This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.
Testing the association between 189 genes and 6 clinical features across 222 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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11 genes correlated to 'Time to Death'.
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ANXA1|ANNEXIN-1-M-E , MAPK14|P38_MAPK-R-V , SMAD1|SMAD1-R-V , SERPINE1|PAI-1-M-E , CHEK2|CHK2_PT68-R-E , ...
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3 genes correlated to 'AGE'.
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IGFBP2|IGFBP2-R-V , SERPINE1|PAI-1-M-E , PEA15|PEA15-R-V
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1 gene correlated to 'GENDER'.
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RAB11A RAB11B|RAB11-R-E
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37 genes correlated to 'HISTOLOGICAL.TYPE'.
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SYK|SYK-M-V , MAPK14|P38_MAPK-R-V , ANXA1|ANNEXIN-1-M-E , BAX|BAX-R-V , AR|AR-R-V , ...
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No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=11 | shorter survival | N=7 | longer survival | N=4 |
AGE | Spearman correlation test | N=3 | older | N=2 | younger | N=1 |
GENDER | t test | N=1 | male | N=1 | female | N=0 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=37 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 |
Time to Death | Duration (Months) | 0-211.2 (median=14.7) |
censored | N = 170 | |
death | N = 52 | |
Significant markers | N = 11 | |
associated with shorter survival | 7 | |
associated with longer survival | 4 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
ANXA1|ANNEXIN-1-M-E | 2.3 | 2.243e-08 | 4.2e-06 | 0.733 |
MAPK14|P38_MAPK-R-V | 4.2 | 2.964e-06 | 0.00056 | 0.724 |
SMAD1|SMAD1-R-V | 6.3 | 2.79e-05 | 0.0052 | 0.678 |
SERPINE1|PAI-1-M-E | 2.3 | 2.905e-05 | 0.0054 | 0.684 |
CHEK2|CHK2_PT68-R-E | 0.01 | 3.39e-05 | 0.0063 | 0.291 |
INPP4B|INPP4B-G-E | 0.11 | 3.722e-05 | 0.0068 | 0.327 |
PGR|PR-R-V | 0 | 4.28e-05 | 0.0078 | 0.281 |
BAX|BAX-R-V | 5.7 | 5.726e-05 | 0.01 | 0.707 |
PXN|PAXILLIN-R-C | 5.4 | 7.157e-05 | 0.013 | 0.676 |
SCD1|SCD1-M-V | 0.04 | 0.0002226 | 0.04 | 0.268 |
AGE | Mean (SD) | 42.74 (13) |
Significant markers | N = 3 | |
pos. correlated | 2 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
IGFBP2|IGFBP2-R-V | 0.3298 | 4.949e-07 | 9.35e-05 |
SERPINE1|PAI-1-M-E | 0.312 | 2.118e-06 | 0.000398 |
PEA15|PEA15-R-V | -0.3081 | 2.885e-06 | 0.000539 |
GENDER | Labels | N |
FEMALE | 100 | |
MALE | 122 | |
Significant markers | N = 1 | |
Higher in MALE | 1 | |
Higher in FEMALE | 0 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
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RAB11A RAB11B|RAB11-R-E | 4.11 | 5.719e-05 | 0.0108 | 0.6577 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 88.02 (11) |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
ASTROCYTOMA | 66 | |
OLIGOASTROCYTOMA | 64 | |
OLIGODENDROGLIOMA | 91 | |
Significant markers | N = 37 |
ANOVA_P | Q | |
---|---|---|
SYK|SYK-M-V | 2.575e-13 | 4.87e-11 |
MAPK14|P38_MAPK-R-V | 1.834e-09 | 3.45e-07 |
ANXA1|ANNEXIN-1-M-E | 2.271e-09 | 4.25e-07 |
BAX|BAX-R-V | 1.413e-08 | 2.63e-06 |
AR|AR-R-V | 7.268e-08 | 1.34e-05 |
PTEN|PTEN-R-V | 2.313e-07 | 4.26e-05 |
YAP1|YAP_PS127-R-E | 2.398e-07 | 4.39e-05 |
RPS6|S6_PS235_S236-R-V | 1.542e-06 | 0.000281 |
ERBB3|HER3_PY1289-R-C | 5.863e-06 | 0.00106 |
ERBB2|HER2_PY1248-R-C | 1.282e-05 | 0.00231 |
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Expresson data file = LGG-TP.rppa.txt
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Clinical data file = LGG-TP.clin.merged.picked.txt
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Number of patients = 222
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Number of genes = 189
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Number of clinical features = 6
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 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 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 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.