A function to replace NA in the data frame by Bayesian PCA.
Arguments
- missdf
a data frame with missing values to be imputed containing features in columns and samples in rows.
- verbose
boolean, if
TRUE, prints the typical prompts ofMetabImpute::Impute().- ...
other parameters of
MetabImpute::Impute()besidesmethodanddata.
Value
A data.frame with imputed values of Bayesian PCA by
MetabImpute::Impute().
No replicates allowed
Impute() allows users to improve the quality of the imputation by
providing the number of replications of the experiment. As one of the aims of
the imputomics is to standardize the
input and the output, our wrappers do not allow for this behavior.
References
Davis TJ, Firzli TR, Higgins Keppler EA, Richardson M, Bean HD (2022). “Addressing Missing Data in GC × GC Metabolomics: Identifying Missingness Type and Evaluating the Impact of Imputation Methods on Experimental Replication.” Analytical Chemistry, 94(31), 10912--10920. ISSN 0003-2700, doi:10.1021/acs.analchem.1c04093 .
Examples
data(sim_miss)
impute_metabimpute_bpca(sim_miss)
#> Loading required package: doParallel
#> Loading required package: foreach
#> Loading required package: iterators
#> Loading required package: parallel
#> Loading required package: missForest
#> Loading required package: missRanger
#> Loading required package: pcaMethods
#>
#> Attaching package: 'pcaMethods'
#> The following object is masked from 'package:stats':
#>
#> loadings
#> Loading required package: PEMM
#> Loading required package: imputeLCMD
#> Loading required package: tmvtnorm
#> Loading required package: mvtnorm
#> Loading required package: Matrix
#> Loading required package: stats4
#> Loading required package: gmm
#> Loading required package: sandwich
#> Loading required package: norm
#> This package has some major limitations
#> (for example, it does not work reliably when
#> the number of variables exceeds 30),
#> and has been superseded by the norm2 package.
#> Loading required package: impute
#> Loading required package: magrittr
#> Loading required package: matrixStats
#>
#> Attaching package: 'matrixStats'
#> The following objects are masked from 'package:Biobase':
#>
#> anyMissing, rowMedians
#> Loading required package: MASS
#> Loading required package: abind
#> Loading required package: randomForest
#> randomForest 4.7-1.1
#> Type rfNews() to see new features/changes/bug fixes.
#>
#> Attaching package: 'randomForest'
#> The following object is masked from 'package:ggplot2':
#>
#> margin
#> The following object is masked from 'package:Biobase':
#>
#> combine
#> The following object is masked from 'package:BiocGenerics':
#>
#> combine
#> Loading required package: glmnet
#> Loaded glmnet 4.1-7
#> Loading required package: rpart
#> Loading required package: FNN
#> X1 X2 X3 X4 X5 X6
#> 1 0.155050833 0.88115677 0.5375502 0.1194841210 0.600405471 0.56556562
#> 2 0.968378809 0.60748407 0.6337790 0.6181194666 0.008132938 0.75207300
#> 3 0.468263086 0.57452956 0.2476089 0.4112819348 0.370125689 0.56556562
#> 4 0.776819652 0.80313329 0.5513393 0.1459775318 0.724248714 0.84777606
#> 5 0.407885741 0.79911692 0.2347611 0.1897135850 0.418270750 0.70873497
#> 6 0.538797149 0.80186265 0.2586147 0.5754945197 0.238248518 0.56556562
#> 7 0.830082966 0.75207300 0.9528881 0.0001881735 0.887881226 0.48833525
#> 8 0.187103555 0.57546133 0.8568746 0.3283282877 0.577775384 0.34722847
#> 9 0.779969688 0.94421829 0.5375502 0.0782780354 0.739950257 0.20304801
#> 10 0.193943927 0.21898736 0.5545242 0.6041721753 0.160679622 0.73995026
#> 11 0.434231178 0.47799791 0.8769438 0.3267990556 0.529571799 0.89369752
#> 12 0.002274518 0.04466879 0.8018626 0.7904525734 0.574529562 0.57452956
#> 13 0.834692139 0.16848571 0.5467373 0.8300829662 0.960086967 0.08444870
#> 14 0.553408593 0.36828087 0.2327532 0.8100510929 0.398616886 0.14825833
#> 15 0.956967818 0.56331137 0.6022678 0.2027530000 0.408468068 0.37028128
#> 16 0.948497073 0.59753202 0.7125391 0.5060450307 0.613321482 0.86117762
#> 17 0.600729976 0.73432757 0.6489036 0.5206679751 0.706590850 0.83668180
#> 18 0.261807405 0.70365677 0.1875624 0.0842680801 0.799116918 0.74054705
#> 19 0.643034251 0.73682406 0.2383929 0.2687653562 0.161331222 0.98576580
#> 20 0.526233507 0.59753202 0.5375502 0.8147157354 0.885932416 0.03208182