This function imputes assays values inside a MultiAssayExperiment

These function allow the user to enter a MultiAssayExperiment and impute all the NA values inside assays.

Usage

imputeAssay(multiassayexperiment, i = 1, ...)

Arguments

multiassayexperiment

A MultiAssayExperiment with genes in the rows, samples in the columns

i

A numeric, logical, or character vector indicating the assays to perform imputation on (default 1L)

...

Arguments passed on to impute::impute.knn

data: An expression matrix with genes in the rows, samples in the columns
k: Number of neighbors to be used in the imputation (default=10)
rowmax: The maximum percent missing data allowed in any row (default 50%). For any rows with more than rowmax% missing are imputed using the overall mean per sample.
colmax: The maximum percent missing data allowed in any column (default 80%). If any column has more than colmax% missing data, the program halts and reports an error.
maxp: The largest block of genes imputed using the knn algorithm inside impute.knn (default 1500); larger blocks are divided by two-means clustering (recursively) prior to imputation. If maxp=p, only knn imputation is done.
rng.seed: The seed used for the random number generator (default 362436069) for reproducibility.

Value

A MultiAssayExperiment with imputed assays values

Examples


example(getSubtypeMap)
#> 
#> gtSbtM> library(curatedTCGAData)
#> 
#> gtSbtM> gbm <- curatedTCGAData("GBM", c("RPPA*", "CNA*"), version = "2.0.1", FALSE)
#> Querying and downloading: GBM_CNACGH_CGH_hg_244a-20160128
#> see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation
#> loading from cache
#> Querying and downloading: GBM_CNACGH_CGH_hg_415k_g4124a-20160128
#> see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation
#> loading from cache
#> Querying and downloading: GBM_CNASNP-20160128
#> see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation
#> loading from cache
#> Querying and downloading: GBM_RPPAArray-20160128
#> see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation
#> loading from cache
#> Querying and downloading: GBM_colData-20160128
#> see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation
#> loading from cache
#> Querying and downloading: GBM_metadata-20160128
#> see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation
#> loading from cache
#> Querying and downloading: GBM_sampleMap-20160128
#> see ?curatedTCGAData and browseVignettes('curatedTCGAData') for documentation
#> loading from cache
#> harmonizing input:
#>   removing 5922 sampleMap rows not in names(experiments)
#> 
#> gtSbtM> getSubtypeMap(gbm)
#>          GBM_annotations                          GBM_subtype
#> 1             Patient_ID                                 Case
#> 2   methylation_subtypes                 MGMT promoter status
#> 3      mutation_subtypes                    IDH/codel subtype
#> 4  histological_subtypes                            Histology
#> 5          mrna_subtypes                     Original Subtype
#> 6          mrna_subtypes                Transcriptome Subtype
#> 7          mrna_subtypes    Pan-Glioma RNA Expression Cluster
#> 8          mrna_subtypes  IDH-specific RNA Expression Cluster
#> 9   methylation_subtypes   Pan-Glioma DNA Methylation Cluster
#> 10  methylation_subtypes IDH-specific DNA Methylation Cluster
#> 11  methylation_subtypes   Supervised DNA Methylation Cluster
#> 12  methylation_subtypes          Random Forest Sturm Cluster
#> 13      protein_subtypes                         RPPA cluster
#> 
#> gtSbtM> sampleTables(gbm)
#> $`GBM_CNACGH_CGH_hg_244a-20160128`
#> 
#>  01  10  11 
#> 267 145  26 
#> 
#> $`GBM_CNACGH_CGH_hg_415k_g4124a-20160128`
#> 
#>  01  10 
#> 169 169 
#> 
#> $`GBM_CNASNP-20160128`
#> 
#>  01  02  10  11 
#> 577  13 488  26 
#> 
#> $`GBM_RPPAArray-20160128`
#> 
#>  01  02 
#> 233  11 
#> 
#> 
#> gtSbtM> TCGAsplitAssays(gbm, c("01", "10"))
#> Warning: Some 'sampleCodes' not found in assays
#> Warning: Inconsistent barcode lengths: 28, 27
#> A MultiAssayExperiment object of 7 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 7:
#>  [1] 01_GBM_CNACGH_CGH_hg_244a-20160128: RaggedExperiment with 81512 rows and 267 columns
#>  [2] 10_GBM_CNACGH_CGH_hg_244a-20160128: RaggedExperiment with 81512 rows and 145 columns
#>  [3] 01_GBM_CNACGH_CGH_hg_415k_g4124a-20160128: RaggedExperiment with 57975 rows and 169 columns
#>  [4] 10_GBM_CNACGH_CGH_hg_415k_g4124a-20160128: RaggedExperiment with 57975 rows and 169 columns
#>  [5] 01_GBM_CNASNP-20160128: RaggedExperiment with 602338 rows and 577 columns
#>  [6] 10_GBM_CNASNP-20160128: RaggedExperiment with 602338 rows and 488 columns
#>  [7] 01_GBM_RPPAArray-20160128: SummarizedExperiment with 208 rows and 233 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files
#> 
#> gtSbtM> getClinicalNames("COAD")
#>  [1] "years_to_birth"                      
#>  [2] "vital_status"                        
#>  [3] "days_to_death"                       
#>  [4] "days_to_last_followup"               
#>  [5] "tumor_tissue_site"                   
#>  [6] "pathologic_stage"                    
#>  [7] "pathology_T_stage"                   
#>  [8] "pathology_N_stage"                   
#>  [9] "pathology_M_stage"                   
#> [10] "gender"                              
#> [11] "date_of_initial_pathologic_diagnosis"
#> [12] "days_to_last_known_alive"            
#> [13] "radiation_therapy"                   
#> [14] "histological_type"                   
#> [15] "residual_tumor"                      
#> [16] "number_of_lymph_nodes"               
#> [17] "race"                                
#> [18] "ethnicity"                           

## convert data to matrix and add as experiment
gbm <-
  c(gbm, RPPA_matrix = data.matrix(assay(gbm[["GBM_RPPAArray-20160128"]])))

imputeAssay(gbm, i = "RPPA_matrix")
#> Warning: 'experiments' dropped; see 'drops()'
#> harmonizing input:
#>   removing 2124 sampleMap rows not in names(experiments)
#>   removing 361 colData rownames not in sampleMap 'primary'
#> Warning: 5 rows with more than 50 % entries missing;
#>  mean imputation used for these rows
#> A MultiAssayExperiment object of 5 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 5:
#>  [1] GBM_CNACGH_CGH_hg_244a-20160128: RaggedExperiment with 81512 rows and 438 columns
#>  [2] GBM_CNACGH_CGH_hg_415k_g4124a-20160128: RaggedExperiment with 57975 rows and 338 columns
#>  [3] GBM_CNASNP-20160128: RaggedExperiment with 602338 rows and 1104 columns
#>  [4] GBM_RPPAArray-20160128: SummarizedExperiment with 208 rows and 244 columns
#>  [5] RPPA_matrix: matrix with 208 rows and 244 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files