This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 17814 genes and 7 clinical features across 32 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.
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1 gene correlated to 'GENDER'.
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MRPL3
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1 gene correlated to 'PATHOLOGY.T'.
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PAPPA2
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No genes correlated to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.N', and 'NEOADJUVANT.THERAPY'.
Complete statistical result table is provided in Supplement Table 1
Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at Q value < 0.05.
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=1 | male | N=1 | female | N=0 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
PATHOLOGY T | Spearman correlation test | N=1 | higher pT | N=0 | lower pT | N=1 |
PATHOLOGY N | Spearman correlation test | N=0 | ||||
NEOADJUVANT THERAPY | t test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.5-56.8 (median=8.3) |
censored | N = 27 | |
death | N = 4 | |
Significant markers | N = 0 |
Table S2. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 65.7 (11) |
Significant markers | N = 0 |
Table S3. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 18 | |
MALE | 14 | |
Significant markers | N = 1 | |
Higher in MALE | 1 | |
Higher in FEMALE | 0 |
Table S4. Get Full Table List of one gene differentially expressed by 'GENDER'
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
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MRPL3 | 5.98 | 2.266e-06 | 0.0404 | 0.9405 |
Figure S1. Get High-res Image As an example, this figure shows the association of MRPL3 to 'GENDER'. P value = 2.27e-06 with T-test analysis.
![](V3ex.png)
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S5. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 56.67 (49) |
Score | N | |
0 | 1 | |
80 | 1 | |
90 | 1 | |
Significant markers | N = 0 |
Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.T'
PATHOLOGY.T | Mean (SD) | 1.66 (0.55) |
N | ||
T1 | 12 | |
T2 | 19 | |
T3 | 1 | |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
Table S7. Get Full Table List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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PAPPA2 | -0.7603 | 4.455e-07 | 0.00794 |
Figure S2. Get High-res Image As an example, this figure shows the association of PAPPA2 to 'PATHOLOGY.T'. P value = 4.45e-07 with Spearman correlation analysis.
![](V5ex.png)
Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.N'
PATHOLOGY.N | Mean (SD) | 0.39 (0.72) |
N | ||
N0 | 23 | |
N1 | 4 | |
N2 | 4 | |
Significant markers | N = 0 |
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Expresson data file = LUAD.medianexp.txt
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Clinical data file = LUAD.clin.merged.picked.txt
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Number of patients = 32
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Number of genes = 17814
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Number of clinical features = 7
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.