This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between '17p loss' and 10 clinical features across 20 patients, no significant finding detected with Q value < 0.25.
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No arm-level cnvs related to clinical features.
Clinical Features |
Time to Death |
AGE | GENDER |
HISTOLOGICAL TYPE |
DISTANT METASTASIS |
LYMPH NODE METASTASIS |
COMPLETENESS OF RESECTION |
NUMBER OF LYMPH NODES |
TUMOR STAGECODE |
NEOPLASM DISEASESTAGE |
||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | t-test | Chi-square test | |
17p loss | 0 (0%) | 17 |
0.317 (1.00) |
0.686 (1.00) |
1 (1.00) |
0.404 (1.00) |
0.509 (1.00) |
0.242 (1.00) |
0.0421 (0.379) |
0.48 (1.00) |
0.798 (1.00) |
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Mutation data file = broad_values_by_arm.mutsig.cluster.txt
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Clinical data file = PAAD-TP.clin.merged.picked.txt
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Number of patients = 20
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Number of significantly arm-level cnvs = 1: '17p loss'
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Number of selected clinical features = 10
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Exclude genes that fewer than K tumors have mutations, K = 3
For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R
For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' function in R
For binary or multi-class clinical features (nominal or ordinal), two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R
For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R
For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.