This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.
Testing the association between 415 miRs and 7 clinical features across 453 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one miRs.
-
8 miRs correlated to 'AGE'.
-
HSA-MIR-1269 , HSA-MIR-30D , HSA-MIR-767 , HSA-MIR-1234 , HSA-MIR-135A-1 , ...
-
1 miR correlated to 'RACE'.
-
HSA-MIR-1304
-
No miRs correlated to 'Time to Death', 'PRIMARY.SITE.OF.DISEASE', 'KARNOFSKY.PERFORMANCE.SCORE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'ETHNICITY'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant miRs | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=8 | older | N=4 | younger | N=4 |
PRIMARY SITE OF DISEASE | Kruskal-Wallis test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | Wilcoxon test | N=0 | ||||
RACE | Kruskal-Wallis test | N=1 | ||||
ETHNICITY | Wilcoxon test | N=0 |
Time to Death | Duration (Months) | 0.3-180.2 (median=30.1) |
censored | N = 198 | |
death | N = 252 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 59.76 (12) |
Significant markers | N = 8 | |
pos. correlated | 4 | |
neg. correlated | 4 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-1269 | 0.3263 | 1.7e-12 | 7.06e-10 |
HSA-MIR-30D | -0.2603 | 2.52e-08 | 1.04e-05 |
HSA-MIR-767 | 0.1997 | 5.628e-05 | 0.0232 |
HSA-MIR-1234 | -0.1859 | 0.0001418 | 0.0584 |
HSA-MIR-135A-1 | 0.1857 | 0.0001502 | 0.0617 |
HSA-MIR-222 | -0.1671 | 0.0003988 | 0.164 |
HSA-MIR-1908 | 0.1903 | 0.0005195 | 0.212 |
HSA-MIR-190 | -0.2191 | 0.0005815 | 0.237 |
PRIMARY.SITE.OF.DISEASE | Labels | N |
OMENTUM | 2 | |
OVARY | 450 | |
PERITONEUM OVARY | 1 | |
Significant markers | N = 0 |
No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 75.31 (13) |
Score | N | |
40 | 2 | |
60 | 17 | |
80 | 39 | |
100 | 6 | |
Significant markers | N = 0 |
No miR related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 3 | |
YES | 450 | |
Significant markers | N = 0 |
RACE | Labels | N |
AMERICAN INDIAN OR ALASKA NATIVE | 2 | |
ASIAN | 15 | |
BLACK OR AFRICAN AMERICAN | 21 | |
WHITE | 400 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-1304 | 0.0004978 | 0.207 |
-
Expresson data file = OV-TP.miRseq_RPKM_log2.txt
-
Clinical data file = OV-TP.merged_data.txt
-
Number of patients = 453
-
Number of miRs = 415
-
Number of clinical features = 7
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
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
For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R
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
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.