Correlation between miRseq expression and clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
28 January 2016 | analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C19886FZ
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C19886FZ
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features. The input file " LAML-TB.miRseq_RPKM_log2.txt " is generated in the pipeline miRseq_Preprocess in the stddata run.

Summary

Testing the association between 354 miRs and 4 clinical features across 188 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one miRs.

- 30 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- HSA-MIR-133A-2 , HSA-MIR-20B , HSA-MIR-532 , HSA-MIR-362 , HSA-MIR-500 , ...

- 30 miRs correlated to 'YEARS_TO_BIRTH'.

- HSA-MIR-598 , HSA-MIR-766 , HSA-MIR-29B-1 , HSA-MIR-20B , HSA-MIR-363 , ...

- 30 miRs correlated to 'GENDER'.

- HSA-MIR-505 , HSA-MIR-107 , HSA-MIR-1226 , HSA-MIR-651 , HSA-MIR-186 , ...

- 1 miR correlated to 'RACE'.

- HSA-MIR-1304

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 miRs that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature | Statistical test | Significant miRs | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
DAYS_TO_DEATH_OR_LAST_FUP | Cox regression test | N=30 |  | N=NA |  | N=NA
YEARS_TO_BIRTH | Spearman correlation test | N=30 | older | N=18 | younger | N=12
GENDER | Wilcoxon test | N=30 | male | N=30 | female | N=0
RACE | Kruskal-Wallis test | N=1 |  |  |  |

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 miRs related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1. Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP | Duration (Months) | 0-94.1 (median=12)
| censored | N = 64
| death | N = 123
| Significant markers | N = 30
| associated with shorter survival | NA
| associated with longer survival | NA

List of top 10 miRs differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2. Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

| logrank_P | Q | C_index
--- | --- | --- | ---
HSA-MIR-133A-2 | 8.95e-06 | 0.0011 | 0.41
HSA-MIR-20B | 1.06e-05 | 0.0011 | 0.621
HSA-MIR-532 | 1.31e-05 | 0.0011 | 0.66
HSA-MIR-362 | 1.35e-05 | 0.0011 | 0.653
HSA-MIR-500 | 1.5e-05 | 0.0011 | 0.643
HSA-MIR-502 | 3e-05 | 0.0018 | 0.637
HSA-MIR-190B | 0.000147 | 0.0074 | 0.545
HSA-MIR-501 | 0.000235 | 0.0095 | 0.648
HSA-MIR-181C | 0.000249 | 0.0095 | 0.362
HSA-MIR-196B | 0.000269 | 0.0095 | 0.578

Clinical variable #2: 'YEARS_TO_BIRTH'

30 miRs related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH | Mean (SD) | 54.9 (16)
| Significant markers | N = 30
| pos. correlated | 18
| neg. correlated | 12

List of top 10 miRs differentially expressed by 'YEARS_TO_BIRTH'

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

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-598 | 0.3153 | 1.721e-05 | 0.00513
HSA-MIR-766 | -0.2942 | 4.174e-05 | 0.00513
HSA-MIR-29B-1 | 0.2923 | 4.688e-05 | 0.00513
HSA-MIR-20B | 0.2889 | 5.795e-05 | 0.00513
HSA-MIR-363 | 0.277 | 0.0001188 | 0.00841
HSA-MIR-29B-2 | 0.271 | 0.0001688 | 0.00996
HSA-MIR-181C | -0.268 | 0.0002011 | 0.0102
HSA-MIR-532 | 0.2644 | 0.0002459 | 0.0109
HSA-MIR-22 | 0.2548 | 0.0004167 | 0.0164
HSA-MIR-500 | 0.2481 | 0.0005977 | 0.0209

Clinical variable #3: 'GENDER'

30 miRs related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 87
| MALE | 101
| Significant markers | N = 30
| Higher in MALE | 30
| Higher in FEMALE | 0

List of top 10 miRs differentially expressed by 'GENDER'

Table S6. Get Full Table List of top 10 miRs differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

| W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC
--- | --- | --- | --- | ---
HSA-MIR-505 | 5930 | 3.646e-05 | 0.00945 | 0.6749
HSA-MIR-107 | 5897 | 5.342e-05 | 0.00945 | 0.6711
HSA-MIR-1226 | 4549 | 0.0002545 | 0.03 | 0.6651
HSA-MIR-651 | 2869 | 0.0004099 | 0.0363 | 0.652
HSA-MIR-186 | 5657 | 0.0006862 | 0.0486 | 0.6438
HSA-MIR-455 | 5546 | 0.001115 | 0.0621 | 0.6385
HSA-MIR-877 | 4610 | 0.001276 | 0.0621 | 0.6438
HSA-MIR-320B-2 | 5477 | 0.001405 | 0.0621 | 0.6359
HSA-MIR-301A | 5566 | 0.00163 | 0.0621 | 0.6334
HSA-MIR-17 | 5557 | 0.001771 | 0.0621 | 0.6324

Clinical variable #4: 'RACE'

One miR related to 'RACE'.

Table S7. Basic characteristics of clinical feature: 'RACE'

RACE | Labels | N
| ASIAN | 2
| BLACK OR AFRICAN AMERICAN | 13
| WHITE | 171
| Significant markers | N = 1

List of one miR differentially expressed by 'RACE'

Table S8. Get Full Table List of one miR differentially expressed by 'RACE'

| kruskal_wallis_P | Q
--- | --- | ---
HSA-MIR-1304 | 0.0004229 | 0.15

Methods & Data
Input

- Expresson data file = LAML-TB.miRseq_RPKM_log2.txt

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

- Number of patients = 188

- Number of miRs = 354

- Number of clinical features = 4

Selected clinical features

- Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

- There are also useful links about clinical features.

- CDE Browser

- Data dictionary for clinical data

- NCI wiki - Biospecimen and Clinical XSD Files Specification

- Survival time data

- Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

- if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

- if 'vital_status'==0(alive),

- if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

- if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

- if 'vital_status'==NA,excludes this case in survival analysis and report the case.

- cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

- This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[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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