Convert MAE to mlr task

Fun_MAE_to_taskFunc(MAE_obj, param.Y.name, param.covariates,
  param_positive_y_level, task_type)

Arguments

MAE_obj

MAE class

param.Y.name

Vector of dependent variable name

param.covariates

Vector of coaraiate variable(s) name

param_positive_y_level

if ClassifTask, value (character or numeric) to be considered as the positive factor outcome

Value

mlr's ClassifTask or RegrTask

Details

In case of individual MAE assay (omic) with multiple sub-assays, only first sub-assay will be used. MAE's helpers functions longFormat and wideFormat may not be best candidates, since mlr's functional data require complete subject structure for all 'assays' ('functionals'). this can be achieved by either removing non-complete subjects, or by creating dummy entities with NA. (which can later be imputed if required).

Either ClassifTask or RegrTask will be returned, based on the type of the param.Y.name variable

Examples

data(miniACC, package = 'MultiAssayExperiment') # ExpressionSet miniACC
#> Loading required package: MultiAssayExperiment
#> #> Attaching package: 'MultiAssayExperiment'
#> The following object is masked _by_ '.GlobalEnv': #> #> miniACC
#> An object of class "MultiAssayExperiment" #> Slot "ExperimentList": #> ExperimentList class object of length 5: #> [1] RNASeq2GeneNorm: SummarizedExperiment with 198 rows and 79 columns #> [2] gistict: SummarizedExperiment with 198 rows and 90 columns #> [3] RPPAArray: SummarizedExperiment with 33 rows and 46 columns #> [4] Mutations: matrix with 97 rows and 90 columns #> [5] miRNASeqGene: SummarizedExperiment with 471 rows and 80 columns #> #> Slot "colData": #> DataFrame with 92 rows and 30 columns #> patientID years_to_birth vital_status days_to_death #> <character> <integer> <integer> <integer> #> TCGA-OR-A5J1 TCGA-OR-A5J1 58 1 1355 #> TCGA-OR-A5J2 TCGA-OR-A5J2 44 1 1677 #> TCGA-OR-A5J3 TCGA-OR-A5J3 23 0 NA #> TCGA-OR-A5J4 TCGA-OR-A5J4 23 1 423 #> TCGA-OR-A5J5 TCGA-OR-A5J5 30 1 365 #> ... ... ... ... ... #> TCGA-PK-A5H9 TCGA-PK-A5H9 27 0 NA #> TCGA-PK-A5HA TCGA-PK-A5HA 63 0 NA #> TCGA-PK-A5HB TCGA-PK-A5HB 63 0 NA #> TCGA-PK-A5HC TCGA-PK-A5HC 44 0 NA #> TCGA-P6-A5OG TCGA-P6-A5OG 45 1 383 #> days_to_last_followup tumor_tissue_site pathologic_stage #> <integer> <character> <character> #> TCGA-OR-A5J1 NA adrenal stage ii #> TCGA-OR-A5J2 NA adrenal stage iv #> TCGA-OR-A5J3 2091 adrenal stage iii #> TCGA-OR-A5J4 NA adrenal stage iv #> TCGA-OR-A5J5 NA adrenal stage iii #> ... ... ... ... #> TCGA-PK-A5H9 616 adrenal stage ii #> TCGA-PK-A5HA 1201 adrenal stage i #> TCGA-PK-A5HB 1293 adrenal NA #> TCGA-PK-A5HC 679 adrenal stage iii #> TCGA-P6-A5OG NA adrenal stage iv #> pathology_T_stage pathology_N_stage gender #> <character> <character> <character> #> TCGA-OR-A5J1 t2 n0 male #> TCGA-OR-A5J2 t3 n0 female #> TCGA-OR-A5J3 t3 n0 female #> TCGA-OR-A5J4 t3 n1 female #> TCGA-OR-A5J5 t4 n0 male #> ... ... ... ... #> TCGA-PK-A5H9 t2 n0 female #> TCGA-PK-A5HA t1 n0 male #> TCGA-PK-A5HB NA NA male #> TCGA-PK-A5HC t4 n0 female #> TCGA-P6-A5OG t4 n0 female #> date_of_initial_pathologic_diagnosis radiation_therapy #> <integer> <character> #> TCGA-OR-A5J1 2000 no #> TCGA-OR-A5J2 2004 no #> TCGA-OR-A5J3 2008 no #> TCGA-OR-A5J4 2000 no #> TCGA-OR-A5J5 2000 no #> ... ... ... #> TCGA-PK-A5H9 2012 no #> TCGA-PK-A5HA 2011 no #> TCGA-PK-A5HB 2003 yes #> TCGA-PK-A5HC 2011 no #> TCGA-P6-A5OG 2011 no #> histological_type residual_tumor #> <character> <character> #> TCGA-OR-A5J1 adrenocortical carcinoma- usual type r0 #> TCGA-OR-A5J2 adrenocortical carcinoma- usual type r2 #> TCGA-OR-A5J3 adrenocortical carcinoma- usual type r0 #> TCGA-OR-A5J4 adrenocortical carcinoma- usual type r2 #> TCGA-OR-A5J5 adrenocortical carcinoma- usual type r2 #> ... ... ... #> TCGA-PK-A5H9 adrenocortical carcinoma- usual type r0 #> TCGA-PK-A5HA adrenocortical carcinoma- usual type r0 #> TCGA-PK-A5HB adrenocortical carcinoma- usual type NA #> TCGA-PK-A5HC adrenocortical carcinoma- usual type r1 #> TCGA-P6-A5OG adrenocortical carcinoma- usual type r2 #> number_of_lymph_nodes race ethnicity #> <integer> <character> <character> #> TCGA-OR-A5J1 NA white NA #> TCGA-OR-A5J2 0 white hispanic or latino #> TCGA-OR-A5J3 0 white hispanic or latino #> TCGA-OR-A5J4 2 white hispanic or latino #> TCGA-OR-A5J5 NA white hispanic or latino #> ... ... ... ... #> TCGA-PK-A5H9 NA asian not hispanic or latino #> TCGA-PK-A5HA 0 NA NA #> TCGA-PK-A5HB NA NA NA #> TCGA-PK-A5HC 0 asian not hispanic or latino #> TCGA-P6-A5OG 0 white not hispanic or latino #> Histology C1A.C1B mRNA_K4 #> <character> <character> <character> #> TCGA-OR-A5J1 Usual Type C1A steroid-phenotype-high+proliferation #> TCGA-OR-A5J2 Usual Type C1A steroid-phenotype-high+proliferation #> TCGA-OR-A5J3 Usual Type C1A steroid-phenotype-high #> TCGA-OR-A5J4 Usual Type NA NA #> TCGA-OR-A5J5 Usual Type C1A steroid-phenotype-high #> ... ... ... ... #> TCGA-PK-A5H9 Usual Type C1B steroid-phenotype-low #> TCGA-PK-A5HA Usual Type C1B steroid-phenotype-low #> TCGA-PK-A5HB Usual Type C1A steroid-phenotype-high #> TCGA-PK-A5HC Usual Type NA NA #> TCGA-P6-A5OG NA NA NA #> MethyLevel miRNA.cluster SCNA.cluster protein.cluster #> <character> <character> <character> <integer> #> TCGA-OR-A5J1 CIMP-high miRNA_1 Quiet NA #> TCGA-OR-A5J2 CIMP-low miRNA_1 Noisy 1 #> TCGA-OR-A5J3 CIMP-intermediate miRNA_6 Chromosomal 3 #> TCGA-OR-A5J4 CIMP-high miRNA_6 Chromosomal NA #> TCGA-OR-A5J5 CIMP-intermediate miRNA_2 Chromosomal NA #> ... ... ... ... ... #> TCGA-PK-A5H9 CIMP-low miRNA_5 Quiet 3 #> TCGA-PK-A5HA CIMP-high miRNA_5 Chromosomal 2 #> TCGA-PK-A5HB CIMP-high miRNA_6 Noisy NA #> TCGA-PK-A5HC NA NA Chromosomal NA #> TCGA-P6-A5OG NA NA NA NA #> COC OncoSign purity ploidy genome_doublings #> <character> <character> <numeric> <numeric> <integer> #> TCGA-OR-A5J1 COC3 CN2 0.9 1.95 0 #> TCGA-OR-A5J2 COC3 TP53/NF1 0.89 1.31 0 #> TCGA-OR-A5J3 COC2 CN2 0.93 1.25 0 #> TCGA-OR-A5J4 NA CN1 0.87 2.6 1 #> TCGA-OR-A5J5 COC2 TP53/NF1 0.93 2.75 1 #> ... ... ... ... ... ... #> TCGA-PK-A5H9 COC1 TP53/NF1 0.79 2 0 #> TCGA-PK-A5HA COC1 CN2 0.83 1.69 0 #> TCGA-PK-A5HB COC3 TP53/NF1 0.88 1.64 0 #> TCGA-PK-A5HC NA TP53/NF1 0.59 2.53 1 #> TCGA-P6-A5OG NA NA NA NA NA #> ADS #> <numeric> #> TCGA-OR-A5J1 -0.08 #> TCGA-OR-A5J2 -0.84 #> TCGA-OR-A5J3 1.18 #> TCGA-OR-A5J4 NA #> TCGA-OR-A5J5 -1 #> ... ... #> TCGA-PK-A5H9 -0.85 #> TCGA-PK-A5HA -1.49 #> TCGA-PK-A5HB -0.31 #> TCGA-PK-A5HC NA #> TCGA-P6-A5OG NA #> #> Slot "sampleMap": #> DataFrame with 385 rows and 3 columns #> assay primary colname #> <factor> <character> <character> #> 1 RNASeq2GeneNorm TCGA-OR-A5J1 TCGA-OR-A5J1-01A-11R-A29S-07 #> 2 RNASeq2GeneNorm TCGA-OR-A5J2 TCGA-OR-A5J2-01A-11R-A29S-07 #> 3 RNASeq2GeneNorm TCGA-OR-A5J3 TCGA-OR-A5J3-01A-11R-A29S-07 #> 4 RNASeq2GeneNorm TCGA-OR-A5J5 TCGA-OR-A5J5-01A-11R-A29S-07 #> 5 RNASeq2GeneNorm TCGA-OR-A5J6 TCGA-OR-A5J6-01A-31R-A29S-07 #> ... ... ... ... #> 381 miRNASeqGene TCGA-PA-A5YG TCGA-PA-A5YG-01A-11R-A29W-13 #> 382 miRNASeqGene TCGA-PK-A5H8 TCGA-PK-A5H8-01A-11R-A29W-13 #> 383 miRNASeqGene TCGA-PK-A5H9 TCGA-PK-A5H9-01A-11R-A29W-13 #> 384 miRNASeqGene TCGA-PK-A5HA TCGA-PK-A5HA-01A-11R-A29W-13 #> 385 miRNASeqGene TCGA-PK-A5HB TCGA-PK-A5HB-01A-11R-A29W-13 #> #> Slot "metadata": #> $title #> [1] "Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma" #> #> $PMID #> [1] "27165744" #> #> $sourceURL #> [1] "http://s3.amazonaws.com/multiassayexperiments/accMAEO.rds" #> #> $RPPAfeatureDataURL #> [1] "http://genomeportal.stanford.edu/pan-tcga/show_target_selection_file?filename=Allprotein.txt" #> #> $colDataExtrasURL #> [1] "http://www.cell.com/cms/attachment/2062093088/2063584534/mmc3.xlsx" #> #> #> Slot "drops": #> list() #>
Fun_MAE_to_taskFunc(miniACC, param.Y.name = 'vital_status', param.covariates = c('gender','days_to_death'), param_positive_y_level = '1')
#> Error in MAE_obj %>% colData %>% data.frame %>% rownames_to_column("primary"): could not find function "%>%"