l-magnificence / Mime

Machine learning-based integration model with elegant performance
Other
83 stars 16 forks source link

Mime

The Mime package provides a user-friendly solution for constructing machine learning-based integration models from transcriptomic data.

With the widespread use of high-throughput sequencing technologies, understanding biology and cancer heterogeneity has been revolutionized. Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.

It offers four main applications:

You can install the development version of Mime like so:

# options("repos"= c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
# options(BioC_mirror="http://mirrors.tuna.tsinghua.edu.cn/bioconductor/")
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)

Quick Start

This is a basic example which shows you how to use Mime:

Mime need multiple cohorts containing transcriptional sequencing data with information of survival or clinical response to therapy as well as a gene set as inputs. You can dowmload the example data in External data file to check the format of input.

load("./Example.cohort.Rdata")
list_train_vali_Data[["Dataset1"]][1:5,1:5]
#>               ID    OS.time OS   MT-CO1   MT-CO3
#>  TCGA.DH.A66B.01 1281.65322  0 13.77340 13.67931
#>  TCGA.HT.7607.01   96.19915  1 14.96535 14.31857
#>  TCGA.DB.A64Q.01  182.37755  0 13.90659 13.65321
#>  TCGA.DU.8167.01  471.97707  0 14.90695 14.59776
#>  TCGA.HT.7610.01 1709.53901  0 15.22784 14.62756

The first column ID is the sample ID, second to third column OS.time and OS are the survival time and status of patients, other columns are the gene expression level scaled with log2(x+1). Dataset1 is the training dataset, while other Datasets as validation.

load("./Example.ici.Rdata")
list_train_vali_Data[["training"]][1:5,1:5]
#>                                   ID Var      FTH1   EEF1A1      ACTB
#>                      SAMf2ce197162ce   N 10.114846 4.817746 11.230180
#>                           ERR2208915   Y  2.044180 5.038854  3.977902
#> G138701_RCCBMS-00141-T_v1_RNA_OnPrem   Y  5.406008 5.341635  5.366668
#>                      SAMe41b1e773582   N  9.215794 4.707360 11.412721
#>                      SAM5ffd7e4cd794   N  9.003710 3.908884 10.440559

The first column ID is the sample ID, second column Var is the theraputic response of patients (N: No response; Y: response), other columns are the gene expression level scaled with log2(x+1). training is the training dataset, while other Datasets as validation.

load("./genelist.Rdata")
#> [1] "MYC"    "CTNNB1" "JAG2"   "NOTCH1" "DLL1"   "AXIN2"  "PSEN2"  "FZD1"   "NOTCH4" "LEF1"   "AXIN1"  "NKD1"   "WNT5B" 
#>[14] "CUL1"   "JAG1"   "MAML1"  "KAT2A"  "GNAI1"  "WNT6"   "PTCH1"  "NCOR2"  "DKK4"   "HDAC2"  "DKK1"   "TCF7"   "WNT1"  
#>[27] "NUMB"   "ADAM17" "DVL2"   "PPARD"  "NCSTN"  "HDAC5"  "CCND2"  "FRAT1"  "CSNK1E" "RBPJ"   "FZD8"   "TP53"   "SKP2"  
#>[40] "HEY2"   "HEY1"   "HDAC11"

This gene set is associated with Wnt/β-catenin signalling from MSigDB.

1. Construct predicting models for prognosis

1.1 Select the optimal model

library(Mime)
load("./Example.cohort.Rdata")
load("./genelist.Rdata")
res <- 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',nodesize =5,seed = 5201314 )

Plot C-index of each model:

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

We can find that StepCox[forward] + plsRcox have the highest C-index. Screenshot

Plot C-index of specific model among different datasets:

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

Plot survival curve of patients according to risk score calculated by specific model among different datasets:

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)

Screenshot

1.2 Calculate AUC scores of each model

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")

Here, we only plot 1-year AUC predicted by all models:

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)

Screenshot

Plot ROC of specific model among different datasets:

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)

Screenshot

Plot 1, 3, and 5-year AUC of specific model among different datasets:

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))

Screenshot

1.3 Meta-analysis of univariate cox regression for specific model

unicox.rs.res <- cal_unicox_ml_res(res.by.ML.Dev.Prog.Sig = res,optimal.model = "StepCox[forward] + plsRcox",type ='categorical')
metamodel <- cal_unicox_meta_ml_res(input = unicox.rs.res)
meta_unicox_vis(metamodel,
                dataset = names(list_train_vali_Data))

Screenshot

1.4 Comparison with previously pblished models

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)

Compare the HR of specific model with previously published models:

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

Screenshot

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)

Compare the C-index of specific model with previously published models:

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

Screenshot

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')

Compare the AUC of specific model with previously published models:

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

Screenshot

1.5 Immune infiltration analysis

After completing the risk grouping, users can perform downstream analysis on the grouped data. Here, we combine Mime with the R package immunedeconv to assist users in quickly previewing immune infiltration. If users require a more precise immune infiltration analysis, they can fine-tune the parameters themselves.

devo <- TME_deconvolution_all(list_train_vali_Data)

Show the results:

immuno_heatmap(res,
               devo,
               model_name="StepCox[backward] + plsRcox",
               dataset="Dataset1")

Screenshot

2. Construct predicting models for response

load("./Example.ici.Rdata")
load("./genelist.Rdata")
res.ici <- ML.Dev.Pred.Category.Sig(train_data = list_train_vali_Data$training,
                                      list_train_vali_Data = list_train_vali_Data,
                                      candidate_genes = genelist,
                                      methods = c('nb','svmRadialWeights','rf','kknn','adaboost','LogitBoost','cancerclass'),
                                      seed = 5201314,
                                      cores_for_parallel = 60
)

Plot AUC of different methods among different datasets:

auc_vis_category_all(res.ici,dataset = c("training","validation"),
                     order= c("training","validation"))

Screenshot

Plot ROC of specific method among different datasets:

plot_list<-list()
methods <- c('nb','svmRadialWeights','rf','kknn','adaboost','LogitBoost','cancerclass')
for (i in methods) {
  plot_list[[i]]<-roc_vis_category(res.ici,model_name = i,dataset = c("training","validation"),
                                   order= c("training","validation"),
                                   anno_position=c(0.4,0.25))
}
aplot::plot_list(gglist=plot_list,ncol=3)

Screenshot

Compared AUC with other published models associated with immunotherapy response:

auc.other.pre <- cal_auc_previous_sig(list_train_vali_Data = list_train_vali_Data,seed = 5201314,
                                      train_data = list_train_vali_Data$training,
                                      cores_for_parallel = 32)

Plot comparison results of specific model:

auc_category_comp(res.ici,
                  auc.other.pre,
                  model_name="svmRadialWeights",
                  dataset=names(list_train_vali_Data))

Screenshot

3. Core feature selection

load("./Example.cohort.Rdata")
load("./genelist.Rdata")
res.feature.all <- ML.Corefeature.Prog.Screen(InputMatrix = list_train_vali_Data$Dataset1,
                                            candidate_genes = genelist,
                                            mode = "all",nodesize =5,seed = 5201314 )

Upset plot of genes filtered by different methods:

core_feature_select(res.feature.all)

Screenshot

Plot the rank of genes filtered by different methods:

core_feature_rank(res.feature.all, top=20)

Screenshot

Here, we randomly select top two genes to analyze their correlation:

dataset_col<-c("#3182BDFF","#E6550DFF")
corplot <- list()
for (i in c(1:2)) {
  print(corplot[[i]]<-cor_plot(list_train_vali_Data[[i]],
                               dataset=names(list_train_vali_Data)[i],
                               color = dataset_col[i],
                               feature1="PSEN2",
                               feature2="WNT5B",
                               method="pearson"))
}
aplot::plot_list(gglist=corplot,ncol=2)

Screenshot

Plot survival curve of patients according to median expression level of specific gene among different datasets:

survplot <- vector("list",2) 
for (i in c(1:2)) {
  print(survplot[[i]]<-core_feature_sur("PSEN2", 
                                        InputMatrix=list_train_vali_Data[[i]],
                                        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)

Screenshot

Citations

If you use Mime in the study, please cite the following publication:

Contact

Any technical question please list in Issues section.