farmer et al., Doane et al., Ivshina et al., Rouzier et al., and Sotiriou et al.) following normalization using log transformation, quantile normalization, XPN processing and updated probeset definitions.

Format: XLS Size: 76KB Download file

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