This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 17733 genes and 6 clinical features across 34 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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1 gene correlated to 'NEOPLASM.DISEASESTAGE'.
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CBFB|865
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3 genes correlated to 'PATHOLOGY.T.STAGE'.
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CBFB|865 , CHTF18|63922 , POLD1|5424
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18 genes correlated to 'PATHOLOGY.N.STAGE'.
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FKTN|2218 , FAM105B|90268 , GTPBP10|85865 , RNF31|55072 , C3ORF19|51244 , ...
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12 genes correlated to 'GENDER'.
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XIST|7503 , TSIX|9383 , RPS4Y1|6192 , PRKY|5616 , DDX3Y|8653 , ...
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No genes correlated to 'Time to Death', and 'AGE'.
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=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=1 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=3 | higher stage | N=3 | lower stage | N=0 |
PATHOLOGY N STAGE | t test | N=18 | class1 | N=13 | class0 | N=5 |
GENDER | t test | N=12 | male | N=10 | female | N=2 |
Time to Death | Duration (Months) | 6.9-121.2 (median=29.8) |
censored | N = 26 | |
death | N = 8 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 50.82 (14) |
Significant markers | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 4 | |
STAGE II | 13 | |
STAGE III | 4 | |
STAGE IV | 8 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
CBFB|865 | 2.635e-06 | 0.0467 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.55 (1.1) |
N | ||
1 | 4 | |
2 | 14 | |
3 | 2 | |
4 | 9 | |
Significant markers | N = 3 | |
pos. correlated | 3 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
CBFB|865 | 0.8406 | 1.153e-08 | 0.000204 |
CHTF18|63922 | 0.7677 | 1.174e-06 | 0.0208 |
POLD1|5424 | 0.7543 | 2.285e-06 | 0.0405 |
PATHOLOGY.N.STAGE | Labels | N |
class0 | 26 | |
class1 | 4 | |
Significant markers | N = 18 | |
Higher in class1 | 13 | |
Higher in class0 | 5 |
T(pos if higher in 'class1') | ttestP | Q | AUC | |
---|---|---|---|---|
FKTN|2218 | 7.88 | 1.39e-08 | 0.000233 | 0.9038 |
FAM105B|90268 | 8.07 | 3.705e-08 | 0.000622 | 0.9904 |
GTPBP10|85865 | 7.23 | 8.11e-08 | 0.00136 | 0.9423 |
RNF31|55072 | 8.24 | 2.022e-07 | 0.00339 | 0.9904 |
C3ORF19|51244 | -6.35 | 7.393e-07 | 0.0124 | 0.9423 |
JKAMP|51528 | 7.17 | 7.728e-07 | 0.013 | 0.9519 |
C6ORF108|10591 | -6.24 | 1.002e-06 | 0.0168 | 0.8654 |
UTP15|84135 | 6.49 | 1.002e-06 | 0.0168 | 0.9231 |
ABLIM2|84448 | 6.32 | 1.055e-06 | 0.0177 | 0.9712 |
MAGED4B|81557 | 7.26 | 1.183e-06 | 0.0198 | 0.9583 |
GENDER | Labels | N |
FEMALE | 17 | |
MALE | 17 | |
Significant markers | N = 12 | |
Higher in MALE | 10 | |
Higher in FEMALE | 2 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
XIST|7503 | -16.4 | 4.761e-17 | 8.43e-13 | 1 |
TSIX|9383 | -17.3 | 2.678e-15 | 4.74e-11 | 1 |
RPS4Y1|6192 | 14.02 | 2.415e-13 | 4.27e-09 | 1 |
PRKY|5616 | 14.84 | 1.211e-12 | 2.14e-08 | 1 |
DDX3Y|8653 | 14.44 | 1.875e-12 | 3.32e-08 | 1 |
KDM5D|8284 | 14.32 | 7.014e-12 | 1.24e-07 | 1 |
EIF1AY|9086 | 13.17 | 4.278e-10 | 7.57e-06 | 1 |
USP9Y|8287 | 12.19 | 7.551e-10 | 1.34e-05 | 1 |
ZFY|7544 | 9.64 | 3.652e-09 | 6.46e-05 | 1 |
TTTY15|64595 | 10.04 | 1.008e-08 | 0.000178 | 1 |
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Expresson data file = ACC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = ACC-TP.merged_data.txt
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Number of patients = 34
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Number of genes = 17733
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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 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.