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01 > library(RTCGA) > infoTCGA <-> > View(infoTCGA) > library(RTCGA.clinical) > clin <-> > class(clin) [1] 'data.frame' > head(clin) times bcr_patient_barcode patient.vital_status 1 3767 TCGA-3C-AAAU 0 2 3801 TCGA-3C-AALI 0 3 1228 TCGA-3C-AALJ 0 4 1217 TCGA-3C-AALK 0 5 158 TCGA-4H-AAAK 0 6 1477 TCGA-5L-AAT0 0 > library(RTCGA.mRNA) > class(BRCA.mRNA) [1] 'data.frame' > dim(BRCA.mRNA) [1] 590 17815 > BRCA.mRNA[1:5,1:5] bcr_patient_barcode ELMO2 CREB3L1 RPS11 PNMA1 1 TCGA-A1-A0SD-01A-11R-A115-07 0.5070833 1.43450 0.765000 0.52600 2 TCGA-A1-A0SE-01A-11R-A084-07 0.1814167 0.89075 0.716000 0.13175 3 TCGA-A1-A0SH-01A-11R-A084-07 0.4615000 2.25925 0.417125 0.32500 4 TCGA-A1-A0SJ-01A-11R-A084-07 0.8770000 0.43775 0.115000 0.75775 5 TCGA-A1-A0SK-01A-12R-A084-07 1.4123333 -0.63725 0.492875 0.94325 > library(dplyr) > exprSet <- brca.mrna="" %="">% + as_tibble() %>% +select(bcr_patient_barcode,PAX8,GATA3,ESR1) %>% +mutate(bcr_patient_barcode=substr(bcr_patient_barcode,1,12)) %>% + inner_join(clin,by='bcr_patient_barcode') > library(survival) > library(survminer) > group <-ifelse(exprset$gata3>median(exprSet$GATA3),'high','low') > sfit <> > sfit Call: survfit(formula = Surv(times, patient.vital_status) ~ group, data = exprSet) n events median 0.95LCL 0.95UCL group=high 295 35 3462 2965 NA group=low 295 46 2763 2207 NA > summary(sfit) Call: survfit(formula = Surv(times, patient.vital_status) ~ group, data = exprSet) group=high time n.risk n.event survival std.err lower 95% CI upper 95% CI 158 254 1 0.996 0.00393 0.988 1.000 160 253 1 0.992 0.00555 0.981 1.000 224 237 1 0.988 0.00692 0.974 1.000 362 207 1 0.983 0.00838 0.967 1.000 365 206 1 0.978 0.00960 0.960 0.997 558 162 1 0.972 0.01128 0.950 0.995 612 152 1 0.966 0.01289 0.941 0.992 825 131 1 0.959 0.01475 0.930 0.988 860 123 1 0.951 0.01656 0.919 0.984 883 120 1 0.943 0.01822 0.908 0.979 921 113 1 0.935 0.01988 0.896 0.974 943 112 1 0.926 0.02138 0.885 0.969 991 107 1 0.918 0.02287 0.874 0.963 1127 101 1 0.908 0.02438 0.862 0.958 1142 99 1 0.899 0.02580 0.850 0.951 1148 98 1 0.890 0.02712 0.838 0.945 1542 61 1 0.875 0.03035 0.818 0.937 1563 58 1 0.860 0.03337 0.797 0.928 1781 51 1 0.844 0.03673 0.775 0.919 1920 46 1 0.825 0.04025 0.750 0.908 2009 44 1 0.806 0.04349 0.726 0.896 2097 41 1 0.787 0.04666 0.700 0.884 2373 34 1 0.764 0.05070 0.670 0.870 2417 32 1 0.740 0.05445 0.640 0.855 2469 30 1 0.715 0.05795 0.610 0.838 2483 29 1 0.690 0.06097 0.581 0.821 2520 27 1 0.665 0.06385 0.551 0.803 2551 26 1 0.639 0.06632 0.522 0.783 2965 20 1 0.607 0.07028 0.484 0.762 3126 18 1 0.574 0.07404 0.445 0.739 3418 14 1 0.533 0.07928 0.398 0.713 3462 13 1 0.492 0.08310 0.353 0.685 3941 11 1 0.447 0.08673 0.306 0.654 3945 9 1 0.397 0.09020 0.255 0.620 4456 8 1 0.348 0.09158 0.207 0.583 group=low time n.risk n.event survival std.err lower 95% CI upper 95% CI 255 226 1 0.996 0.00441 0.987 1.000 304 214 1 0.991 0.00639 0.978 1.000 426 189 1 0.986 0.00823 0.970 1.000 524 171 1 0.980 0.01000 0.961 1.000 548 168 1 0.974 0.01152 0.952 0.997 571 166 1 0.968 0.01286 0.943 0.994 612 157 1 0.962 0.01418 0.935 0.990 639 154 1 0.956 0.01540 0.926 0.986 723 143 1 0.949 0.01668 0.917 0.982 749 138 1 0.942 0.01792 0.908 0.978 754 137 1 0.935 0.01906 0.899 0.973 785 128 2 0.921 0.02138 0.880 0.964 811 126 2 0.906 0.02341 0.861 0.953 921 119 1 0.899 0.02442 0.852 0.948 967 115 1 0.891 0.02543 0.842 0.942 991 113 1 0.883 0.02639 0.833 0.936 1142 102 1 0.874 0.02752 0.822 0.930 1148 101 1 0.866 0.02857 0.811 0.923 1272 90 1 0.856 0.02983 0.799 0.916 1286 89 2 0.837 0.03211 0.776 0.902 1365 75 1 0.826 0.03357 0.762 0.894 1556 58 2 0.797 0.03797 0.726 0.875 1563 55 1 0.783 0.03995 0.708 0.865 1692 47 2 0.749 0.04465 0.667 0.842 1694 45 2 0.716 0.04848 0.627 0.818 1699 43 1 0.699 0.05013 0.608 0.805 1793 39 1 0.681 0.05195 0.587 0.791 1993 30 1 0.659 0.05496 0.559 0.776 2009 29 1 0.636 0.05757 0.533 0.759 2207 27 2 0.589 0.06220 0.479 0.724 2520 24 1 0.564 0.06426 0.451 0.705 2573 22 1 0.539 0.06626 0.423 0.686 2763 19 2 0.482 0.07038 0.362 0.642 2798 17 2 0.425 0.07263 0.304 0.594 3063 13 1 0.393 0.07404 0.271 0.568 3461 10 1 0.353 0.07634 0.231 0.540 4267 6 1 0.294 0.08328 0.169 0.513 > ggsurvplot(sfit,conf.int = FALSE,pval = TRUE)  |
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