impute-methods: Quantitative proteomics data imputation
In MSnbase: Base Functions and Classes for Mass Spectrometry and Proteomics
Description
Details
Methods
Author(s)
References
Examples
Description
The impute method performs data imputation on an
MSnSet instance using a variety of methods (see below). The
imputation and the parameters are logged into the
processingData(object) slot.
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.
Details
There are two types of mechanisms resulting in missing values in
LC/MSMS experiments.
Missing values resulting from absence of detection of a
feature, despite ions being present at detectable concentrations.
For example in the case of ion suppression or as a result from the
stochastic, data-dependent nature of the MS acquisition
method. These missing value are expected to be randomly
distributed in the data and are defined as missing at random (MAR)
or missing completely at random (MCAR).
Biologically relevant missing values resulting from the
absence of the low abundance of ions (below the limit of detection
of the instrument). These missing values are not expected to be
randomly distributed in the data and are defined as missing not at
random (MNAR).
MNAR features should ideally be imputed with a left-censor method,
such as QRILC below. Conversely, it is recommended to use host
deck methods such nearest neighbours, Bayesian missing value
imputation or maximum likelihood methods when values are missing at
random.
Currently, the following imputation methods are available:
- MLE
Maximum likelihood-based imputation method using the EM
algorithm. Implemented in the norm::imp.norm function. See
imp.norm for details and additional
parameters. Note that here, ... are passed to the
em.norm function, rather to the actual
imputation function imp.norm.
- bpca
Bayesian missing value imputation are available, as
implemented in the and pcaMethods::pca functions. See
pca for details and additional
parameters.
- knn
Nearest neighbour averaging, as implemented in the
impute::impute.knn function. See
impute.knn for details and additional
parameters.
- QRILC
A missing data imputation method that performs the
imputation of left-censored missing data using random draws from a
truncated distribution with parameters estimated using quantile
regression. Implemented in the imputeLCMD::impute.QRILC
function. See impute.QRILC for details
and additional parameters.
- MinDet
Performs the imputation of left-censored missing data
using a deterministic minimal value approach. Considering a
expression data with n samples and p features, for
each sample, the missing entries are replaced with a minimal value
observed in that sample. The minimal value observed is estimated as
being the q-th quantile (default q = 0.01) of the observed
values in that sample. Implemented in the
imputeLCMD::impute.MinDet function. See
impute.MinDet for details and additional
parameters.
- MinProb
Performs the imputation of left-censored missing data
by random draws from a Gaussian distribution centred to a minimal
value. Considering an expression data matrix with n samples
and p features, for each sample, the mean value of the
Gaussian distribution is set to a minimal observed value in that
sample. The minimal value observed is estimated as being the q-th
quantile (default q = 0.01) of the observed values in that
sample. The standard deviation is estimated as the median of the
feature standard deviations. Note that when estimating the
standard deviation of the Gaussian distribution, only the
peptides/proteins which present more than 50% recorded values are
considered. Implemented in the imputeLCMD::impute.MinProb
function. See impute.MinProb for details
and additional parameters.
- min
Replaces the missing values by the smallest non-missing
value in the data.
- zero
Replaces the missing values by 0.
- mixed
A mixed imputation applying two methods (to be defined
by the user as mar for values missing at random and
mnar for values missing not at random, see example) on two
M[C]AR/MNAR subsets of the data (as defined by the user by a
randna logical, of length equal to nrow(object)).
- nbavg
Average neighbour imputation for fractions collected
along a fractionation/separation gradient, such as sub-cellular
fractions. The method assumes that the fraction are ordered along
the gradient and is invalid otherwise.
Continuous sets NA value at the beginning and the end of
the quantitation vectors are set to the lowest observed value in
the data or to a user defined value passed as argument k.
Them, when a missing value is flanked by two non-missing
neighbouring values, it is imputed by the mean of its direct
neighbours. A stretch of 2 or more missing values will not be
imputed. See the example below.
- none
No imputation is performed and the missing values are
left untouched. Implemented in case one wants to only impute value
missing at random or not at random with the mixed method.
The naset MSnSet is an real quantitative
data where quantitative values have been replaced by NAs. See
script/naset.R for details.
Methods
signature(object = "MSnSet", method, ...) This method
performs data imputation on the object MSnSet
instance using the method algorithm. ... is used to
pass parameters to the imputation function. See the respective
methods for details and additional parameters.
Author(s)
Laurent Gatto and Samuel Wieczorek
References
Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor
Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing
value estimation methods for DNA microarrays Bioinformatics (2001) 17
(6): 520-525.
Oba et al., A Bayesian missing value estimation method for gene
expression profile data, Bioinformatics (2003) 19 (16): 2088-2096.
Cosmin Lazar (2015). imputeLCMD: A collection of methods for
left-censored missing data imputation. R package version
2.0. http://CRAN.R-project.org/package=imputeLCMD.
Lazar C, Gatto L, Ferro M, Bruley C, Burger T. Accounting for the
Multiple Natures of Missing Values in Label-Free Quantitative Proteomics
Data Sets to Compare Imputation Strategies. J Proteome Res. 2016 Apr
1;15(4):1116-25. doi: 10.1021/acs.jproteome.5b00981. PubMed PMID:
26906401.
Examples
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data(naset)
## table of missing values along the rows
table(fData(naset)$nNA)
## table of missing values along the columns
pData(naset)$nNA
## non-random missing values
notna <- which(!fData(naset)$randna)
length(notna)
notna
impute(naset, method = "min")
if (require("imputeLCMD")) {
impute(naset, method = "QRILC")
impute(naset, method = "MinDet")
}
if (require("norm"))
impute(naset, method = "MLE")
impute(naset, "mixed",
randna = fData(naset)$randna,
mar = "knn", mnar = "QRILC")
## 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)
exprs(impute(x, "nbavg"))
Example output
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: mzR
Loading required package: Rcpp
Loading required package: S4Vectors
Loading required package: stats4
Attaching package: ‘S4Vectors’
The following object is masked from ‘package:base’:
expand.grid
Loading required package: ProtGenerics
Attaching package: ‘ProtGenerics’
The following object is masked from ‘package:stats’:
smooth
This is MSnbase version 2.16.0
Visit https://lgatto.github.io/MSnbase/ to get started.
Attaching package: ‘MSnbase’
The following object is masked from ‘package:base’:
trimws
0 1 2 3 4 8 9 10
301 247 91 13 2 23 10 2
[1] 34 45 56 39 47 52 49 61 41 42 55 45 51 43 57 53
[1] 35
[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
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 Mon Feb 15 13:06:15 2021
MSnbase version: 1.15.6
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
Loading required package: pcaMethods
Attaching package: ‘pcaMethods’
The following object is masked from ‘package:stats’:
loadings
Loading required package: impute
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 Mon Feb 15 13:06:17 2021
Using default parameters
MSnbase version: 1.15.6
Iterations of EM:
1...2...3...4...5...6...7...8...9...10...11...
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 Mon Feb 15 13:06:17 2021
Using default parameters
MSnbase version: 1.15.6
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 Mon Feb 15 13:06:18 2021
Using default parameters
MSnbase version: 1.15.6
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
Assuming values are ordered.
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
MSnbase documentation built on Jan. 23, 2021, 2 a.m.
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Description Details Methods Author(s) References Examples
Description
The impute method performs data imputation on an
MSnSet instance using a variety of methods (see below). The
imputation and the parameters are logged into the
processingData(object) slot.
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.
Details
There are two types of mechanisms resulting in missing values in LC/MSMS experiments.
Missing values resulting from absence of detection of a feature, despite ions being present at detectable concentrations. For example in the case of ion suppression or as a result from the stochastic, data-dependent nature of the MS acquisition method. These missing value are expected to be randomly distributed in the data and are defined as missing at random (MAR) or missing completely at random (MCAR).
Biologically relevant missing values resulting from the absence of the low abundance of ions (below the limit of detection of the instrument). These missing values are not expected to be randomly distributed in the data and are defined as missing not at random (MNAR).
MNAR features should ideally be imputed with a left-censor method,
such as QRILC below. Conversely, it is recommended to use host
deck methods such nearest neighbours, Bayesian missing value
imputation or maximum likelihood methods when values are missing at
random.
Currently, the following imputation methods are available:
- MLE
Maximum likelihood-based imputation method using the EM algorithm. Implemented in the
norm::imp.normfunction. Seeimp.normfor details and additional parameters. Note that here,...are passed to theem.normfunction, rather to the actual imputation functionimp.norm.- bpca
Bayesian missing value imputation are available, as implemented in the and
pcaMethods::pcafunctions. Seepcafor details and additional parameters.- knn
Nearest neighbour averaging, as implemented in the
impute::impute.knnfunction. Seeimpute.knnfor details and additional parameters.- QRILC
A missing data imputation method that performs the imputation of left-censored missing data using random draws from a truncated distribution with parameters estimated using quantile regression. Implemented in the
imputeLCMD::impute.QRILCfunction. Seeimpute.QRILCfor details and additional parameters.- MinDet
Performs the imputation of left-censored missing data using a deterministic minimal value approach. Considering a expression data with n samples and p features, for each sample, the missing entries are replaced with a minimal value observed in that sample. The minimal value observed is estimated as being the q-th quantile (default
q = 0.01) of the observed values in that sample. Implemented in theimputeLCMD::impute.MinDetfunction. Seeimpute.MinDetfor details and additional parameters.- MinProb
Performs the imputation of left-censored missing data by random draws from a Gaussian distribution centred to a minimal value. Considering an expression data matrix with n samples and p features, for each sample, the mean value of the Gaussian distribution is set to a minimal observed value in that sample. The minimal value observed is estimated as being the q-th quantile (default
q = 0.01) of the observed values in that sample. The standard deviation is estimated as the median of the feature standard deviations. Note that when estimating the standard deviation of the Gaussian distribution, only the peptides/proteins which present more than 50% recorded values are considered. Implemented in theimputeLCMD::impute.MinProbfunction. Seeimpute.MinProbfor details and additional parameters.- min
Replaces the missing values by the smallest non-missing value in the data.
- zero
Replaces the missing values by 0.
- mixed
A mixed imputation applying two methods (to be defined by the user as
marfor values missing at random andmnarfor values missing not at random, see example) on two M[C]AR/MNAR subsets of the data (as defined by the user by arandnalogical, of length equal tonrow(object)).- nbavg
Average neighbour imputation for fractions collected along a fractionation/separation gradient, such as sub-cellular fractions. The method assumes that the fraction are ordered along the gradient and is invalid otherwise.
Continuous sets
NAvalue at the beginning and the end of the quantitation vectors are set to the lowest observed value in the data or to a user defined value passed as argumentk. Them, when a missing value is flanked by two non-missing neighbouring values, it is imputed by the mean of its direct neighbours. A stretch of 2 or more missing values will not be imputed. See the example below.- none
No imputation is performed and the missing values are left untouched. Implemented in case one wants to only impute value missing at random or not at random with the
mixedmethod.
The naset MSnSet is an real quantitative
data where quantitative values have been replaced by NAs. See
script/naset.R for details.
Methods
signature(object = "MSnSet", method, ...)This method performs data imputation on the
objectMSnSetinstance using themethodalgorithm....is used to pass parameters to the imputation function. See the respective methods for details and additional parameters.
Author(s)
Laurent Gatto and Samuel Wieczorek
References
Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing value estimation methods for DNA microarrays Bioinformatics (2001) 17 (6): 520-525.
Oba et al., A Bayesian missing value estimation method for gene expression profile data, Bioinformatics (2003) 19 (16): 2088-2096.
Cosmin Lazar (2015). imputeLCMD: A collection of methods for left-censored missing data imputation. R package version 2.0. http://CRAN.R-project.org/package=imputeLCMD.
Lazar C, Gatto L, Ferro M, Bruley C, Burger T. Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. J Proteome Res. 2016 Apr 1;15(4):1116-25. doi: 10.1021/acs.jproteome.5b00981. PubMed PMID: 26906401.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | data(naset)
## table of missing values along the rows
table(fData(naset)$nNA)
## table of missing values along the columns
pData(naset)$nNA
## non-random missing values
notna <- which(!fData(naset)$randna)
length(notna)
notna
impute(naset, method = "min")
if (require("imputeLCMD")) {
impute(naset, method = "QRILC")
impute(naset, method = "MinDet")
}
if (require("norm"))
impute(naset, method = "MLE")
impute(naset, "mixed",
randna = fData(naset)$randna,
mar = "knn", mnar = "QRILC")
## 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)
exprs(impute(x, "nbavg"))
|
Example output
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: mzR
Loading required package: Rcpp
Loading required package: S4Vectors
Loading required package: stats4
Attaching package: ‘S4Vectors’
The following object is masked from ‘package:base’:
expand.grid
Loading required package: ProtGenerics
Attaching package: ‘ProtGenerics’
The following object is masked from ‘package:stats’:
smooth
This is MSnbase version 2.16.0
Visit https://lgatto.github.io/MSnbase/ to get started.
Attaching package: ‘MSnbase’
The following object is masked from ‘package:base’:
trimws
0 1 2 3 4 8 9 10
301 247 91 13 2 23 10 2
[1] 34 45 56 39 47 52 49 61 41 42 55 45 51 43 57 53
[1] 35
[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
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 Mon Feb 15 13:06:15 2021
MSnbase version: 1.15.6
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
Loading required package: pcaMethods
Attaching package: ‘pcaMethods’
The following object is masked from ‘package:stats’:
loadings
Loading required package: impute
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 Mon Feb 15 13:06:17 2021
Using default parameters
MSnbase version: 1.15.6
Iterations of EM:
1...2...3...4...5...6...7...8...9...10...11...
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 Mon Feb 15 13:06:17 2021
Using default parameters
MSnbase version: 1.15.6
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 Mon Feb 15 13:06:18 2021
Using default parameters
MSnbase version: 1.15.6
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
Assuming values are ordered.
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
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