From 74df94e788f03e620b7833d938837a7b0b6522f9 Mon Sep 17 00:00:00 2001 From: Cesaire Joris Kuete Fouodo Date: Tue, 26 Nov 2024 20:56:01 +0100 Subject: [PATCH] Add data description --- R/multi_omics.R | 23 +++ README.Rmd | 74 ++++--- README.md | 524 +++++++++++++++++++++++++++++++++++++++--------- 3 files changed, 493 insertions(+), 128 deletions(-) create mode 100644 R/multi_omics.R diff --git a/R/multi_omics.R b/R/multi_omics.R new file mode 100644 index 0000000..2ef8338 --- /dev/null +++ b/R/multi_omics.R @@ -0,0 +1,23 @@ +#' Simulated multiomics data for 70 training participants and 23 testing participants, +#' each with an effect size of 20 on each layer. Each layer includes 50 participants for +#' training and 20 for testing. Participants do not perfectly overlap across layers. +#' The simulation is based on the R package \code{interSIM}. + +#' +#' The dataset is a list containing training and testing data, +#' called \code{training} and \code{testing} respectively. Each data is a list +#' containing the following multi_omics at each layer. +#' +#' \itemize{ +#' \item \code{methylation}: A \code{data.frame} containing the simulated methylation dataset. +#' \item \code{genexpr} : A \code{data.frame} containing the gene expression dataset. +#' \item \code{proteinexpr}: A \code{data.frame} containing the protein expression dataset. +#' \item \code{target}: A \code{data.frame} with two columns, containing patient IDs and values of target variable. +#' } +#' +#' @docType data +#' @keywords datasets +#' @name multi_omics +#' @usage data(multi_omics) +#' @format A list with training and testing data contaning methylation, gene expressions and protein expressions data. +"multi_omics" diff --git a/README.Rmd b/README.Rmd index ae342f7..ea6d034 100644 --- a/README.Rmd +++ b/README.Rmd @@ -63,10 +63,10 @@ Two types of data were simulated: training and testing datasets. Each consists o ```{r data_exam, include=TRUE, eval=TRUE} -data("entities") +data("multi_omics") # This is a list containing two lists of data: training and test. -# Each sublist contains three entities. -str(object = entities, max.level = 2L) +# Each sublist contains three omics data. +str(object = multi_omics, max.level = 2L) ``` Variable selection, training and prediction are the main functionalities of `fuseMLR`. We can perform variable selection, train and fuse models for training studies, and predict new studies. @@ -79,8 +79,8 @@ We need to set up training resources. training <- createTraining(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target, - verbose = FALSE) + target_df = multi_omics$training$target, + verbose = TRUE) print(training) ``` @@ -90,50 +90,56 @@ print(training) # Create gene expression layer createTrainLayer(training = training, train_layer_id = "geneexpr", - train_data = entities$training$geneexpr, + train_data = multi_omics$training$geneexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, mtry = 3L, - probability = TRUE), + probability = TRUE, + na.action = "na.learn"), lrner_package = "ranger", lrn_fct = "ranger", param_train_list = list(probability = TRUE, - mtry = 1L), + mtry = 1L, + na.action = "na.learn"), param_pred_list = list(), - na_rm = TRUE) + na_rm = FALSE) # Create gene protein abundance layer createTrainLayer(training = training, train_layer_id = "proteinexpr", - train_data = entities$training$proteinexpr, + train_data = multi_omics$training$proteinexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, mtry = 3L, - probability = TRUE), + probability = TRUE, + na.action = "na.learn"), lrner_package = "ranger", lrn_fct = "ranger", param_train_list = list(probability = TRUE, - mtry = 1L), + mtry = 1L, + na.action = "na.learn"), param_pred_list = list(), - na_rm = TRUE) + na_rm = FALSE) # Create methylation layer createTrainLayer(training = training, train_layer_id = "methylation", - train_data = entities$training$proteinexpr, + train_data = multi_omics$training$proteinexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, mtry = 3L, - probability = TRUE), + probability = TRUE, + na.action = "na.learn"), lrner_package = "ranger", lrn_fct = "ranger", param_train_list = list(probability = TRUE, - mtry = 1L), + mtry = 1L, + na.action = "na.learn"), param_pred_list = list(), - na_rm = TRUE) + na_rm = FALSE) ``` - Also add a meta layer. @@ -146,7 +152,7 @@ createTrainMetaLayer(training = training, lrn_fct = "weightedMeanLearner", param_train_list = list(), param_pred_list = list(), - na_rm = FALSE) + na_action = "na.impute") ``` - An upset plot of the training data: Visualize patient overlap across layers. @@ -162,8 +168,7 @@ Perform variable selection on our training resources ```{r varsel, include=TRUE, eval=TRUE} # Variable selection set.seed(5467) -var_sel_res <- varSelection(training = training, - verbose = FALSE) +var_sel_res <- varSelection(training = training) print(var_sel_res) ``` @@ -178,10 +183,8 @@ set.seed(5462) training <- fusemlr(training = training, use_var_sel = TRUE, resampling_method = NULL, - resampling_arg = list(y = entities$training$target$disease, - k = 10L), - impute = TRUE, - verbose = FALSE) + resampling_arg = list(y = multi_omics$training$target$disease, + k = 10L)) print(training) # See also summary(training) @@ -206,17 +209,17 @@ testing <- createTesting(id = "testing", # Create gene expression layer createTestLayer(testing = testing, test_layer_id = "geneexpr", - test_data = entities$testing$geneexpr) + test_data = multi_omics$testing$geneexpr) # Create gene protein abundance layer createTestLayer(testing = testing, test_layer_id = "proteinexpr", - test_data = entities$testing$proteinexpr) + test_data = multi_omics$testing$proteinexpr) # Create methylation layer createTestLayer(testing = testing, test_layer_id = "methylation", - test_data = entities$testing$proteinexpr) + test_data = multi_omics$testing$proteinexpr) ``` - An upset plot of the training data: Visualize patient overlap across layers. @@ -237,7 +240,7 @@ print(predictions) ```{r performance_all, include=TRUE, eval=TRUE} pred_values <- predictions$predicted_values actual_pred <- merge(x = pred_values, - y = entities$testing$target, + y = multi_omics$testing$target, by = "IDS", all.y = TRUE) x <- as.integer(actual_pred$disease == 2L) @@ -275,12 +278,10 @@ We distinguish common supervised learning arguments from method specific argumen The interface approach leverages the arguments in ```createTrainLayer()``` to map the argument names of the original learning function. In the example below, the gene expression layer is re-created using the ```svm``` (Support Vector Machine) function from the ```e1071``` package as the learner. A discrepancy arises in the argument names of the ```predict.svm``` function, which uses ```object``` and ```newdata```. ```{r interface, include=TRUE, eval=TRUE} -# Remove the current gene expression layer from training -removeLayer(training = training, layer_id = "geneexpr") # Re-create the gene expression layer with support vector machine as learner. createTrainLayer(training = training, train_layer_id = "geneexpr", - train_data = entities$training$geneexpr, + train_data = multi_omics$training$geneexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, @@ -304,12 +305,10 @@ createTrainLayer(training = training, ) # Variable selection set.seed(5467) -var_sel_res <- varSelection(training = training, - verbose = FALSE) +var_sel_res <- varSelection(training = training) set.seed(5462) training <- fusemlr(training = training, - use_var_sel = TRUE, - verbose = FALSE) + use_var_sel = TRUE) print(training) ``` @@ -355,11 +354,10 @@ createTrainMetaLayer(training = training, lrner_package = NULL, lrn_fct = "mylasso", param_train_list = list(nlambda = 100L), - na_rm = TRUE) + na_action = "na.impute") set.seed(5462) training <- fusemlr(training = training, - use_var_sel = TRUE, - verbose = FALSE) + use_var_sel = TRUE) print(training) ``` diff --git a/README.md b/README.md index d6c05a2..7c20032 100644 --- a/README.md +++ b/README.md @@ -98,10 +98,10 @@ typically expected in reality. The data simulation code is available [here](https://github.com/imbs-hl/fuseMLR/blob/master/test_code/build_data.R). ``` r -data("entities") +data("multi_omics") # This is a list containing two lists of data: training and test. -# Each sublist contains three entities. -str(object = entities, max.level = 2L) +# Each sublist contains three omics data. +str(object = multi_omics, max.level = 2L) ``` ## List of 2 @@ -128,16 +128,16 @@ We need to set up training resources. training <- createTraining(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target, - verbose = FALSE) + target_df = multi_omics$training$target, + verbose = TRUE) print(training) ``` ## Training : training + ## Problem typ : classification ## Status : Not trained ## Number of layers: 0 ## Layers trained : 0 - ## n : 70 - Prepare training layers: Training layers contain `TrainData`, `Lrner` and `VarSel` objects. Therefore arguments to instantiate those object @@ -151,73 +151,85 @@ print(training) # Create gene expression layer createTrainLayer(training = training, train_layer_id = "geneexpr", - train_data = entities$training$geneexpr, + train_data = multi_omics$training$geneexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, mtry = 3L, - probability = TRUE), + probability = TRUE, + na.action = "na.learn"), lrner_package = "ranger", lrn_fct = "ranger", param_train_list = list(probability = TRUE, - mtry = 1L), + mtry = 1L, + na.action = "na.learn"), param_pred_list = list(), - na_rm = TRUE) + na_rm = FALSE) ``` ## Training : training + ## Problem typ : classification ## Status : Not trained ## Number of layers: 1 ## Layers trained : 0 - ## n : 70 + ## p : 131 + ## n : 50 ``` r # Create gene protein abundance layer createTrainLayer(training = training, train_layer_id = "proteinexpr", - train_data = entities$training$proteinexpr, + train_data = multi_omics$training$proteinexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, mtry = 3L, - probability = TRUE), + probability = TRUE, + na.action = "na.learn"), lrner_package = "ranger", lrn_fct = "ranger", param_train_list = list(probability = TRUE, - mtry = 1L), + mtry = 1L, + na.action = "na.learn"), param_pred_list = list(), - na_rm = TRUE) + na_rm = FALSE) ``` ## Training : training + ## Problem typ : classification ## Status : Not trained ## Number of layers: 2 ## Layers trained : 0 - ## n : 70 + ## p : 131 | 160 + ## n : 50 | 50 ``` r # Create methylation layer createTrainLayer(training = training, train_layer_id = "methylation", - train_data = entities$training$proteinexpr, + train_data = multi_omics$training$proteinexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, mtry = 3L, - probability = TRUE), + probability = TRUE, + na.action = "na.learn"), lrner_package = "ranger", lrn_fct = "ranger", param_train_list = list(probability = TRUE, - mtry = 1L), + mtry = 1L, + na.action = "na.learn"), param_pred_list = list(), - na_rm = TRUE) + na_rm = FALSE) ``` ## Training : training + ## Problem typ : classification ## Status : Not trained ## Number of layers: 3 ## Layers trained : 0 - ## n : 70 + ## p : 131 | 160 | 160 + ## n : 50 | 50 | 50 - Also add a meta layer. @@ -229,14 +241,16 @@ createTrainMetaLayer(training = training, lrn_fct = "weightedMeanLearner", param_train_list = list(), param_pred_list = list(), - na_rm = FALSE) + na_action = "na.impute") ``` ## Training : training + ## Problem typ : classification ## Status : Not trained ## Number of layers: 4 ## Layers trained : 0 - ## n : 70 + ## p : 131 | 160 | 160 + ## n : 50 | 50 | 50 - An upset plot of the training data: Visualize patient overlap across layers. @@ -254,8 +268,22 @@ Perform variable selection on our training resources ``` r # Variable selection set.seed(5467) -var_sel_res <- varSelection(training = training, - verbose = FALSE) +var_sel_res <- varSelection(training = training) +``` + + ## Variable selection on layer geneexpr started. + + ## Variable selection on layer geneexpr done. + + ## Variable selection on layer proteinexpr started. + + ## Variable selection on layer proteinexpr done. + + ## Variable selection on layer methylation started. + + ## Variable selection on layer methylation done. + +``` r print(var_sel_res) ``` @@ -295,19 +323,173 @@ set.seed(5462) training <- fusemlr(training = training, use_var_sel = TRUE, resampling_method = NULL, - resampling_arg = list(y = entities$training$target$disease, - k = 10L), - impute = TRUE, - verbose = FALSE) + resampling_arg = list(y = multi_omics$training$target$disease, + k = 10L)) +``` + + ## Training for fold 1. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 2. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 3. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 4. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 5. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + ## Training for fold 6. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 7. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 8. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 9. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 10. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + +``` r print(training) ``` ## Training : training + ## Problem typ : classification ## Status : Trained ## Number of layers: 4 ## Layers trained : 4 - ## n : 70 + ## p : 131 | 160 | 160 | 3 + ## n : 50 | 50 | 50 | 64 ``` r # See also summary(training) @@ -337,31 +519,37 @@ testing <- createTesting(id = "testing", # Create gene expression layer createTestLayer(testing = testing, test_layer_id = "geneexpr", - test_data = entities$testing$geneexpr) + test_data = multi_omics$testing$geneexpr) ``` ## Testing : testing ## Number of layers: 1 + ## p : 131 + ## n : 20 ``` r # Create gene protein abundance layer createTestLayer(testing = testing, test_layer_id = "proteinexpr", - test_data = entities$testing$proteinexpr) + test_data = multi_omics$testing$proteinexpr) ``` ## Testing : testing ## Number of layers: 2 + ## p : 131 | 160 + ## n : 20 | 20 ``` r # Create methylation layer createTestLayer(testing = testing, test_layer_id = "methylation", - test_data = entities$testing$proteinexpr) + test_data = multi_omics$testing$proteinexpr) ``` ## Testing : testing ## Number of layers: 3 + ## p : 131 | 160 | 160 + ## n : 20 | 20 | 20 - An upset plot of the training data: Visualize patient overlap across layers. @@ -384,34 +572,34 @@ print(predictions) ## Nb. layers : 4 ## ## $predicted_values - ## IDS geneexpr proteinexpr methylation meta_layer - ## 1 patient1 0.8001571 0.93959762 0.92495794 0.6890614 - ## 2 patient10 0.2715532 0.51328056 0.48941032 0.5052011 - ## 3 patient16 0.6811413 0.51328056 0.48941032 0.9140717 - ## 4 patient17 0.3817786 0.30925873 0.36974444 0.4484350 - ## 5 patient2 0.5207353 0.56663333 0.66165238 0.7033941 - ## 6 patient23 0.4298159 0.63428413 0.92495794 0.4410852 - ## 7 patient25 0.2796373 0.40415000 0.48941032 0.3569530 - ## 8 patient27 0.3638444 0.51328056 0.48941032 0.3509955 - ## 9 patient29 0.3765897 0.45992778 0.24329444 0.4032013 - ## 10 patient31 0.5207353 0.27210000 0.15999683 0.5766641 - ## 11 patient39 0.5207353 0.08805476 0.04742698 0.7415073 - ## 12 patient43 0.3011865 0.51328056 0.48941032 0.4768056 - ## 13 patient44 0.4087905 0.51328056 0.48941032 0.8973592 - ## 14 patient46 0.5207353 0.21791667 0.48941032 0.5488790 - ## 15 patient52 0.5085119 0.12568889 0.04742698 0.5486128 - ## 16 patient54 0.7875341 0.51328056 0.48941032 0.4649553 - ## 17 patient58 0.5329587 0.77942222 0.59402937 0.6451195 - ## 18 patient59 0.5207353 0.13636111 0.22532778 0.1973598 - ## 19 patient60 0.7329190 0.83852619 0.65352381 0.4406220 - ## 20 patient62 0.7837778 0.96909206 0.95261508 0.8848065 - ## 21 patient72 0.6949579 0.94951984 0.95594841 0.1869428 - ## 25 patient74 0.5458302 0.68164921 0.13405079 0.3992720 - ## 26 patient76 0.6821508 0.51328056 0.48941032 0.5353148 - ## 27 patient77 0.4781778 0.45292381 0.57492778 0.2957376 - ## 31 patient8 0.7393611 0.87369683 0.51296032 0.2653453 - ## 33 patient87 0.5207353 0.30864365 0.04329365 0.2709699 - ## 34 patient97 0.5207353 0.74017381 0.34702778 0.5900609 + ## IDS geneexpr proteinexpr methylation meta_layer + ## 1 participant1 0.8001571 0.93959762 0.92495794 0.5913131 + ## 2 participant10 0.2715532 0.51328056 0.48941032 0.4917420 + ## 3 participant16 0.6811413 0.51328056 0.48941032 0.8707412 + ## 4 participant17 0.3817786 0.30925873 0.36974444 0.3996534 + ## 5 participant2 0.5207353 0.56663333 0.66165238 0.7283805 + ## 6 participant23 0.4298159 0.63428413 0.92495794 0.4959176 + ## 7 participant25 0.2796373 0.40415000 0.48941032 0.3712966 + ## 8 participant27 0.3638444 0.51328056 0.48941032 0.3592147 + ## 9 participant29 0.3765897 0.45992778 0.24329444 0.3585116 + ## 10 participant31 0.5207353 0.27210000 0.15999683 0.6485929 + ## 11 participant39 0.5207353 0.08805476 0.04742698 0.7452092 + ## 12 participant43 0.3011865 0.51328056 0.48941032 0.4547310 + ## 13 participant44 0.4087905 0.51328056 0.48941032 0.8651174 + ## 14 participant46 0.5207353 0.21791667 0.48941032 0.5946519 + ## 15 participant52 0.5085119 0.12568889 0.04742698 0.5941352 + ## 16 participant54 0.7875341 0.51328056 0.48941032 0.4317251 + ## 17 participant58 0.5329587 0.77942222 0.59402937 0.6139244 + ## 18 participant59 0.5207353 0.13636111 0.22532778 0.3050300 + ## 19 participant60 0.7329190 0.83852619 0.65352381 0.3844854 + ## 20 participant62 0.7837778 0.96909206 0.95261508 0.8205853 + ## 21 participant72 0.6949579 0.94951984 0.95594841 0.3009025 + ## 25 participant74 0.5458302 0.68164921 0.13405079 0.4305326 + ## 26 participant76 0.6821508 0.51328056 0.48941032 0.5444192 + ## 27 participant77 0.4781778 0.45292381 0.57492778 0.3755746 + ## 31 participant8 0.7393611 0.87369683 0.51296032 0.3608910 + ## 33 participant87 0.5207353 0.30864365 0.04329365 0.3519856 + ## 34 participant97 0.5207353 0.74017381 0.34702778 0.5633052 - Prediction performances for layer-specific available patients, and all patients on the meta layer. @@ -419,7 +607,7 @@ print(predictions) ``` r pred_values <- predictions$predicted_values actual_pred <- merge(x = pred_values, - y = entities$testing$target, + y = multi_omics$testing$target, by = "IDS", all.y = TRUE) x <- as.integer(actual_pred$disease == 2L) @@ -434,7 +622,7 @@ print(perf_estimated) ``` ## geneexpr proteinexpr methylation meta_layer - ## 0.1564895 0.1831100 0.2382557 0.2250521 + ## 0.3093583 0.3448970 0.2932064 0.2993118 - Prediction performances for overlapping individuals. @@ -450,7 +638,7 @@ print(perf_overlapping) ``` ## geneexpr proteinexpr methylation meta_layer - ## 0.1564895 0.1831100 0.2382557 0.2250521 + ## 0.3093583 0.3448970 0.2932064 0.2993118 Note that our example is based on simulated data for usage illustration; only one run is not enough to appreciate the performances of our models. @@ -478,25 +666,11 @@ example below, the gene expression layer is re-created using the `svm` learner. A discrepancy arises in the argument names of the `predict.svm` function, which uses `object` and `newdata`. -``` r -# Remove the current gene expression layer from training -removeLayer(training = training, layer_id = "geneexpr") -``` - - ## Warning in training$removeLayer(id = layer_id): training was already trained. - ## Do not forget to train it again to update its meta layer. - - ## Training : training - ## Status : Trained - ## Number of layers: 3 - ## Layers trained : 3 - ## n : 70 - ``` r # Re-create the gene expression layer with support vector machine as learner. createTrainLayer(training = training, train_layer_id = "geneexpr", - train_data = entities$training$geneexpr, + train_data = multi_omics$training$geneexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, @@ -521,29 +695,200 @@ createTrainLayer(training = training, ``` ## Training : training + ## Problem typ : classification ## Status : Trained ## Number of layers: 4 - ## Layers trained : 3 - ## n : 70 + ## Layers trained : 4 + ## p : 131 | 160 | 160 | 3 + ## n : 50 | 50 | 50 | 64 ``` r # Variable selection set.seed(5467) -var_sel_res <- varSelection(training = training, - verbose = FALSE) +var_sel_res <- varSelection(training = training) +``` + + ## Variable selection on layer geneexpr started. + + ## Variable selection on layer geneexpr done. + + ## Variable selection on layer proteinexpr started. + + ## Variable selection on layer proteinexpr done. + + ## Variable selection on layer methylation started. + + ## Variable selection on layer methylation done. + +``` r set.seed(5462) training <- fusemlr(training = training, - use_var_sel = TRUE, - verbose = FALSE) + use_var_sel = TRUE) +``` + + ## Training for fold 1. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 2. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 3. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 4. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 5. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 6. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 7. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 8. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 9. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training for fold 10. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + + ## Training on layer geneexpr started. + + ## Training on layer geneexpr done. + + ## Training on layer proteinexpr started. + + ## Training on layer proteinexpr done. + + ## Training on layer methylation started. + + ## Training on layer methylation done. + +``` r print(training) ``` ## Training : training + ## Problem typ : classification ## Status : Trained ## Number of layers: 4 - ## Layers trained : 4 - ## n : 70 + ## Layers trained : 5 + ## p : 131 | 160 | 160 | 3 + ## n : 50 | 50 | 50 | 64 ## Wrapping @@ -587,11 +932,10 @@ createTrainMetaLayer(training = training, lrner_package = NULL, lrn_fct = "mylasso", param_train_list = list(nlambda = 100L), - na_rm = TRUE) + na_action = "na.impute") set.seed(5462) training <- fusemlr(training = training, - use_var_sel = TRUE, - verbose = FALSE) + use_var_sel = TRUE) print(training) ```