Correlation between miRseq expression and clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1S46QZ3
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1S46QZ3
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 540 miRs and 6 clinical features across 46 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one miRs.

- 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

- HSA-MIR-376C , HSA-MIR-577 , HSA-MIR-584 , HSA-MIR-134 , HSA-MIR-409 , ...

- 30 miRs correlated to 'ETHNICITY'.

- HSA-MIR-3194 , HSA-MIR-497 , HSA-MIR-3136 , HSA-MIR-101-2 , HSA-MIR-23B , ...

- No miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'YEARS_TO_BIRTH', 'GENDER', and 'RACE'.

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=0 |  |  |  |
YEARS_TO_BIRTH | Spearman correlation test | N=0 |  |  |  |
GENDER | Wilcoxon test | N=0 |  |  |  |
HISTOLOGICAL_TYPE | Kruskal-Wallis test | N=30 |  |  |  |
RACE | Kruskal-Wallis test | N=0 |  |  |  |
ETHNICITY | Wilcoxon test | N=30 | not hispanic or latino | N=30 | hispanic or latino | N=0

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No miR 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) | 0-211.2 (median=27.3)
| censored | N = 36
| death | N = 9
| Significant markers | N = 0

Clinical variable #2: 'YEARS_TO_BIRTH'

No miR related to 'YEARS_TO_BIRTH'.

Table S2. Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH | Mean (SD) | 56.3 (14)
| Significant markers | N = 0

Clinical variable #3: 'GENDER'

No miR related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 25
| MALE | 21
| Significant markers | N = 0

Clinical variable #4: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

Table S4. Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE | Labels | N
| DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL) | 39
| PRIMARY DLBCL OF THE CNS | 3
| PRIMARY MEDIASTINAL (THYMIC) DLBCL | 4
| Significant markers | N = 30

List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Table S5. Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

| kruskal_wallis_P | Q
--- | --- | ---
HSA-MIR-376C | 0.001577 | 0.19
HSA-MIR-577 | 0.001906 | 0.19
HSA-MIR-584 | 0.002525 | 0.19
HSA-MIR-134 | 0.00282 | 0.19
HSA-MIR-409 | 0.003025 | 0.19
HSA-MIR-136 | 0.003104 | 0.19
HSA-MIR-125B-2 | 0.003111 | 0.19
HSA-MIR-127 | 0.003709 | 0.19
HSA-MIR-758 | 0.004413 | 0.19
HSA-LET-7D | 0.004626 | 0.19

Clinical variable #5: 'RACE'

No miR related to 'RACE'.

Table S6. Basic characteristics of clinical feature: 'RACE'

RACE | Labels | N
| ASIAN | 18
| BLACK OR AFRICAN AMERICAN | 1
| WHITE | 27
| Significant markers | N = 0

Clinical variable #6: 'ETHNICITY'

30 miRs related to 'ETHNICITY'.

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

ETHNICITY | Labels | N
| HISPANIC OR LATINO | 12
| NOT HISPANIC OR LATINO | 34
| Significant markers | N = 30
| Higher in NOT HISPANIC OR LATINO | 30
| Higher in HISPANIC OR LATINO | 0

List of top 10 miRs differentially expressed by 'ETHNICITY'

Table S8. Get Full Table List of top 10 miRs differentially expressed by 'ETHNICITY'

| W(pos if higher in 'NOT HISPANIC OR LATINO') | wilcoxontestP | Q | AUC
--- | --- | --- | --- | ---
HSA-MIR-3194 | c("301", "0.0007937") | c("301", "0.0007937") | 0.142 | 0.8361
HSA-MIR-497 | c("338", "0.000839") | c("338", "0.000839") | 0.142 | 0.8284
HSA-MIR-3136 | c("326", "0.001066") | c("326", "0.001066") | 0.142 | 0.8232
HSA-MIR-101-2 | c("76", "0.001425") | c("76", "0.001425") | 0.142 | 0.8137
HSA-MIR-23B | c("331", "0.001554") | c("331", "0.001554") | 0.142 | 0.8113
HSA-MIR-146B | c("79", "0.001843") | c("79", "0.001843") | 0.142 | 0.8064
HSA-MIR-1976 | c("329", "0.001843") | c("329", "0.001843") | 0.142 | 0.8064
HSA-MIR-1306 | c("324", "0.002796") | c("324", "0.002796") | 0.166 | 0.7941
HSA-MIR-675 | c("314", "0.003032") | c("314", "0.003032") | 0.166 | 0.7929
HSA-MIR-324 | c("320", "0.003861") | c("320", "0.003861") | 0.166 | 0.7843

Methods & Data
Input

- Expresson data file = DLBC-TP.miRseq_RPKM_log2.txt

- Clinical data file = DLBC-TP.merged_data.txt

- Number of patients = 46

- Number of miRs = 540

- Number of clinical features = 6

Selected clinical features

- For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

- 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, 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

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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