aggregateFeatures  R Documentation 
This function aggregates the quantitative features of an assay,
applying a summarisation function (fun
) to sets of features.
The fcol
variable name points to a rowData column that defines
how to group the features during aggregate. This variable can
eigher be a vector (we then refer to an aggregation by vector)
or an adjacency matrix (aggregation by matrix).
The rowData of the aggregated SummarizedExperiment
assay
contains a .n
variable that provides the number of parent
features that were aggregated.
When aggregating with a vector, the newly aggregated
SummarizedExperiment
assay also contains a new aggcounts
assay
containing the aggregation counts matrix, i.e. the number of
features that were aggregated for each sample, which can be
accessed with the aggcounts()
accessor.
## S4 method for signature 'QFeatures'
aggregateFeatures(
object,
i,
fcol,
name = "newAssay",
fun = MsCoreUtils::robustSummary,
...
)
## S4 method for signature 'SummarizedExperiment'
aggregateFeatures(object, fcol, fun = MsCoreUtils::robustSummary, ...)
## S4 method for signature 'QFeatures'
adjacencyMatrix(object, i, adjName = "adjacencyMatrix")
adjacencyMatrix(object, i, adjName = "adjacencyMatrix") < value
## S4 method for signature 'SummarizedExperiment'
aggcounts(object, ...)
object 
An instance of class 
i 
When adding an adjacency matrix to an assay of a

fcol 
A 
name 
A 
fun 
A function used for quantitative feature aggregation. See Details for examples. 
... 
Additional parameters passed the 
adjName 

value 
An adjacency matrix with row and column names. The
matrix will be coerced to compressed, columnoriented sparse
matrix (class 
Aggregation is performed by a function that takes a matrix as
input and returns a vector of length equal to ncol(x)
. Examples
thereof are
MsCoreUtils::medianPolish()
to fits an additive model (two way
decomposition) using Tukey's median polish_ procedure using
stats::medpolish()
;
MsCoreUtils::robustSummary()
to calculate a robust aggregation
using MASS::rlm()
(default);
base::colMeans()
to use the mean of each column;
colMeansMat(x, MAT)
to aggregate feature by the calculating
the mean of peptide intensities via an adjacency matrix. Shared
peptides are reused multiple times.
matrixStats::colMedians()
to use the median of each column.
base::colSums()
to use the sum of each column;
colSumsMat(x, MAT)
to aggregate feature by the summing the
peptide intensities for each protein via an adjacency
matrix. Shared peptides are reused multiple times.
See MsCoreUtils::aggregate_by_vector()
for more aggregation functions.
A QFeatures
object with an additional assay or a
SummarizedExperiment
object (or subclass thereof).
Missing quantitative values have different effects based on the aggregation method employed:
The aggregation functions should be able to deal with missing
values by either ignoring or propagating them. This is often
done with an na.rm
argument, that can be passed with
...
. For example, rowSums
, rowMeans
, rowMedians
,
... will ignore NA
values with na.rm = TRUE
, as illustrated
below.
Missing values will result in an error when using medpolish
,
unless na.rm = TRUE
is used. Note that this option relies on
implicit assumptions and/or performes an implicit imputation:
when summing, the values are implicitly imputed by 0, assuming
that the NA
represent a trully absent features; when
averaging, the assumption is that the NA
represented a
genuinely missing value.
When using robust summarisation, individual missing values are
excluded prior to fitting the linear model by robust
regression. To remove all values in the feature containing the
missing values, use filterNA()
.
More generally, missing values often need dedicated handling such
as filtering (see filterNA()
) or imputation (see impute()
).
Missing values in the row data of an assay will also impact the
resulting (aggregated) assay row data, as illustrated in the
example below. Any feature variables (a column in the row data)
containing NA
values will be dropped from the aggregated row
data. The reasons underlying this drop are detailed in the
reduceDataFrame()
manual page: only invariant aggregated rows,
i.e. rows resulting from the aggregation from identical variables,
are preserved during aggregations.
The situation illustrated below should however only happen in rare
cases and should often be imputable using the value of the other
aggregation rows before aggregation to preserve the invariant
nature of that column. In cases where an NA
is present in an
otherwise variant column, the column would be dropped anyway.
When considering nonunique peptides explicitly, i.e. peptides
that map to multiple proteins rather than as a protein group, it
is convenient to encode this ambiguity explicitly using a
peptidebyproteins (sparse) adjacency matrix. This matrix is
typically stored in the rowdata and set/retrieved with the
adjacencyMatrix()
function. It can be created manually (as
illustrated below) or using PSMatch::makeAdjacencyMatrix()
.
The QFeatures vignette provides an extended example and
the Processing vignette, for a complete quantitative
proteomics data processing pipeline. The
MsCoreUtils::aggregate_by_vector()
manual page provides
further details.
## 
## An example QFeatures with PSMlevel data
## 
data(feat1)
feat1
## Aggregate PSMs into peptides
feat1 < aggregateFeatures(feat1, "psms", "Sequence", name = "peptides")
feat1
## Aggregate peptides into proteins
feat1 < aggregateFeatures(feat1, "peptides", "Protein", name = "proteins")
feat1
assay(feat1[[1]])
assay(feat1[[2]])
aggcounts(feat1[[2]])
assay(feat1[[3]])
aggcounts(feat1[[3]])
## 
## Aggregation with missing quantitative values
## 
data(ft_na)
ft_na
assay(ft_na[[1]])
rowData(ft_na[[1]])
## By default, missing values are propagated
ft2 < aggregateFeatures(ft_na, 1, fcol = "X", fun = colSums)
assay(ft2[[2]])
aggcounts(ft2[[2]])
## The rowData .n variable tallies number of initial rows that
## were aggregated (irrespective of NAs) for all the samples.
rowData(ft2[[2]])
## Ignored when setting na.rm = TRUE
ft3 < aggregateFeatures(ft_na, 1, fcol = "X", fun = colSums, na.rm = TRUE)
assay(ft3[[2]])
aggcounts(ft3[[2]])
## 
## Aggregation with missing values in the row data
## 
## Row data results without any NAs, which includes the
## Y variables
rowData(ft2[[2]])
## Missing value in the Y feature variable
rowData(ft_na[[1]])[1, "Y"] < NA
rowData(ft_na[[1]])
ft3 < aggregateFeatures(ft_na, 1, fcol = "X", fun = colSums)
## The Y feature variable has been dropped!
assay(ft3[[2]])
rowData(ft3[[2]])
## 
## Using a peptidebyproteins adjacency matrix
## 
## Let's use assay peptides from object feat1 and
## define that peptide SYGFNAAR maps to proteins
## Prot A and B
se < feat1[["peptides"]]
rowData(se)$Protein[3] < c("ProtA;ProtB")
rowData(se)
## This can also be defined using anadjacency matrix, manual
## encoding here. See PSMatch::makeAdjacencyMatrix() for a
## function that does it automatically.
adj < matrix(0, nrow = 3, ncol = 2,
dimnames = list(rownames(se),
c("ProtA", "ProtB")))
adj[1, 1] < adj[2, 2] < adj[3, 1:2] < 1
adj
adjacencyMatrix(se) < adj
rowData(se)
adjacencyMatrix(se)
## Aggregation using the adjacency matrix
se2 < aggregateFeatures(se, fcol = "adjacencyMatrix",
fun = MsCoreUtils::colMeansMat)
## Peptide SYGFNAAR was taken into account in both ProtA and ProtB
## aggregations.
assay(se2)
## Aggregation by matrix on a QFeature object works as with a
## vector
ft < QFeatures(list(peps = se))
ft < aggregateFeatures(ft, "peps", "adjacencyMatrix", name = "protsByMat",
fun = MsCoreUtils::colMeansMat)
assay(ft[[2]])
rowData(ft[[2]])
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