This pipeline computes the correlation between APOBRC groups and selected clinical features.
Testing the association between APOBEC groups identified by 2 different apobec score and 15 clinical features across 178 patients, no significant finding detected with Q value < 0.25.
-
3 subtypes identified in current cancer cohort by 'APOBEC MUTLOAD MINESTIMATE'. These subtypes do not correlate to any clinical features.
-
3 subtypes identified in current cancer cohort by 'APOBEC ENRICH'. These subtypes do not correlate to any clinical features.
Clinical Features |
Statistical Tests |
APOBEC MUTLOAD MINESTIMATE |
APOBEC ENRICH |
Time to Death | logrank test |
0.536 (0.765) |
0.307 (0.658) |
YEARS TO BIRTH | Kruskal-Wallis (anova) |
0.337 (0.674) |
0.368 (0.69) |
PATHOLOGIC STAGE | Fisher's exact test |
0.0651 (0.658) |
0.296 (0.658) |
PATHOLOGY T STAGE | Fisher's exact test |
0.122 (0.658) |
0.211 (0.658) |
PATHOLOGY N STAGE | Fisher's exact test |
0.468 (0.765) |
0.529 (0.765) |
PATHOLOGY M STAGE | Fisher's exact test |
1 (1.00) |
1 (1.00) |
GENDER | Fisher's exact test |
0.591 (0.772) |
0.501 (0.765) |
RADIATION THERAPY | Fisher's exact test |
1 (1.00) |
0.592 (0.772) |
KARNOFSKY PERFORMANCE SCORE | Kruskal-Wallis (anova) |
0.275 (0.658) |
0.232 (0.658) |
HISTOLOGICAL TYPE | Fisher's exact test |
1 (1.00) |
0.239 (0.658) |
NUMBER PACK YEARS SMOKED | Kruskal-Wallis (anova) |
0.71 (0.887) |
0.454 (0.765) |
YEAR OF TOBACCO SMOKING ONSET | Kruskal-Wallis (anova) |
0.252 (0.658) |
0.12 (0.658) |
RESIDUAL TUMOR | Fisher's exact test |
0.303 (0.658) |
0.301 (0.658) |
RACE | Fisher's exact test |
0.74 (0.888) |
0.844 (0.973) |
ETHNICITY | Fisher's exact test |
0.192 (0.658) |
0.277 (0.658) |
Cluster Labels | 0 | HIGH | LOW |
---|---|---|---|
Number of samples | 68 | 55 | 55 |
Cluster Labels | FC.HIGH.ENRICH | FC.LOW.ENRICH | FC.NO.ENRICH |
---|---|---|---|
Number of samples | 58 | 52 | 68 |
-
APOBEC groups file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/APOBEC_Pipelines/LUSC-TP/22526217/APOBEC_clinical_corr_input_22539289/APOBEC_for_clinical.correlaion.input.categorical.txt
-
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LUSC-TP/22506957/LUSC-TP.merged_data.txt
-
Number of patients = 178
-
Number of selected clinical features = 15
APOBEC classification based on APOBEC_MutLoad_MinEstimate : a. APOBEC non group -- samples with zero value, b. APOBEC high group -- samples above median value in non zero samples, c. APOBEC low group -- samples below median value in non zero samples.
APOBEC classification based on APOBEC_enrich : a. No Enrichmment group -- all samples with BH_Fisher_p-value_tCw > 0.05, b. Low enrichment group -- samples with BH_Fisher_p-value_tCw = < 0.05 and APOBEC_enrich=<2, c. High enrichment group -- samples with BH_Fisher_p-value_tCw =< 0.05 and APOBEC_enrich>2.
For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R
For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.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.