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:

Establishing prognosis models using 10 machine learning algorithms.
Building binary response models with 7 machine learning algorithms.
Conducting core feature selection related to prognosis using 8 machine learning methods.
Visualizing the performance of each model.

⚡ Here is a detailed tutorial on step-wise procedures of this versatile machine-learning framework, which can be also accessed from JoVE (https://www.jove.com/t/68553/constructing-visualizing-models-using-mime-based-machine-learning).

Install

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.

We recommend training dataset with more than 100 samples and gene set with more than 50 genes.

1. Construct predicting models for prognosis

1.1 Select the optimal model

library(Mime1)
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 )

ML.Dev.Prog.Sig() provides three modes including all, single, and double. all means using all ten algorithms and the combinations. single means using only one of the ten algorithms. double means using the combination with two algorithms. In most casees, we will generally use all mode to analysis data.
If you set unicox.filter.for.candi as T (default), Mime will firstly perform univariable cox regression among provided genes in the training dataset to screen out the prognostic variables which are then used to construct models.

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.

Plot C-index of specific model among different datasets:

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

If input object res is from mode all used in ML.Dev.Prog.Sig(), you should define model as specific model name, while define model as SOD.

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)

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")

cal_AUC_ml_res() also provides three modes, which should be consistent with mode uesd by ML.Dev.Prog.Sig().
AUC_time for 1 year, 2 years, 3 years......, We recommend using the shortest survival time among all datasets.

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)

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)

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))

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))

type includes categorical and continuous. categorical means that the patients are divided into two subgroups based on the median of the risk score. continuous means performing the univariable Cox regression bu using the continuous risk score.

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)

cal_RS_pre.prog.sig() will calculate the risk score based on the signatures from previous papers.
If use_your_own_collected_sig is set as T, you should provide a data frame containing the information of the signatures. The column names of the data frame are model, PMID, Cancer, Author, Coef and symbol. The model consists of the first name of the first author and PMID of paper. Cancer uses abbreviations like the format of TCGA. Author is the first name of the first author. Coef is the coefficient of each variable. symbol is the gene name. Otherwise, we use our collected models of glioma.

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")

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)

cal_cindex_pre.prog.sig() will calculate the C-index based on the signatures from previous papers like the fuction cal_RS_pre.prog.sig().

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))

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')

cal_auc_pre.prog.sig() will calculate the AUC based on the signatures from previous papers like the fuction cal_RS_pre.prog.sig().
AUC_time is like the requirement by cal_AUC_ml_res().

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))

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)

If you want to use this function, you should install package immunedeconv ahead.
TME_deconvolution_all() includes 10 deconvolution methods ("quantiseq", "xcell", "epic", "abis", "mcp_counter", "estimate", "cibersort", "cibersort_abs", "timer", "consensus_tme") from immunedeconv::deconvolution_methods. By default, deconvolution methods are set as ("xcell", "epic", "abis", "estimate", "cibersort", "cibersort_abs").

Show the results:

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

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
)

ML.Dev.Pred.Category.Sig() develop the predictive model for the binary variables with machine learning algorithms.

Plot AUC of different methods among different datasets:

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

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)

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)

cal_auc_previous_sig() will calculate the AUC based on the signatures from previous papers for immunotherapy response.
cores_for_parallel means the cores you can choose for parallel operation. If multi-cores condition is error, please set cores_for_parallel as 1.

Plot comparison results of specific model:

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

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 )

ML.Corefeature.Prog.Screen() provides three modes including all, single, and all_without_SVM. all mode means using all eight methods ("RSF", "Enet", "Boruta", "Xgboost", "SVM-REF", "Lasso", "CoxBoost', "StepCox") for selecting. single mode means using only one method for running. If you use single mode, single_ml should be specified among eight methods. Since SVM takes too long time, we define other seven methods used for selecting as all_without_SVM mode.
The output genes are closely associated with patient outcome and higher frequence of screening means more critical.

Upset plot of genes filtered by different methods:

core_feature_select(res.feature.all)

Plot the rank of genes filtered by different methods:

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

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)

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)

Citations

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

Liu H, Zhang W, Zhang Y, Adegboro AA, Fasoranti DO, Dai L, Pan Z, Liu H, Xiong Y, Li W, Peng K, Wanggou S, Li X. Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection. Comput Struct Biotechnol J. 2024.
Liu H, Zhang W, Zhang, Y, Li X, Wanggou S. Constructing and Visualizing Models using Mime-based Machine-learning Framework. J Vis Exp. 2025.

Contact

Any technical question please list in Issues section.

Name		Name	Last commit message	Last commit date
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R		R
data		data
fig		fig
inst		inst
man		man
tests		tests
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.Rhistory		.Rhistory
.gitattributes		.gitattributes
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
LICENSE.md		LICENSE.md
MIME.Rproj		MIME.Rproj
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.md		README.md

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Mime

Install

Quick Start

1. Construct predicting models for prognosis

1.1 Select the optimal model

1.2 Calculate AUC scores of each model

1.3 Meta-analysis of univariate cox regression for specific model

1.4 Comparison with previously pblished models

1.5 Immune infiltration analysis

2. Construct predicting models for response

3. Core feature selection

Citations

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Mime

Install

Quick Start

1. Construct predicting models for prognosis

1.1 Select the optimal model

1.2 Calculate AUC scores of each model

1.3 Meta-analysis of univariate cox regression for specific model

1.4 Comparison with previously pblished models

1.5 Immune infiltration analysis

2. Construct predicting models for response

3. Core feature selection

Citations

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