This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.
Testing the association between 17276 genes and 4 clinical features across 173 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.
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24 genes correlated to 'Time to Death'.
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PWWP2A|114825 , MYB|4602 , CLINT1|9685 , ADSS|159 , HSDL1|83693 , ...
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46 genes correlated to 'AGE'.
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GBP2|2634 , PI4K2A|55361 , FBXO32|114907 , C7ORF58|79974 , SLC22A16|85413 , ...
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3 genes correlated to 'GENDER'.
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NCRNA00183|554203 , HDHD1A|8226 , CD24|100133941
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No genes correlated to 'RACE'
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=24 | shorter survival | N=8 | longer survival | N=16 |
AGE | Spearman correlation test | N=46 | older | N=35 | younger | N=11 |
GENDER | Wilcoxon test | N=3 | male | N=3 | female | N=0 |
RACE | Kruskal-Wallis test | N=0 |
Time to Death | Duration (Months) | 0.9-94.1 (median=12) |
censored | N = 57 | |
death | N = 94 | |
Significant markers | N = 24 | |
associated with shorter survival | 8 | |
associated with longer survival | 16 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
PWWP2A|114825 | 0.37 | 6.386e-07 | 0.011 | 0.336 |
MYB|4602 | 0.54 | 6.724e-07 | 0.012 | 0.343 |
CLINT1|9685 | 0.32 | 1.4e-06 | 0.024 | 0.368 |
ADSS|159 | 0.29 | 2.173e-06 | 0.038 | 0.334 |
HSDL1|83693 | 0.46 | 2.639e-06 | 0.046 | 0.345 |
PTP4A3|11156 | 1.4 | 2.701e-06 | 0.047 | 0.646 |
GMCL1|64395 | 0.45 | 4.064e-06 | 0.07 | 0.39 |
C2ORF67|151050 | 0.55 | 4.498e-06 | 0.078 | 0.338 |
LOC100130264|100130264 | 0.78 | 4.63e-06 | 0.08 | 0.343 |
IL2RA|3559 | 1.23 | 4.705e-06 | 0.081 | 0.633 |
AGE | Mean (SD) | 55.27 (16) |
Significant markers | N = 46 | |
pos. correlated | 35 | |
neg. correlated | 11 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
GBP2|2634 | 0.4101 | 2.1e-08 | 0.000363 |
PI4K2A|55361 | 0.406 | 2.98e-08 | 0.000515 |
FBXO32|114907 | 0.368 | 6.309e-07 | 0.0109 |
C7ORF58|79974 | 0.372 | 6.373e-07 | 0.011 |
SLC22A16|85413 | -0.3624 | 9.597e-07 | 0.0166 |
HK2|3099 | -0.3611 | 1.059e-06 | 0.0183 |
PPARD|5467 | 0.3581 | 1.314e-06 | 0.0227 |
TMEM117|84216 | 0.3708 | 1.616e-06 | 0.0279 |
STK16|8576 | 0.3544 | 1.716e-06 | 0.0296 |
KLRF1|51348 | 0.3514 | 2.129e-06 | 0.0368 |
GENDER | Labels | N |
FEMALE | 80 | |
MALE | 93 | |
Significant markers | N = 3 | |
Higher in MALE | 3 | |
Higher in FEMALE | 0 |
W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
NCRNA00183|554203 | 1159 | 6.406e-15 | 1.11e-10 | 0.8442 |
HDHD1A|8226 | 1995 | 1.518e-07 | 0.00262 | 0.7319 |
CD24|100133941 | 5374 | 4.798e-07 | 0.00828 | 0.7223 |
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Expresson data file = LAML-TB.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = LAML-TB.merged_data.txt
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Number of patients = 173
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Number of genes = 17276
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Number of clinical features = 4
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.