Filter features of one SE based on their rowData

The filterFeaturesOneSE methods enables users to filter features based on a variable in their rowData. It is directly inspired of the function filterFeature of the package QFeatures. The first difference is that the filter only applies to one SummarizedExperiment contained in the object rather than applying on all the SE. This method generates a new SummarizedExperiment object which is added to the QFeatures object. If the SE on which the filter applies is the last one of the object, then a new xxxx. If it is not the last one, the new SE is added and all the further SE are deleted. The features matching the. The filters can be provided as instances of class AnnotationFilter (see the package QFeatures) or of class FunctionFilter (see below).

Usage

FunctionFilter(name, ...)

filterFeaturesOneSE(object, ...)

# S4 method for class 'QFeatures'
filterFeaturesOneSE(object, i, name = "newAssay", filters)

Arguments

name

A character(1) naming the new assay. Default is newAssay. Note that the function will fail if there's already an assay with name.

...

Additional arguments

object

An instance of class QFeatures or SummarizedExperiment.

i

The index or name of the assay which features will be filtered the create the new assay.

filters

A list() containing instances of class AnnotationFilter or FunctionFilter

Value

A filtered QFeature object

Function filters

The function filters are filters as defined in the DaparToolshed package. Each filter is defined by a name (which is the name of a function) and a list which contains the parameters passed to the function. Those filters can be created with the FunctionFilter constructor.

Those functions are divided into two main categories:

• the one that filter on one rowData feature,

• the one based on a two-dimensional information such as the adjacency matrix

for the first category, all filters of class AnnotationFilter::AnnotationFilter can be used as they are used in QFeatures

For the second category, the package DaparToolshed provides filter functions based either on the adjacency matrix:

Or based on the quantitative metadata (identification):

Author

Samuel Wieczorek

Examples

data(feat1, package = 'QFeatures')
## ----------------------------------------
## Creating function filters
## ----------------------------------------

#FunctionFilter('FUN',
#               param1 = 'value_of_param1',
#               param2 = 'value_of_param2')

FunctionFilter('qMetacellWholeLine',
               cmd = 'delete',
               pattern = 'imputed POV')
#> An object of class "FunctionFilter"
#> Slot "name":
#> [1] "qMetacellWholeLine"
#> 
#> Slot "params":
#> $cmd
#> [1] "delete"
#> 
#> $pattern
#> [1] "imputed POV"
#> 
#> 

## ----------------------------------------------------------------
## Filter the last assay to keep only specific peptides. This filter
## only applies on peptide dataset.
## ----------------------------------------------------------------

spec.filter <- FunctionFilter('specPeptides', list())
## using a user-defined character filter
filterFeaturesOneSE(feat1, list(FunctionFilter('specPeptides', list())))
#> An instance of class QFeatures (type: bulk) with 1 set:
#> 
#>  [1] psms: SummarizedExperiment with 10 rows and 2 columns 


## ----------------------------------------------------------------
## Filter the last assay to keep only specific peptides and topn 
## peptides. The two filters are run sequentially.
## ----------------------------------------------------------------

lst.filters <- list(FunctionFilter('specPeptides', list()))
lst.filters <- append(lst.filters,
FunctionFilter('topnPeptides',
fun = 'rowSums',
top = 2))
filterFeaturesOneSE(feat1, lst.filters)
#> An instance of class QFeatures (type: bulk) with 1 set:
#> 
#>  [1] psms: SummarizedExperiment with 10 rows and 2 columns 

## ----------------------------------------------------------------
## Filter the last assay to delete peptides where, in at least one 
## condition, there is less than 80% of samples marked as 'imputed POV'
## ----------------------------------------------------------------

filter <- FunctionFilter('qMetacellOnConditions',
cmd = 'delete',
mode = 'AtLeastOneCond',
pattern = 'imputed POV',
conds = SummarizedExperiment::colData(feat1)$Condition,
percent = TRUE,
th = 0.8,
operator = '<')

 filterFeaturesOneSE(feat1, filter)
#> An instance of class QFeatures (type: bulk) with 1 set:
#> 
#>  [1] psms: SummarizedExperiment with 10 rows and 2 columns