The impute method performs data imputation on MSnSet instances
using a variety of methods.
Users should proceed with care when imputing data and take precautions to assure that the imputation produce valid results, in particular with naive imputations such as replacing missing values with 0.
See MsCoreUtils::impute_matrix() for details on the different
imputation methods available and strategies.
# S4 method for MSnSet
impute(object, method, ...)An MSnSet object with missing values to be
imputed.
character(1) defining the imputation method. See
MsCoreUtils::imputeMethods() for available ones. See
MsCoreUtils::impute_matrix() for details.
Additional parameters passed to the inner imputation
function. See MsCoreUtils::impute_matrix() for details.
data(naset)
## table of missing values along the rows
table(fData(naset)$nNA)
#>
#> 0 1 2 3 4 8 9 10
#> 301 247 91 13 2 23 10 2
## table of missing values along the columns
pData(naset)$nNA
#> [1] 34 45 56 39 47 52 49 61 41 42 55 45 51 43 57 53
## non-random missing values
notna <- which(!fData(naset)$randna)
length(notna)
#> [1] 35
notna
#> [1] 6 20 79 88 130 187 227 231 238 264 275 317 324 363 373 382 409 437 445
#> [20] 453 456 474 484 485 492 514 516 546 568 580 594 631 648 664 671
impute(naset, method = "min")
#> MSnSet (storageMode: lockedEnvironment)
#> assayData: 689 features, 16 samples
#> element names: exprs
#> protocolData: none
#> phenoData
#> sampleNames: M1F1A M1F4A ... M2F11B (16 total)
#> varLabels: nNA
#> varMetadata: labelDescription
#> featureData
#> featureNames: AT1G09210 AT1G21750 ... AT4G39080 (689 total)
#> fvarLabels: nNA randna
#> fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> Annotation:
#> - - - Processing information - - -
#> Data imputation using min Thu Mar 14 06:11:08 2024
#> MSnbase version: 1.15.6
if (require("imputeLCMD")) {
impute(naset, method = "QRILC")
impute(naset, method = "MinDet")
}
#> Loading required package: imputeLCMD
#> Loading required package: tmvtnorm
#> Loading required package: mvtnorm
#> Loading required package: Matrix
#>
#> Attaching package: ‘Matrix’
#> The following object is masked from ‘package:S4Vectors’:
#>
#> expand
#> 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 and has been superseded by the norm2 package.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: pcaMethods
#>
#> Attaching package: ‘pcaMethods’
#> The following object is masked from ‘package:stats’:
#>
#> loadings
#> Loading required package: impute
#> Imputing along margin 2 (samples/columns).
#> Imputing along margin 2 (samples/columns).
#> MSnSet (storageMode: lockedEnvironment)
#> assayData: 689 features, 16 samples
#> element names: exprs
#> protocolData: none
#> phenoData
#> sampleNames: M1F1A M1F4A ... M2F11B (16 total)
#> varLabels: nNA
#> varMetadata: labelDescription
#> featureData
#> featureNames: AT1G09210 AT1G21750 ... AT4G39080 (689 total)
#> fvarLabels: nNA randna
#> fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> Annotation:
#> - - - Processing information - - -
#> Data imputation using MinDet Thu Mar 14 06:11:08 2024
#> MSnbase version: 1.15.6
if (require("norm"))
impute(naset, method = "MLE")
#> Imputing along margin 2 (samples/columns).
#> Warning: NAs introduced by coercion to integer range
#> Iterations of EM:
#> 1...2...
#> MSnSet (storageMode: lockedEnvironment)
#> assayData: 689 features, 16 samples
#> element names: exprs
#> protocolData: none
#> phenoData
#> sampleNames: M1F1A M1F4A ... M2F11B (16 total)
#> varLabels: nNA
#> varMetadata: labelDescription
#> featureData
#> featureNames: AT1G09210 AT1G21750 ... AT4G39080 (689 total)
#> fvarLabels: nNA randna
#> fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> Annotation:
#> - - - Processing information - - -
#> Data imputation using MLE Thu Mar 14 06:11:08 2024
#> MSnbase version: 1.15.6
impute(naset, "mixed",
randna = fData(naset)$randna,
mar = "knn", mnar = "QRILC")
#> Imputing along margin 1 (features/rows).
#> Imputing along margin 1 (features/rows).
#> MSnSet (storageMode: lockedEnvironment)
#> assayData: 689 features, 16 samples
#> element names: exprs
#> protocolData: none
#> phenoData
#> sampleNames: M1F1A M1F4A ... M2F11B (16 total)
#> varLabels: nNA
#> varMetadata: labelDescription
#> featureData
#> featureNames: AT1G09210 AT1G21750 ... AT4G39080 (689 total)
#> fvarLabels: nNA randna
#> fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> Annotation:
#> - - - Processing information - - -
#> Data imputation using mixed Thu Mar 14 06:11:08 2024
#> MSnbase version: 1.15.6
## neighbour averaging
x <- naset[1:4, 1:6]
exprs(x)[1, 1] <- NA ## min value
exprs(x)[2, 3] <- NA ## average
exprs(x)[3, 1:2] <- NA ## min value and average
## 4th row: no imputation
exprs(x)
#> M1F1A M1F4A M1F7A M1F11A M1F2B M1F5B
#> AT1G09210 NA 0.275500 0.21600 0.18525 0.465667 0.199667
#> AT1G21750 0.332000 0.279667 NA 0.16600 0.451500 0.200375
#> AT1G51760 NA NA 0.16825 0.18825 0.459750 0.214500
#> AT1G56340 0.336733 NA NA NA 0.487167 0.201833
exprs(impute(x, "nbavg"))
#> Assuming values are ordered.
#> Imputing along margin 1 (features/rows).
#> M1F1A M1F4A M1F7A M1F11A M1F2B M1F5B
#> AT1G09210 0.166000 0.275500 0.2160000 0.18525 0.465667 0.199667
#> AT1G21750 0.332000 0.279667 0.2228335 0.16600 0.451500 0.200375
#> AT1G51760 0.166000 0.167125 0.1682500 0.18825 0.459750 0.214500
#> AT1G56340 0.336733 NA NA NA 0.487167 0.201833