This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 17276 genes and 3 clinical features across 173 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
-
6 genes correlated to 'Time to Death'.
-
MYB|4602 , PWWP2A|114825 , CLINT1|9685 , ADSS|159 , PTP4A3|11156 , ...
-
12 genes correlated to 'AGE'.
-
GBP2|2634 , PI4K2A|55361 , FBXO32|114907 , C7ORF58|79974 , SLC22A16|85413 , ...
-
17 genes correlated to 'GENDER'.
-
PRKY|5616 , XIST|7503 , TSIX|9383 , ZFY|7544 , UTY|7404 , ...
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=6 | shorter survival | N=1 | longer survival | N=5 |
AGE | Spearman correlation test | N=12 | older | N=10 | younger | N=2 |
GENDER | t test | N=17 | male | N=4 | female | N=13 |
Time to Death | Duration (Months) | 0.9-94.1 (median=12) |
censored | N = 57 | |
death | N = 94 | |
Significant markers | N = 6 | |
associated with shorter survival | 1 | |
associated with longer survival | 5 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
MYB|4602 | 0.54 | 6.519e-07 | 0.011 | 0.342 |
PWWP2A|114825 | 0.37 | 6.753e-07 | 0.012 | 0.336 |
CLINT1|9685 | 0.32 | 1.362e-06 | 0.024 | 0.367 |
ADSS|159 | 0.29 | 2.184e-06 | 0.038 | 0.335 |
PTP4A3|11156 | 1.41 | 2.564e-06 | 0.044 | 0.647 |
HSDL1|83693 | 0.46 | 2.597e-06 | 0.045 | 0.345 |
AGE | Mean (SD) | 55.26 (16) |
Significant markers | N = 12 | |
pos. correlated | 10 | |
neg. correlated | 2 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
GBP2|2634 | 0.4101 | 2.097e-08 | 0.000362 |
PI4K2A|55361 | 0.4064 | 2.889e-08 | 0.000499 |
FBXO32|114907 | 0.3685 | 6.076e-07 | 0.0105 |
C7ORF58|79974 | 0.3723 | 6.221e-07 | 0.0107 |
SLC22A16|85413 | -0.3627 | 9.392e-07 | 0.0162 |
HK2|3099 | -0.3621 | 9.824e-07 | 0.017 |
PPARD|5467 | 0.358 | 1.325e-06 | 0.0229 |
STK16|8576 | 0.355 | 1.643e-06 | 0.0284 |
TMEM117|84216 | 0.3703 | 1.679e-06 | 0.029 |
KLRF1|51348 | 0.3506 | 2.251e-06 | 0.0389 |
GENDER | Labels | N |
FEMALE | 80 | |
MALE | 93 | |
Significant markers | N = 17 | |
Higher in MALE | 4 | |
Higher in FEMALE | 13 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
PRKY|5616 | 52.08 | 2.604e-93 | 4.5e-89 | 1 |
XIST|7503 | -44.33 | 3.364e-82 | 5.81e-78 | 0.9995 |
TSIX|9383 | -33.18 | 2.43e-66 | 4.19e-62 | 0.9998 |
ZFY|7544 | 56.47 | 1.64e-49 | 2.83e-45 | 1 |
UTY|7404 | 68.34 | 1.127e-29 | 1.95e-25 | 1 |
PRKX|5613 | -12.96 | 3.466e-27 | 5.98e-23 | 0.9194 |
KDM5C|8242 | -10.21 | 7.258e-19 | 1.25e-14 | 0.8719 |
NCRNA00183|554203 | -9.4 | 7.232e-17 | 1.25e-12 | 0.8442 |
ZFX|7543 | -9.25 | 2.677e-16 | 4.62e-12 | 0.8653 |
ZRSR2|8233 | -9.07 | 1.408e-15 | 2.43e-11 | 0.8535 |
-
Expresson data file = LAML-TB.uncv2.mRNAseq_RSEM_normalized_log2.txt
-
Clinical data file = LAML-TB.merged_data.txt
-
Number of patients = 173
-
Number of genes = 17276
-
Number of clinical features = 3
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 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.