This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features. The input file "PCPG-TP.rppa.txt" is generated in the pipeline RPPA_AnnotateWithGene in the stddata run.
Testing the association between 192 genes and 7 clinical features across 79 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.
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1 gene correlated to 'YEARS_TO_BIRTH'.
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EEF2K|EEF2K
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30 genes correlated to 'TUMOR_TISSUE_SITE'.
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ANXA1|ANNEXIN-1 , CAV1|CAVEOLIN-1 , BAK1|BAK , MYH9|MYOSIN-IIA_PS1943 , PREX1|PREX1 , ...
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2 genes correlated to 'GENDER'.
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RPS6KA1|P90RSK_PT359_S363 , BIRC2 |CIAP
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1 gene correlated to 'NUMBER_OF_LYMPH_NODES'.
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RPS6|S6_PS240_S244
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1 gene correlated to 'RACE'.
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GSK3A GSK3B|GSK3-ALPHA-BETA
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No genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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YEARS_TO_BIRTH | Spearman correlation test | N=1 | older | N=0 | younger | N=1 |
TUMOR_TISSUE_SITE | Wilcoxon test | N=30 | extra-adrenal site | N=30 | adrenal gland | N=0 |
GENDER | Wilcoxon test | N=2 | male | N=2 | female | N=0 |
KARNOFSKY_PERFORMANCE_SCORE | Wilcoxon test | N=0 | ||||
HISTOLOGICAL_TYPE | Kruskal-Wallis test | N=0 | ||||
NUMBER_OF_LYMPH_NODES | Spearman correlation test | N=1 | higher number_of_lymph_nodes | N=0 | lower number_of_lymph_nodes | N=1 |
RACE | Kruskal-Wallis test | N=1 |
YEARS_TO_BIRTH | Mean (SD) | 47.77 (15) |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
EEF2K|EEF2K | -0.381 | 0.0005318 | 0.102 |
TUMOR_TISSUE_SITE | Labels | N |
ADRENAL GLAND | 67 | |
EXTRA-ADRENAL SITE | 12 | |
Significant markers | N = 30 | |
Higher in EXTRA-ADRENAL SITE | 30 | |
Higher in ADRENAL GLAND | 0 |
W(pos if higher in 'EXTRA-ADRENAL SITE') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
ANXA1|ANNEXIN-1 | 657 | 0.0005086 | 0.0548 | 0.8172 |
CAV1|CAVEOLIN-1 | 649 | 0.0007601 | 0.0548 | 0.8072 |
BAK1|BAK | 160 | 0.0009715 | 0.0548 | 0.801 |
MYH9|MYOSIN-IIA_PS1943 | 638 | 0.001297 | 0.0548 | 0.7935 |
PREX1|PREX1 | 636 | 0.001426 | 0.0548 | 0.791 |
YAP1|YAP | 629 | 0.001976 | 0.0632 | 0.7823 |
SMAD3|SMAD3 | 183 | 0.002841 | 0.0682 | 0.7724 |
SYK|SYK | 621 | 0.002841 | 0.0682 | 0.7724 |
STAT3|STAT3_PY705 | 613 | 0.004038 | 0.0861 | 0.7624 |
NDRG1|NDRG1_PT346 | 605 | 0.005676 | 0.103 | 0.7525 |
GENDER | Labels | N |
FEMALE | 40 | |
MALE | 39 | |
Significant markers | N = 2 | |
Higher in MALE | 2 | |
Higher in FEMALE | 0 |
W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
RPS6KA1|P90RSK_PT359_S363 | 416 | 0.0003647 | 0.07 | 0.7333 |
BIRC2 |CIAP | 1097 | 0.001912 | 0.184 | 0.7032 |
No gene related to 'KARNOFSKY_PERFORMANCE_SCORE'.
KARNOFSKY_PERFORMANCE_SCORE | Labels | N |
class100 | 29 | |
class90 | 4 | |
Significant markers | N = 0 |
HISTOLOGICAL_TYPE | Labels | N |
PARAGANGLIOMA | 5 | |
PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA | 6 | |
PHEOCHROMOCYTOMA | 68 | |
Significant markers | N = 0 |
NUMBER_OF_LYMPH_NODES | Mean (SD) | 1.6 (4) |
Value | N | |
0 | 6 | |
1 | 3 | |
13 | 1 | |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
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RPS6|S6_PS240_S244 | -0.8739 | 0.0009485 | 0.182 |
RACE | Labels | N |
ASIAN | 4 | |
BLACK OR AFRICAN AMERICAN | 9 | |
WHITE | 65 | |
Significant markers | N = 1 |
kruskal_wallis_P | Q | |
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GSK3A GSK3B|GSK3-ALPHA-BETA | 0.0001786 | 0.0343 |
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Expresson data file = PCPG-TP.rppa.txt
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Clinical data file = PCPG-TP.merged_data.txt
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Number of patients = 79
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Number of genes = 192
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Number of clinical features = 7
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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.
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There are also useful links about clinical features.
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Survival time data
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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.
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if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'
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if 'vital_status'==0(alive),
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if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'
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if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.
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if 'vital_status'==NA,excludes this case in survival analysis and report the case.
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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 .
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This analysis excluded clinical variables that has only NA values.
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 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.
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.