🤩 Mime1 | 终极机器学习算法大组合筛选你的最佳模型（一）（预后模型篇）

rm(list = ls())
# if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
# 
# depens<-c('GSEABase', 'GSVA', 'cancerclass', 'mixOmics', 'sparrow', 'sva' , 'ComplexHeatmap' )
# 
# for(i in 1:length(depens)){
#   depen<-depens[i]
#   if (!requireNamespace(depen, quietly = TRUE))  BiocManager::install(depen,update = FALSE)
# }
# 
# if (!requireNamespace("CoxBoost", quietly = TRUE))
#   devtools::install_github("binderh/CoxBoost")
# 
# if (!requireNamespace("fastAdaboost", quietly = TRUE))
#   devtools::install_github("souravc83/fastAdaboost")
# 
# if (!requireNamespace("Mime", quietly = TRUE))
#   devtools::install_github("l-magnificence/Mime")

library(Mime1)

load("./Example.cohort.Rdata")
list_train_vali_Data[["Dataset1"]][1:5,1:5]

load("./genelist.Rdata")

genelist

res <- Mime1::ML.Dev.Prog.Sig(train_data = list_train_vali_Data$Dataset1,
                     list_train_vali_Data = list_train_vali_Data,
                     unicox.filter.for.candi = T,
                     unicox_p_cutoff = 0.05,
                     candidate_genes = genelist,
                     mode = 'all', ## 'all', 'single', and 'double'
                     nodesize =5,
                     seed = 123 
                     )

cindex_dis_all(res,
               validate_set = names(list_train_vali_Data)[-1],
               order = names(list_train_vali_Data),
               width = 0.35
               )

cindex_dis_select(res,
                  model="StepCox[forward] + plsRcox",
                  order= names(list_train_vali_Data))

survplot <- vector("list",2) 
for (i in c(1:2)) {
  print(survplot[[i]]<-rs_sur(res, model_name = "StepCox[forward] + plsRcox",
                              dataset = names(list_train_vali_Data)[i],
                              #color=c("blue","green"),
                              median.line = "hv",
                              cutoff = 0.5,
                              conf.int = T,
                              xlab="Day",pval.coord=c(1000,0.9))
        )
}
aplot::plot_list(gglist=survplot,ncol=2)

all.auc.1y <- cal_AUC_ml_res(res.by.ML.Dev.Prog.Sig = res,train_data = list_train_vali_Data[["Dataset1"]],
                            inputmatrix.list = list_train_vali_Data,mode = 'all',AUC_time = 1,
                            auc_cal_method="KM")
all.auc.3y <- cal_AUC_ml_res(res.by.ML.Dev.Prog.Sig = res,train_data = list_train_vali_Data[["Dataset1"]],
                            inputmatrix.list = list_train_vali_Data,mode = 'all',AUC_time = 3,
                            auc_cal_method="KM")
all.auc.5y <- cal_AUC_ml_res(res.by.ML.Dev.Prog.Sig = res,train_data = list_train_vali_Data[["Dataset1"]],
                            inputmatrix.list = list_train_vali_Data,mode = 'all',AUC_time = 5,
                            auc_cal_method="KM")

auc_dis_all(all.auc.1y,
            dataset = names(list_train_vali_Data),
            validate_set=names(list_train_vali_Data)[-1],
            order= names(list_train_vali_Data),
            width = 0.35,
            year=1)

roc_vis(all.auc.1y,
        model_name = "StepCox[forward] + plsRcox",
        dataset = names(list_train_vali_Data),
        order= names(list_train_vali_Data),
        anno_position=c(0.65,0.55),
        year=1)

auc_dis_select(list(all.auc.1y,all.auc.3y,all.auc.5y),
               model_name="StepCox[forward] + plsRcox",
               dataset = names(list_train_vali_Data),
               order= names(list_train_vali_Data),
               year=c(1,3,5))

unicox.rs.res <- cal_unicox_ml_res(res.by.ML.Dev.Prog.Sig = res,
                                   optimal.model = "StepCox[forward] + plsRcox",
                                   type ='categorical' # 'categorical' or 'continuous'
                                   )

metamodel <- cal_unicox_meta_ml_res(input = unicox.rs.res)

meta_unicox_vis(metamodel,
                dataset = names(list_train_vali_Data))

rs.glioma.lgg.gbm <- cal_RS_pre.prog.sig(use_your_own_collected_sig = F,
                                         type.sig = c('LGG','GBM','Glioma'),
                                        list_input_data = list_train_vali_Data)

HR_com(rs.glioma.lgg.gbm,
       res,
       model_name="StepCox[forward] + plsRcox",
       dataset=names(list_train_vali_Data),
       type = "categorical")

cc.glioma.lgg.gbm <- cal_cindex_pre.prog.sig(use_your_own_collected_sig = F,
                                             type.sig = c('Glioma','LGG','GBM'),
                                            list_input_data = list_train_vali_Data)

cindex_comp(cc.glioma.lgg.gbm,
            res,
            model_name="StepCox[forward] + plsRcox",
            dataset=names(list_train_vali_Data))

auc.glioma.lgg.gbm.1 <- cal_auc_pre.prog.sig(use_your_own_collected_sig = F,
                                            type.sig = c('Glioma','LGG','GBM'),
                                            list_input_data = list_train_vali_Data,AUC_time = 1,
                                            auc_cal_method = 'KM')

auc_comp(auc.glioma.lgg.gbm.1,
         all.auc.1y,
         model_name="StepCox[forward] + plsRcox",
         dataset=names(list_train_vali_Data))