Correlation between copy number variation genes (focal events) and molecular subtypes
Kidney Chromophobe (Primary solid tumor)
17 October 2014 | analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1GF0SDS
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between copy number variation genes (focal events) and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1GF0SDS
Overview
Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and molecular subtypes.

Summary

Testing the association between copy number variation 2 focal events and 8 molecular subtypes across 66 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

- amp_8q11.23 cnv correlated to 'MRNASEQ_CNMF'.

- amp_15q22.31 cnv correlated to 'MRNASEQ_CNMF'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 2 focal events and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 2 significant findings detected.

|  | Clinical Features | CN CNMF | METHLYATION CNMF | MRNASEQ CNMF | MRNASEQ CHIERARCHICAL | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL | MIRSEQ MATURE CNMF | MIRSEQ MATURE CHIERARCHICAL
| nCNV (%) | nWild-Type | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
amp 8q11 23 | 19 (29%) | 47 | 0.321 (1.00) | 0.625 (1.00) | 0.00283 (0.0453) | 0.0245 (0.343) | 1 (1.00) | 0.228 (1.00) | 0.363 (1.00) | 0.118 (1.00)
amp 15q22 31 | 23 (35%) | 43 | 0.0534 (0.694) | 0.582 (1.00) | 0.00627 (0.094) | 0.182 (1.00) | 0.526 (1.00) | 0.301 (1.00) | 0.554 (1.00) | 0.365 (1.00)

'amp_8q11.23' versus 'MRNASEQ_CNMF'

P value = 0.00283 (Fisher's exact test), Q value = 0.045

Table S1. Gene #1: 'amp_8q11.23' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 19 | 22 | 15 | 10
AMP PEAK 1(8Q11.23) MUTATED | 3 | 13 | 2 | 1
AMP PEAK 1(8Q11.23) WILD-TYPE | 16 | 9 | 13 | 9

Figure S1. Get High-res Image Gene #1: 'amp_8q11.23' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'amp_15q22.31' versus 'MRNASEQ_CNMF'

P value = 0.00627 (Fisher's exact test), Q value = 0.094

Table S2. Gene #2: 'amp_15q22.31' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 19 | 22 | 15 | 10
AMP PEAK 2(15Q22.31) MUTATED | 5 | 13 | 5 | 0
AMP PEAK 2(15Q22.31) WILD-TYPE | 14 | 9 | 10 | 10

Figure S2. Get High-res Image Gene #2: 'amp_15q22.31' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

Methods & Data
Input

- Copy number data file = transformed.cor.cli.txt

- Molecular subtype file = KICH-TP.transferedmergedcluster.txt

- Number of patients = 66

- Number of significantly focal cnvs = 2

- Number of molecular subtypes = 8

- Exclude genes that fewer than K tumors have alterations, K = 3

Fisher's exact test

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

Q value calculation

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.

Download Results

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[2] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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