This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.
Testing the association between copy number variation 9 focal events and 3 clinical features across 16 patients, no significant finding detected with Q value < 0.25.
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No focal cnvs related to clinical features.
Clinical Features |
Time to Death |
AGE | GENDER | ||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | |
AMP PEAK 1(2P16 1) MUTATION ANALYSIS | 6 (38%) | 10 |
0.0806 (1.00) |
0.801 (1.00) |
0.633 (1.00) |
DEL PEAK 2(1Q43) MUTATION ANALYSIS | 3 (19%) | 13 |
0.683 (1.00) |
0.944 (1.00) |
0.213 (1.00) |
DEL PEAK 3(2Q22 3) MUTATION ANALYSIS | 4 (25%) | 12 |
0.698 (1.00) |
0.686 (1.00) |
0.0885 (1.00) |
DEL PEAK 4(6Q14 1) MUTATION ANALYSIS | 3 (19%) | 13 |
0.333 (1.00) |
0.163 (1.00) |
0.55 (1.00) |
DEL PEAK 5(6Q23 3) MUTATION ANALYSIS | 3 (19%) | 13 |
0.912 (1.00) |
0.0369 (0.995) |
0.0625 (1.00) |
DEL PEAK 7(9P21 3) MUTATION ANALYSIS | 4 (25%) | 12 |
0.333 (1.00) |
0.851 (1.00) |
1 (1.00) |
DEL PEAK 9(13Q33 3) MUTATION ANALYSIS | 3 (19%) | 13 |
0.388 (1.00) |
0.224 (1.00) |
0.213 (1.00) |
DEL PEAK 10(15Q15 1) MUTATION ANALYSIS | 4 (25%) | 12 |
0.778 (1.00) |
0.953 (1.00) |
0.585 (1.00) |
DEL PEAK 11(16P13 13) MUTATION ANALYSIS | 3 (19%) | 13 |
0.683 (1.00) |
0.901 (1.00) |
0.213 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = DLBC-TP.clin.merged.picked.txt
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Number of patients = 16
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Number of significantly focal cnvs = 9
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Number of selected clinical features = 3
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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 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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.