MSnSetList-class.RdA class for storing lists of MSnSet
instances.
There are two ways to store different sets of measurements pertaining an experimental unit, such as replicated measures of different conditions that were recorded over more than one MS acquisition. Without focusing on any proteomics technology in particular, these multiple assays can be recorded as
A single combined MSnSet (see the section
Combining MSnSet instances in the MSnbase-demo
section). In such cases, the different experimental (phenotypical)
conditions are recorded as an
AnnotatedDataFrame in the phenoData
slots.
Quantitative data for features that were missing in an assay are
generally encode as missing with NA values. Alternatively,
only features observed in all assays could be selected. See the
commonFeatureNames functions to select only common
features among two or more MSnSet instance.
Each set of measurements is stored in an MSnSet which
are combined into one MSnSetList. Each MSnSet elements
can have identical or different samples and features. Unless
compiled directly manually by the user, one would expect at least
one of these dimensions (features/rows or samples/columns) are
conserved (i.e. all feature or samples names are identical). See
split/unsplit below.
Objects can be created and manipluated with:
MSnSetList(x, log, featureDAta)The class constructor
that takes a list of valid MSnSet instances as input
x, an optional logging list, and an optional feature
metadata data.frame.
split(x, f)An MSnSetList can be created from
an MSnSet instance. x is a single
MSnSet and f is a factor or a
character of length 1. In the latter case, f will be
matched to the feature- and phenodata variable names (in that
order). If a match is found, the respective variable is extracted,
converted to a factor if it is not one already, and used to split
x along the features/rows (f was a feature variable
name) or samples/columns (f was a phenotypic variable
name). If f is passed as a factor, its length will be
matched to nrow(x) or ncol(x) (in that order) to
determine if x will be split along the features (rows) or
sample (columns). Hence, the length of f must match exactly
to either dimension.
unsplit(value, f)The unsplit method reverses
the effect of splitting the value MSnSet along the
groups f.
as(x, "MSnSetList")Where x is an instance of
class MzTab. See the class documentation for
details.
x:Object of class list containing valid
MSnSet instances. Can be extracted with the
msnsets() accessor.
log:Object of class list containing an object
creation log, containing among other elements the call that
generated the object. Can be accessed with objlog().
featureData:Object of class DataFrame that
stores metadata for each object in the x slot. The number of
rows of this data.frame must be equal to the number of items
in the x slot and their respective (row)names must be
identical.
.__classVersion__:The version of the instance. For development purposes only.
"[["Extracts a single MSnSet at position.
"["Extracts one of more MSnSets as
MSnSetList.
lengthReturns the number of MSnSets.
namesReturns the names of MSnSets, if
available. The replacement method is also available.
showDisplay the object by printing a short summary.
lapply(x, FUN, ...)Apply function FUN to each
element of the input x. If the application of FUN
returns and MSnSet, then the return value is an
MSnSetList, otherwise a list
sapply(x, FUN, ..., simplify = TRUE, USE.NAMES =
TRUE)A lapply wrapper that simplifies the ouptut to a
vector, matric or array is possible. See ?base::sapply
for details.
fDataReturns the features metadata featureData
slot.
fData<-Features metadata featureData
replacement method.
The commonFeatureNames function to select common
features among MSnSet instances.
library("pRolocdata")
data(tan2009r1)
data(tan2009r2)
## The MSnSetList class
## for an unnamed list, names are set to indices
msnl <- MSnSetList(list(tan2009r1, tan2009r2))
names(msnl)
#> [1] "1" "2"
## a named example
msnl <- MSnSetList(list(A = tan2009r1, B = tan2009r2))
names(msnl)
#> [1] "A" "B"
msnsets(msnl)
#> $A
#> MSnSet (storageMode: lockedEnvironment)
#> assayData: 888 features, 4 samples
#> element names: exprs
#> protocolData: none
#> phenoData
#> sampleNames: X114 X115 X116 X117
#> varLabels: Fractions
#> varMetadata: labelDescription
#> featureData
#> featureNames: P20353 P53501 ... P07909 (888 total)
#> fvarLabels: FBgn Protein.ID ... markers.tl (16 total)
#> fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> pubMedIds: 19317464
#> Annotation:
#> - - - Processing information - - -
#> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:44 2015
#> MSnbase version: 1.17.12
#>
#> $B
#> MSnSet (storageMode: lockedEnvironment)
#> assayData: 871 features, 4 samples
#> element names: exprs
#> protocolData: none
#> phenoData
#> sampleNames: X114 X115 X116 X117
#> varLabels: Fractions
#> varMetadata: labelDescription
#> featureData
#> featureNames: P20432 P20353 ... Q9VIW3 (871 total)
#> fvarLabels: FBgn Protein.ID ... markers (13 total)
#> fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> pubMedIds: 19317464
#> Annotation:
#> - - - Processing information - - -
#> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:44 2015
#> MSnbase version: 1.17.12
#>
length(msnl)
#> [1] 2
objlog(msnl)
#> $call
#> MSnSetList(x = list(A = tan2009r1, B = tan2009r2))
#>
msnl[[1]] ## an MSnSet
#> MSnSet (storageMode: lockedEnvironment)
#> assayData: 888 features, 4 samples
#> element names: exprs
#> protocolData: none
#> phenoData
#> sampleNames: X114 X115 X116 X117
#> varLabels: Fractions
#> varMetadata: labelDescription
#> featureData
#> featureNames: P20353 P53501 ... P07909 (888 total)
#> fvarLabels: FBgn Protein.ID ... markers.tl (16 total)
#> fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> pubMedIds: 19317464
#> Annotation:
#> - - - Processing information - - -
#> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:44 2015
#> MSnbase version: 1.17.12
msnl[1] ## an MSnSetList of length 1
#> Instance of class 'MSnSetList' containig 1 objects.
## Iterating over the elements
lapply(msnl, dim) ## a list
#> $A
#> [1] 888 4
#>
#> $B
#> [1] 871 4
#>
lapply(msnl, normalise, method = "quantiles") ## an MSnSetList
#> Instance of class 'MSnSetList' containig 2 objects.
fData(msnl)
#> DataFrame with 2 rows and 0 columns
fData(msnl)$X <- sapply(msnl, nrow)
fData(msnl)
#> DataFrame with 2 rows and 1 column
#> X
#> <integer>
#> A 888
#> B 871
## Splitting and unsplitting
## splitting along the columns/samples
data(dunkley2006)
head(pData(dunkley2006))
#> membrane.prep fraction replicate
#> M1F1A 1 1 A
#> M1F4A 1 4 A
#> M1F7A 1 7 A
#> M1F11A 1 11 A
#> M1F2B 1 2 B
#> M1F5B 1 5 B
(splt <- split(dunkley2006, "replicate"))
#> Instance of class 'MSnSetList' containig 2 objects.
lapply(splt, dim) ## the number of rows and columns of the split elements
#> $A
#> [1] 689 8
#>
#> $B
#> [1] 689 8
#>
unsplt <- unsplit(splt, dunkley2006$replicate)
stopifnot(compareMSnSets(dunkley2006, unsplt))
## splitting along the rows/features
head(fData(dunkley2006))
#> assigned evidence method new pd.2013 pd.markers markers.orig
#> AT1G09210 ER predicted PLSDA known ER ER lumen ER
#> AT1G21750 ER predicted PLSDA known ER ER lumen ER
#> AT1G51760 ER unknown PLSDA new ER ER lumen unknown
#> AT1G56340 ER predicted PLSDA known ER ER lumen ER
#> AT2G32920 ER predicted PLSDA known ER ER lumen ER
#> AT2G47470 ER predicted PLSDA known ER ER lumen ER
#> markers
#> AT1G09210 ER lumen
#> AT1G21750 ER lumen
#> AT1G51760 ER lumen
#> AT1G56340 ER lumen
#> AT2G32920 ER lumen
#> AT2G47470 ER lumen
(splt <- split(dunkley2006, "markers"))
#> Instance of class 'MSnSetList' containig 10 objects.
unsplt <- unsplit(splt, factor(fData(dunkley2006)$markers))
simplify2array(lapply(splt, dim))
#> ER lumen ER membrane Golgi Mitochondrion PM Plastid Ribosome TGN unknown
#> [1,] 14 45 28 55 46 20 19 13 428
#> [2,] 16 16 16 16 16 16 16 16 16
#> vacuole
#> [1,] 21
#> [2,] 16
stopifnot(compareMSnSets(dunkley2006, unsplt))