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Knit directory: survival-susie/
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Result format: an R matrix with the following columns
MAF: Minor allele frequencies
missing.rate: Missing rates
p.value.spa: p value (recommanded) from a saddlepoint approximation.
p.value.norm: p value from a normal distribution approximation.
Stat: score statistics
Var: estimated variances of the score statistics
z: z values corresponding to the score statistics
library(SPACox)Loading required package: seqminerLoading required package: data.tablelibrary(survival)# Simulation phenotype and genotype
set.seed(1)
N = 1e3
nSNP = 3
MAF = 0.1
Phen.mtx = data.frame(ID = paste0("IID-",1:N),
event=rbinom(N,1,0.5),
time=runif(N),
Cov1=rnorm(N),
Cov2=rbinom(N,1,0.5))
# First two SNPs with cor = 1
x1 = rnorm(N)
x2 = x1 + rnorm(N, sd = 1)
Geno.mtx = matrix(c(x1, x1, x2),N,nSNP)
cor(Geno.mtx) [,1] [,2] [,3]
[1,] 1.0000000 1.0000000 0.7441044
[2,] 1.0000000 1.0000000 0.7441044
[3,] 0.7441044 0.7441044 1.0000000# NOTE: The row and column names of genotype matrix are required.
rownames(Geno.mtx) = paste0("IID-",1:N)
colnames(Geno.mtx) = paste0("SNP-",1:nSNP)# Attach the survival package so that we can use its function Surv()
t1 = proc.time()
obj.null = SPACox_Null_Model(Surv(time,event)~Cov1+Cov2, data=Phen.mtx,
pIDs=Phen.mtx$ID, gIDs=rownames(Geno.mtx))[1] "Start calculating empirical CGF for martingale residuals..."
[1] "Complete 1000/10000."
[1] "Complete 2000/10000."
[1] "Complete 3000/10000."
[1] "Complete 4000/10000."
[1] "Complete 5000/10000."
[1] "Complete 6000/10000."
[1] "Complete 7000/10000."
[1] "Complete 8000/10000."
[1] "Complete 9000/10000."
[1] "Complete 10000/10000."SPACox.res = SPACox(obj.null, Geno.mtx)[1] "Sample size is 1000."
[1] "Number of variants is 3."
[1] "Start Analyzing..."
[1] "2024-06-06 16:41:49 CDT"
[1] "Analysis Complete."
[1] "2024-06-06 16:41:49 CDT"t2 = proc.time()
t2 - t1 user system elapsed
1.145 0.501 1.707 # The below is an example code to use survival package
t1 = proc.time()
coxph(Surv(time,event)~Cov1+Cov2+Geno.mtx, data=Phen.mtx)Call:
coxph(formula = Surv(time, event) ~ Cov1 + Cov2 + Geno.mtx, data = Phen.mtx)
coef exp(coef) se(coef) z p
Cov1 -0.05679 0.94479 0.04371 -1.299 0.194
Cov2 -0.10803 0.89760 0.09267 -1.166 0.244
Geno.mtxSNP-1 0.09007 1.09425 0.06721 1.340 0.180
Geno.mtxSNP-2 NA NA 0.00000 NA NA
Geno.mtxSNP-3 -0.03418 0.96640 0.04745 -0.720 0.471
Likelihood ratio test=4.18 on 4 df, p=0.3816
n= 1000, number of events= 480 t2 = proc.time()
t2 - t1 user system elapsed
0.012 0.001 0.014 # we recommand using column of 'p.value.spa' to associate genotype with time-to-event phenotypes
head(SPACox.res) MAF missing.rate p.value.spa p.value.norm Stat Var
SNP-1 0.02784507 0 0.2216493 0.2216493 27.86489 519.8315
SNP-2 0.02784507 0 0.2216493 0.2216493 27.86489 519.8315
SNP-3 0.01215918 0 0.6529406 0.6529406 14.59243 1053.0433
z
SNP-1 1.2221541
SNP-2 1.2221541
SNP-3 0.4496809coxph(Surv(time,event)~Cov1+Cov2+Geno.mtx[,1], data=Phen.mtx)Call:
coxph(formula = Surv(time, event) ~ Cov1 + Cov2 + Geno.mtx[,
1], data = Phen.mtx)
coef exp(coef) se(coef) z p
Cov1 -0.05569 0.94583 0.04365 -1.276 0.202
Cov2 -0.10232 0.90274 0.09231 -1.108 0.268
Geno.mtx[, 1] 0.05345 1.05490 0.04387 1.218 0.223
Likelihood ratio test=3.67 on 3 df, p=0.2999
n= 1000, number of events= 480 coxph(Surv(time,event)~Cov1+Cov2+Geno.mtx[,3], data=Phen.mtx)Call:
coxph(formula = Surv(time, event) ~ Cov1 + Cov2 + Geno.mtx[,
3], data = Phen.mtx)
coef exp(coef) se(coef) z p
Cov1 -0.04969 0.95153 0.04337 -1.146 0.252
Cov2 -0.09478 0.90957 0.09211 -1.029 0.303
Geno.mtx[, 3] 0.01400 1.01410 0.03098 0.452 0.651
Likelihood ratio test=2.38 on 3 df, p=0.497
n= 1000, number of events= 480 # Simulation phenotype and genotype
set.seed(1)
N = 10000
nSNP = 1000
MAF = 0.1
Phen.mtx = data.frame(ID = paste0("IID-",1:N),
event=rbinom(N,1,0.5),
time=runif(N),
Cov1=rnorm(N),
Cov2=rbinom(N,1,0.5))
Geno.mtx = matrix(rbinom(N*nSNP,2,MAF),N,nSNP)
# NOTE: The row and column names of genotype matrix are required.
rownames(Geno.mtx) = paste0("IID-",1:N)
colnames(Geno.mtx) = paste0("SNP-",1:nSNP)
t1 = proc.time()
obj.null = SPACox_Null_Model(Surv(time,event)~Cov1+Cov2, data=Phen.mtx,
pIDs=Phen.mtx$ID, gIDs=rownames(Geno.mtx))[1] "Start calculating empirical CGF for martingale residuals..."
[1] "Complete 1000/10000."
[1] "Complete 2000/10000."
[1] "Complete 3000/10000."
[1] "Complete 4000/10000."
[1] "Complete 5000/10000."
[1] "Complete 6000/10000."
[1] "Complete 7000/10000."
[1] "Complete 8000/10000."
[1] "Complete 9000/10000."
[1] "Complete 10000/10000."SPACox.res = SPACox(obj.null, Geno.mtx)[1] "Sample size is 10000."
[1] "Number of variants is 1000."
[1] "Start Analyzing..."
[1] "2024-06-06 16:41:54 CDT"
[1] "Analysis Complete."
[1] "2024-06-06 16:41:56 CDT"t2 = proc.time()
time.spa = t2 - t1
# The below is an example code to use survival package
t1 = proc.time()
coxph(Surv(time,event)~Cov1+Cov2+Geno.mtx[,1], data=Phen.mtx)Call:
coxph(formula = Surv(time, event) ~ Cov1 + Cov2 + Geno.mtx[,
1], data = Phen.mtx)
coef exp(coef) se(coef) z p
Cov1 -0.000685 0.999315 0.014318 -0.048 0.962
Cov2 -0.029170 0.971251 0.028430 -1.026 0.305
Geno.mtx[, 1] 0.008415 1.008450 0.032692 0.257 0.797
Likelihood ratio test=1.12 on 3 df, p=0.7722
n= 10000, number of events= 4953 t2 = proc.time()
time.coxph = (t2 - t1)*nSNPtime.spa user system elapsed
4.195 1.891 6.175 time.coxph user system elapsed
47 12 59
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin20.6.0 (64-bit)
Running under: macOS Monterey 12.0.1
Matrix products: default
BLAS: /usr/local/Cellar/openblas/0.3.18/lib/libopenblasp-r0.3.18.dylib
LAPACK: /usr/local/Cellar/r/4.1.1_1/lib/R/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] survival_3.2-11 SPACox_0.1.2 data.table_1.14.9 seqminer_9.1
[5] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 pillar_1.9.0 compiler_4.1.1 bslib_0.4.1
[5] later_1.3.0 jquerylib_0.1.4 git2r_0.28.0 tools_4.1.1
[9] digest_0.6.28 lattice_0.20-44 jsonlite_1.7.2 evaluate_0.14
[13] lifecycle_1.0.3 tibble_3.1.5 pkgconfig_2.0.3 rlang_1.1.1
[17] Matrix_1.5-3 cli_3.6.1 rstudioapi_0.13 yaml_2.2.1
[21] xfun_0.27 fastmap_1.1.0 stringr_1.4.0 knitr_1.36
[25] fs_1.5.0 vctrs_0.6.3 sass_0.4.4 grid_4.1.1
[29] rprojroot_2.0.2 glue_1.4.2 R6_2.5.1 fansi_0.5.0
[33] rmarkdown_2.11 magrittr_2.0.1 whisker_0.4 splines_4.1.1
[37] promises_1.2.0.1 ellipsis_0.3.2 htmltools_0.5.5 httpuv_1.6.3
[41] utf8_1.2.2 stringi_1.7.5 cachem_1.0.6