CimpleG

Overview

Installation

# Install from CRAN:install.packages("CimpleG")# Install dev version from github:devtools::install_github("costalab/CimpleG")

Getting started

library("CimpleG")data(train_data)data(train_targets)data(test_data)data(test_targets)# check the train_targets table to see# what other columns can be used as targets# colnames(train_targets)# mini example with just 4 target signaturesset.seed(42)cimpleg_result<- CimpleG(train_data=train_data,train_targets=train_targets,test_data=test_data,test_targets=test_targets,method="CimpleG",has_annotation=TRUE,target_columns= c("neurons","glia","blood_cells","fibroblasts"  ))cimpleg_result$results

# check generated signaturescimpleg_result$signatures#>      neurons         glia  blood_cells  fibroblasts#> "cg24548498" "cg14501977" "cg04785083" "cg03369247"

Get signature annotation

# Get it directly from the results objectcimpleg_result$annotation#> # A tibble: 4 × 8#>   IlmnID     CHR_hg38 Start_hg38  End_hg38 UCSC_RefGene_Name  UCSC_RefGene_Group#>   <chr>      <chr>         <dbl>     <dbl> <chr>              <chr>#> 1 cg24548498 chr2      181684680 181684682 <NA>               <NA>#> 2 cg14501977 chr12     123948446 123948448 CCDC92             5'UTR#> 3 cg04785083 chr1        8971202   8971204 CA6                Body#> 4 cg03369247 chr8       20174518  20174520 SLC18A1;SLC18A1;S… Body;Body;Body;Bo…#> # ℹ 2 more variables: UCSC_CpG_Islands_Name <chr>,#> #   Relation_to_UCSC_CpG_Island <chr># or idependently through the "get_cpg_annotation" functionsignature_annotation<- get_cpg_annotation(cimpleg_result$signatures)# check signature annotationsignature_annotation#> # A tibble: 4 × 8#>   IlmnID     CHR_hg38 Start_hg38  End_hg38 UCSC_RefGene_Name  UCSC_RefGene_Group#>   <chr>      <chr>         <dbl>     <dbl> <chr>              <chr>#> 1 cg24548498 chr2      181684680 181684682 <NA>               <NA>#> 2 cg14501977 chr12     123948446 123948448 CCDC92             5'UTR#> 3 cg04785083 chr1        8971202   8971204 CA6                Body#> 4 cg03369247 chr8       20174518  20174520 SLC18A1;SLC18A1;S… Body;Body;Body;Bo…#> # ℹ 2 more variables: UCSC_CpG_Islands_Name <chr>,#> #   Relation_to_UCSC_CpG_Island <chr>

Plot generated signatures

# adjust target names to match signature names# check generated signaturesplt<- signature_plot(cimpleg_result,train_data,train_targets,sample_id_column="gsm",true_label_column="cell_type")print(plt$plot)

Difference of means vs Sum of variances (dmsv) plots

basic plot

plt<- dmsv_plot(dat=train_data,target_vector=train_targets$neurons==1)print(plt)

plt<- diffmeans_sumvariance_plot(data=train_data,target_vector=train_targets$neurons==1)print(plt)

adding color, highlighting selected features

df_dmeansvar<- compute_diffmeans_sumvar(data=train_data,target_vector=train_targets$neurons==1)parab_param<-.7df_dmeansvar$is_selected<- select_features(x=df_dmeansvar$diff_means,y=df_dmeansvar$sum_variance,a=parab_param)

plt<- dmsv_plot(dat=df_dmeansvar,label_var1="Neurons",highlight_var="is_selected",display_var="is_selected",point_color="purple")print(plt)

plt<- diffmeans_sumvariance_plot(data=df_dmeansvar,label_var1="Neurons",color_all_points="purple",threshold_func=function(x,a) (a*x)^2,is_feature_selected_col="is_selected",func_factor=parab_param)print(plt)

labeling specific features

# labeling best signature found by CimpleGdf_dmeansvar$best_neuron_sig<- (df_dmeansvar$id%in%cimpleg_result$signatures["neurons"])plt<- dmsv_plot(dat=df_dmeansvar,label_var1="Neurons",highlight_var="is_selected",display_var="best_neuron_sig",point_color="red")print(plt)

Deconvolution plots

mini example with just 4 signatures

deconv_result<- run_deconvolution(cpg_obj=cimpleg_result,new_data=test_data)plt<- deconvolution_barplot(deconvoluted_data=deconv_result,meta_data=test_targets,sample_id="gsm",true_label="cell_type")print(plt$plot)

Benchmarking example

This example is a little more advanced

first lets create additional deconvolution results so that we can compare them

set.seed(42)cimpleg_hyper<- CimpleG(train_data=train_data,train_targets=train_targets,test_data=test_data,test_targets=test_targets,method="CimpleG",pred_type="hyper",target_columns= c("neurons","glia","blood_cells","fibroblasts"  ))#> Training for target 'neurons' with 'CimpleG' has finished.: 0.251 sec elapsed#> Training for target 'glia' with 'CimpleG' has finished.: 0.253 sec elapsed#> Training for target 'blood_cells' with 'CimpleG' has finished.: 0.29 sec elapsed#> Training for target 'fibroblasts' with 'CimpleG' has finished.: 0.268 sec elapseddeconv_hyper<- run_deconvolution(cpg_obj=cimpleg_hyper,new_data=test_data)set.seed(42)cimpleg_3sigs<- CimpleG(train_data=train_data,train_targets=train_targets,test_data=test_data,test_targets=test_targets,method="CimpleG",n_sigs=3,target_columns= c("neurons","glia","blood_cells","fibroblasts"  ))#> Training for target 'neurons' with 'CimpleG' has finished.: 0.315 sec elapsed#> Training for target 'glia' with 'CimpleG' has finished.: 0.296 sec elapsed#> Training for target 'blood_cells' with 'CimpleG' has finished.: 0.349 sec elapsed#> Training for target 'fibroblasts' with 'CimpleG' has finished.: 0.307 sec elapseddeconv_3sigs<- run_deconvolution(cpg_obj=cimpleg_3sigs,new_data=test_data)

let’s also create some simulated true values just so that we can compare all the results

remember this is just an example, the results themselves are meaningless!

deconv_3sigs$prop_3sigs<-deconv_3sigs$proportiondeconv_hyper$prop_hyper<-deconv_hyper$proportiondeconv_result$prop_cimpleg<-deconv_result$proportiondummy_deconvolution_data<-deconv_result|>dplyr::mutate(true_vals=proportion+ runif(nrow(deconv_result),min=-0.1,max=0.1))|>dplyr::select(cell_type,sample_id,prop_cimpleg,true_vals)|>dplyr::left_join(deconv_hyper|>dplyr::select(-proportion),by= c("sample_id","cell_type"))|>dplyr::left_join(deconv_3sigs|>dplyr::select(-proportion),by= c("sample_id","cell_type"))|>dplyr::mutate_if(is.numeric,function(x) {    ifelse(x<0,0,x)  })|>dplyr::mutate_if(is.numeric,function(x) {    ifelse(x>1,1,x)  })|>tibble::as_tibble()

let’s now make use of some plotting functions designed to compare deconvolution results

first we can check how the true values compare against the predicted values

scatter_plts<-CimpleG:::deconv_pred_obs_plot(deconv_df=dummy_deconvolution_data,true_values_col="true_vals",predicted_cols= c("prop_cimpleg","prop_hyper","prop_3sigs"),sample_id_col="sample_id",group_col="cell_type")scatter_panel<-scatter_plts|>patchwork::wrap_plots(ncol=1)print(scatter_panel)

now, more interestingly, we can see in detail and rank one of the measures used to evaluate the deconvolution results

rank_plts<-CimpleG:::deconv_ranking_plot(deconv_df=dummy_deconvolution_data,true_values_col="true_vals",predicted_cols= c("prop_cimpleg","prop_hyper","prop_3sigs"),sample_id_col="sample_id",group_col="cell_type",metrics="rmse")rank_panel<-list(rank_plts$perf_boxplt[[1]],rank_plts$nemenyi_plt[[1]])|>patchwork::wrap_plots()print(rank_panel)