Expression-based Pathway Signature Analysis (EPSA): Mining publicly available microarray data for insight into human disease

doi:10.1186/1755-8794-1-51

BMC Medical Genomics

Volume 1

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Expression-based Pathway Signature Analysis (EPSA): Mining publicly available microarray data for insight into human disease

Jessica D Tenenbaum⁴ , Michael G Walker¹ , Paul J Utz² and Atul J Butte¹^,3

¹Stanford Medical Informatics, 251 Campus Drive MSOB x215, Stanford, CA 94305, USA

²Stanford University School of Medicine, Department of Medicine, Division of Immunology and Rheumatology, CCSR Building, Room 2215, Stanford, CA 94305, USA

³Stanford University School of Medicine, Department of Pediatrics, Stanford, CA 94305, USA

⁴Duke Translational Medicine Institute, PO Box 17969, Durham, NC 27715, USA

author email corresponding author email

BMC Medical Genomics 2008, 1:51doi:10.1186/1755-8794-1-51


Published:	20 October 2008

Additional files

Additional file 1:

Actual versus randomly permuted correlations using Connectivity Map perturbagen profiles and an ovarian cancer cohort. This figure shows statistically significant correlations between perturbagen signature profiles and ovarian cancer tumor profiles, compared to randomly generated correlations.

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Additional file 2:

Correlation values for murine and human positive controls with AfCS compendium ligands. These panels show the relative correlations between known perturbations and the profiles observed in the AfCS murine dataset.

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Additional file 3:

Actual versus randomly permuted correlations using AfCS ligand profiles and a DLCBL patient cohort. Comparison of average correlation of DLBCL patient profiles and murine compendium pathway signatures, versus compendium ligand signatures and randomly permuted signatures.

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Additional file 4:

Q-values for correlation of Rosenwald data with AfCS compendium ligands. This graphs illustrates the false discovery rates of the observed correlations between DLBCL profiles and ligands signatures from the AfCS murine dataset.

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