This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.
Testing the association between 18555 genes and 5 clinical features across 261 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one genes.
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1 gene correlated to 'Time to Death'.
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KRTCAP3|200634
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129 genes correlated to 'AGE'.
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STS|412 , LRP6|4040 , C8ORF55|51337 , EIF4E3|317649 , C12ORF4|57102 , ...
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No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', 'RACE', and 'ETHNICITY'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=1 | shorter survival | N=0 | longer survival | N=1 |
AGE | Spearman correlation test | N=129 | older | N=77 | younger | N=52 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
RACE | Kruskal-Wallis test | N=0 | ||||
ETHNICITY | Wilcoxon test | N=0 |
Time to Death | Duration (Months) | 0.3-180.2 (median=28.2) |
censored | N = 112 | |
death | N = 147 | |
Significant markers | N = 1 | |
associated with shorter survival | 0 | |
associated with longer survival | 1 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
KRTCAP3|200634 | 0.68 | 2.131e-06 | 0.04 | 0.403 |
AGE | Mean (SD) | 58.96 (11) |
Significant markers | N = 129 | |
pos. correlated | 77 | |
neg. correlated | 52 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
STS|412 | -0.3934 | 7.899e-11 | 1.47e-06 |
LRP6|4040 | 0.3678 | 1.483e-09 | 2.75e-05 |
C8ORF55|51337 | -0.3588 | 3.909e-09 | 7.25e-05 |
EIF4E3|317649 | -0.3555 | 5.541e-09 | 0.000103 |
C12ORF4|57102 | 0.3551 | 5.776e-09 | 0.000107 |
APPL2|55198 | 0.3478 | 1.242e-08 | 0.00023 |
PDHA1|5160 | -0.3456 | 1.551e-08 | 0.000288 |
GREB1|9687 | -0.3446 | 1.716e-08 | 0.000318 |
CLSTN3|9746 | 0.3431 | 1.993e-08 | 0.00037 |
ADAM15|8751 | -0.3411 | 2.442e-08 | 0.000453 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 74.29 (12) |
Score | N | |
60 | 5 | |
80 | 8 | |
100 | 1 | |
Significant markers | N = 0 |
RACE | Labels | N |
AMERICAN INDIAN OR ALASKA NATIVE | 1 | |
ASIAN | 12 | |
BLACK OR AFRICAN AMERICAN | 16 | |
NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER | 1 | |
WHITE | 222 | |
Significant markers | N = 0 |
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Expresson data file = OV-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = OV-TP.merged_data.txt
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Number of patients = 261
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Number of genes = 18555
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Number of clinical features = 5
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.