Easily Process a Batch of Cox Models

Related tags

Deep Learning r-package batch-processing cox-model
Overview

ezcox: Easily Process a Batch of Cox Models

CRAN status Hits R-CMD-check Codecov test coverage Lifecycle: stable

The goal of ezcox is to operate a batch of univariate or multivariate Cox models and return tidy result.

⏬ Installation

You can install the released version of ezcox from CRAN with:

install.packages("ezcox")

And the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("ShixiangWang/ezcox")

It is possible to install ezcox from Conda conda-forge channel:

conda install r-ezcox --channel conda-forge

Visualization feature of ezcox needs the recent version of forestmodel, please run the following commands:

remotes::install_github("ShixiangWang/forestmodel")

🔰 Example

This is a basic example which shows you how to get result from a batch of cox models.

library(ezcox)
#> Welcome to 'ezcox' package!
#> =======================================================================
#> You are using ezcox version 0.8.1
#> 
#> Github page  : https://github.com/ShixiangWang/ezcox
#> Documentation: https://shixiangwang.github.io/ezcox/articles/ezcox.html
#> 
#> Run citation("ezcox") to see how to cite 'ezcox'.
#> =======================================================================
#> 
library(survival)

# Build unvariable models
ezcox(lung, covariates = c("age", "sex", "ph.ecog"))
#> => Processing variable age
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> => Processing variable sex
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> => Processing variable ph.ecog
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> # A tibble: 3 × 12
#>   Variable is_control contrast_level ref_level n_contrast n_ref    beta    HR
#>   <chr>    <lgl>      <chr>          <chr>          <int> <int>   <dbl> <dbl>
#> 1 age      FALSE      age            age              228   228  0.0187 1.02 
#> 2 sex      FALSE      sex            sex              228   228 -0.531  0.588
#> 3 ph.ecog  FALSE      ph.ecog        ph.ecog          227   227  0.476  1.61 
#> # … with 4 more variables: lower_95 <dbl>, upper_95 <dbl>, p.value <dbl>,
#> #   global.pval <dbl>

# Build multi-variable models
# Control variable 'age'
ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age")
#> => Processing variable sex
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> => Processing variable ph.ecog
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> # A tibble: 4 × 12
#>   Variable is_control contrast_level ref_level n_contrast n_ref    beta    HR
#>   <chr>    <lgl>      <chr>          <chr>          <int> <int>   <dbl> <dbl>
#> 1 sex      FALSE      sex            sex              228   228 -0.513  0.599
#> 2 sex      TRUE       age            age              228   228  0.017  1.02 
#> 3 ph.ecog  FALSE      ph.ecog        ph.ecog          227   227  0.443  1.56 
#> 4 ph.ecog  TRUE       age            age              228   228  0.0113 1.01 
#> # … with 4 more variables: lower_95 <dbl>, upper_95 <dbl>, p.value <dbl>,
#> #   global.pval <dbl>
lung$ph.ecog = factor(lung$ph.ecog)
zz = ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age", return_models=TRUE)
#> => Processing variable sex
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> => Processing variable ph.ecog
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
mds = get_models(zz)
str(mds, max.level = 1)
#> List of 2
#>  $ Surv ~ sex + age    :List of 19
#>   ..- attr(*, "class")= chr "coxph"
#>   ..- attr(*, "Variable")= chr "sex"
#>  $ Surv ~ ph.ecog + age:List of 22
#>   ..- attr(*, "class")= chr "coxph"
#>   ..- attr(*, "Variable")= chr "ph.ecog"
#>  - attr(*, "class")= chr [1:2] "ezcox_models" "list"
#>  - attr(*, "has_control")= logi TRUE

show_models(mds)

🌟 Vignettes

📃 Citation

If you are using it in academic research, please cite the preprint arXiv:2110.14232 along with URL of this repo.

Comments
  • Fast way to add interaction terms?

    Fast way to add interaction terms?

    Hi, just wondering how the the interaction terms can be handled as "controls" here. Any way to add them rather than manually create new 'interaction variables' in the data? Cheers.

    opened by lijing-lin 12
  • similar tools or approach

    similar tools or approach

    • https://github.com/kevinblighe/RegParallel https://bioconductor.org/packages/release/data/experiment/vignettes/RegParallel/inst/doc/RegParallel.html
    • https://pubmed.ncbi.nlm.nih.gov/25769333/
    opened by ShixiangWang 12
  • 没有show-models这个函数

    没有show-models这个函数

    install.packages("ezcox")#先安装包 packageVersion("ezcox")#0.4.0版本 library(survival) library(ezcox) library("devtools") install.packages("devtools") devtools::install_github("ShixiangWang/ezcox") lung$ph.ecog <- factor(lung$ph.ecog) zz <- ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age", return_models = TRUE) zz mds <- get_models(zz) str(mds, max.level = 1) install.packages("forestmodel") library("forestmodel") show_models(mds) 问题是没有show-models这个函数

    opened by demi0304 4
  • 并行速度不够快

    并行速度不够快 

    library(survival)
### write a function
fastcox_single <- function(num){
  data= cbind(clin,expreset[,num])
  UniNames <- colnames(data)[-c(1:2)]
  do.call(rbind,lapply(UniNames,function(i){
    surv =as.formula(paste('Surv(times, status)~',i))
    cur_cox=coxph(surv, data = data)
    x = summary(cur_cox)
    HR=x$coefficients[i,"exp(coef)"]
    HR.confint.lower = signif(x$conf.int[i,"lower .95"],3)
    HR.confint.upper = signif(x$conf.int[i,"upper .95"],3)
    CI <- paste0("(",HR.confint.lower, "-",HR.confint.upper,")")
    p.value=x$coef[i,"Pr(>|z|)"]
    data.frame(gene=i,HR=HR,CI=CI,p.value=p.value)
  }))
}


clin = share.data[,1:2]
expreset = share.data[,-c(1:2)]
length = ncol(expreset)
groupdf = data.frame(colnuber = seq(1,length),
                     group = rep(1:ceiling(length/100),each=100,length.out=length))
index = split(groupdf$colnuber,groupdf$group)
library(future.apply)
# options(future.globals.maxSize= 891289600)
plan(multiprocess)
share.data.os.result=do.call(rbind,future_lapply(index,fastcox_single))


#=== Use ezcox
# devtools::install_github("ShixiangWang/ezcox")
res = ezcox::ezcox(share.data, covariates = colnames(share.data)[-(1:2)], parallel = TRUE, time = "times")


share.data$VIM.INHBE
tt = ezcox::ezcox(share.data, covariates = "VIM.INHBE", return_models = T, time = "times")

    大批量计算时两者时间差4倍

    enhancement 
    opened by ShixiangWang 3
  • 建议

    建议

    诗翔：

    我用你的这个R包，有两个建议，你可以改进一下：

    1. 对covariates的顺序，按照用户给的顺序进行展示，现在是按照字符的大小排序的。
    2. 对HR太大的值，使用科学记数法进行展示

    这个是用的代码

    zz = ezcox(
  scores.combined,
  covariates = c("JSI", "Tindex", "Subclonal_Aca", "Subclonal_Nec", "ITH_Aca", "ITH_Nec"),
  controls = "Age",
  time = "Survival_months",
  status = "Death",
  return_models = TRUE
)

mds = get_models(zz)

show_models(mds, drop_controls = TRUE)

    这个是现在的图

    image

    opened by qingjian1991 2
  • Change format setting including text size

    Change format setting including text size

    See

    library(survival)
library(forestmodel)
library(ezcox)
show_forest(lung, covariates = c("sex", "ph.ecog"), controls = "age", format_options = forest_model_format_options(text_size = 3))

    image

    opened by ShixiangWang 0
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Releases(v1.0.1)
Owner
Shixiang Wang
Don't Program by Coincidence.
Shixiang Wang
GitHub https://shixiangwang.github.io/ezcox/
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