TCGA⽣存分析-单基因01
> library(RTCGA)
> infoTCGA <->
> View(infoTCGA)
> library(RTCGA.clinical)
> clin <->
> class(clin)
间谍英文[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
6 147
7 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]
我的阅读故事
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
好看的皮囊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 0.4377
5 0.115000 0.75775
5 TCGA-A1-A0SK-01A-12R-A084-07 1.4123333 -0.63725 0.492875 0.94325
> library(dplyr)
> exprSet <- a="" %="">%
+ as_tibble() %>%
+lect(bcr_patient_barcode,PAX8,GATA3,ESR1) %>%
托福和雅思
> library(survival)
> library(survminer)
> group <-ifel(exprt$gata3>median(exprSet$GATA3),'high','low') > sfit <>
> sfit
Call: survfit(formula = Surv(times, patient.vital_status) ~ group,扩句练习
data = exprSet)
n events median 0.95LCL 0.95UCL
group=high 295 35 3462 2965 NA
group=low 295 46 2763 2207 NA
饮料推广> summary(sfit)
Call: survfit(formula = Surv(times, patient.vital_status) ~ group,
data = exprSet)
group=high
中国驾考网
time n.risk n.event 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
大腿肌肉疼1148 98 1 0.890 0.02712 0.838 0.945
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 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
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)
