Data from: Significance Analysis of Prognostic Signatures
Data files
Jan 25, 2013 version files 3.52 GB
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Breast_ERHigh.zip
297.20 MB
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Breast_ERLow.zip
292.65 MB
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Breast_ERNegHer2Neg.zip
295.83 MB
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Breast_Global.zip
299.54 MB
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Breast_Her2.zip
299.23 MB
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Breast.Ps.OnPermutedData.RData.zip
6.73 MB
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Breast.zip
535.37 MB
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BreastOutput_SubScaled.zip
875.54 KB
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BreastOutput_TradScaled.zip
883.55 KB
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BreastOvary_HCv2.zip
143.83 KB
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BreastSubtypeSpecScaleRankDir.zip
675.82 KB
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computeSAPS.Permute.PValue.R.zip
1.50 KB
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FinalOutput_Breast.zip
3.81 MB
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FinalOutput_Ovary.zip
2.52 MB
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molsigdb.v3.0.entrezForR.txt.zip
1.72 MB
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Ovary_Angio.zip
297.98 MB
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Ovary_Global.zip
294.81 MB
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Ovary_NonAngio.zip
301.11 MB
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Ovary.Ps.OnPermutedData.RData.zip
4.08 MB
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Ovary.zip
284.34 MB
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OvaryOutput_SubScaled.zip
574.26 KB
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OvaryOutput_TradScaled.zip
590.33 KB
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OvaryTradScaleRankDir.zip
370.57 KB
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README_for_Breast_ERHigh.txt
293 B
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README_for_Breast_ERLow.txt
291 B
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README_for_Breast_ERNegHer2Neg.txt
289 B
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README_for_Breast_Global.zip
299.54 MB
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README_for_Breast_Her2.txt
274 B
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README_for_Breast.Ps.OnPermutedData.RData.txt
231 B
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README_for_Breast.txt
671 B
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README_for_BreastOutput_SubScaled.txt
538 B
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README_for_BreastOutput_TradScaled.txt
679 B
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README_for_BreastOvary_HCv2.txt
249 B
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README_for_BreastSubtypeSpecScaleRankDir.txt
166 B
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README_for_computeSAPS.Permute.PValue.R.txt
184 B
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README_for_FinalOutput_Breast.txt
1.48 KB
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README_for_FinalOutput_Ovary.txt
1.48 KB
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README_for_molsigdb.v3.0.entrezForR.txt.txt
124 B
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README_for_Ovary_Angio.txt
280 B
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README_for_Ovary_Global.txt
278 B
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README_for_Ovary_NonAngio.txt
287 B
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README_for_Ovary.Ps.OnPermutedData.RData.txt
230 B
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README_for_Ovary.txt
648 B
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README_for_OvaryOutput_SubScaled.txt
576 B
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README_for_OvaryOutput_TradScaled.txt
615 B
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README_for_OvaryTradScaleRankDir.txt
150 B
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README_for_runSAPSonPermutedData.txt
144 B
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README_for_saps.txt
268 B
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README_for_sapsFigures.txt
112 B
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runSAPSonPermutedData.zip
5.38 KB
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saps.zip
3.91 KB
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sapsFigures.zip
2.05 KB
Abstract
A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that "random" gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets.