Correlation between mRNAseq expression and clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1765CZ9
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1765CZ9
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 17276 genes and 3 clinical features across 173 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

- 6 genes correlated to 'Time to Death'.

- PWWP2A|114825 , MYB|4602 , CLINT1|9685 , ADSS|159 , HSDL1|83693 , ...

- 11 genes correlated to 'AGE'.

- GBP2|2634 , PI4K2A|55361 , FBXO32|114907 , C7ORF58|79974 , SLC22A16|85413 , ...

- 17 genes correlated to 'GENDER'.

- PRKY|5616 , XIST|7503 , TSIX|9383 , ZFY|7544 , UTY|7404 , ...

Results
Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature | Statistical test | Significant genes | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
Time to Death | Cox regression test | N=6 | shorter survival | N=1 | longer survival | N=5
AGE | Spearman correlation test | N=11 | older | N=9 | younger | N=2
GENDER | t test | N=17 | male | N=4 | female | N=13

Clinical variable #1: 'Time to Death'

6 genes related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'

Time to Death | Duration (Months) | 0.9-94.1 (median=12)
| censored | N = 57
| death | N = 94
| Significant markers | N = 6
| associated with shorter survival | 1
| associated with longer survival | 5

List of 6 genes significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of 6 genes significantly associated with 'Time to Death' by Cox regression test

| HazardRatio | Wald_P | Q | C_index
--- | --- | --- | --- | ---
PWWP2A|114825 | 0.37 | 6.386e-07 | 0.011 | 0.336
MYB|4602 | 0.54 | 6.724e-07 | 0.012 | 0.343
CLINT1|9685 | 0.32 | 1.4e-06 | 0.024 | 0.368
ADSS|159 | 0.29 | 2.173e-06 | 0.038 | 0.334
HSDL1|83693 | 0.46 | 2.639e-06 | 0.046 | 0.345
PTP4A3|11156 | 1.4 | 2.701e-06 | 0.047 | 0.646

Figure S1. Get High-res Image As an example, this figure shows the association of PWWP2A|114825 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 6.39e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

11 genes related to 'AGE'.

Table S3. Basic characteristics of clinical feature: 'AGE'

AGE | Mean (SD) | 55.27 (16)
| Significant markers | N = 11
| pos. correlated | 9
| neg. correlated | 2

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
GBP2|2634 | 0.4101 | 2.1e-08 | 0.000363
PI4K2A|55361 | 0.406 | 2.98e-08 | 0.000515
FBXO32|114907 | 0.368 | 6.309e-07 | 0.0109
C7ORF58|79974 | 0.372 | 6.373e-07 | 0.011
SLC22A16|85413 | -0.3624 | 9.597e-07 | 0.0166
HK2|3099 | -0.3611 | 1.059e-06 | 0.0183
PPARD|5467 | 0.3581 | 1.314e-06 | 0.0227
TMEM117|84216 | 0.3708 | 1.616e-06 | 0.0279
STK16|8576 | 0.3544 | 1.716e-06 | 0.0296
KLRF1|51348 | 0.3514 | 2.129e-06 | 0.0368

Figure S2. Get High-res Image As an example, this figure shows the association of GBP2|2634 to 'AGE'. P value = 2.1e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

17 genes related to 'GENDER'.

Table S5. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 80
| MALE | 93
| Significant markers | N = 17
| Higher in MALE | 4
| Higher in FEMALE | 13

List of top 10 genes differentially expressed by 'GENDER'

Table S6. Get Full Table List of top 10 genes differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
PRKY|5616 | 52.08 | 2.604e-93 | 4.5e-89 | 1
XIST|7503 | -44.33 | 3.364e-82 | 5.81e-78 | 0.9995
TSIX|9383 | -33.18 | 2.43e-66 | 4.19e-62 | 0.9998
ZFY|7544 | 56.47 | 1.64e-49 | 2.83e-45 | 1
UTY|7404 | 68.34 | 1.127e-29 | 1.95e-25 | 1
PRKX|5613 | -12.96 | 3.466e-27 | 5.98e-23 | 0.9194
KDM5C|8242 | -10.21 | 7.258e-19 | 1.25e-14 | 0.8719
NCRNA00183|554203 | -9.4 | 7.232e-17 | 1.25e-12 | 0.8442
ZFX|7543 | -9.25 | 2.677e-16 | 4.62e-12 | 0.8653
ZRSR2|8233 | -9.07 | 1.408e-15 | 2.43e-11 | 0.8535

Figure S3. Get High-res Image As an example, this figure shows the association of PRKY|5616 to 'GENDER'. P value = 2.6e-93 with T-test analysis.

Methods & Data
Input

- Expresson data file = LAML-TB.uncv2.mRNAseq_RSEM_normalized_log2.txt

- Clinical data file = LAML-TB.merged_data.txt

- Number of patients = 173

- Number of genes = 17276

- Number of clinical features = 3

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.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] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] 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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