R/DaparToolshed-filterFeaturesOneSE.R

In samWieczorek/Dapar2: Tools for the Differential Analysis of Proteins Abundance with R

##' @title Filter features of one SE based on their rowData
##'
##' @description
##'
##' 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).
##'
##' @section 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] 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:
##'  - [DaparToolshed::topnPeptides()]: xxxx
##'  - [DaparToolshed::sharedPeptides()]: xxx
##'  - [DaparToolshed::specPeptides()]: xxx
##'
##' Or based on the quantitative metadata (identification):
##'  - [DaparToolshed::qMetacellWholeMatrix()]: xxx
##'  - [DaparToolshed::qMetacellWholeLine()]: xxx
##'  - [DaparToolshed::qMetacellOnConditions()]: xxx
##'
##' @return A filtered `QFeature` object
##'
##' @author Samuel Wieczorek
##'
##' @name QFeatures-filtering-oneSE
##'
##' @rdname QFeatures-filtering-oneSE
##'
##' @aliases filterFeaturesOneSE filterFeaturesOneSE, DaparToolsehd, 
##' FunctionFilter, VariableFilter
##'
##' @examples
##'
##' ## ----------------------------------------
##' ## Creating function filters
##' ## ----------------------------------------
##'
##' FunctionFilter('FUN',
##'                param1 = 'value_of_param1',
##'                param2 = 'value_of_param2')
##'
##' FunctionFilter('qMetacellWholeLine',
##'                cmd = 'delete',
##'                pattern = '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())))
##'
##'
##' ## ----------------------------------------------------------------
##' ## 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)
##'
##' ## ----------------------------------------------------------------
##' ## 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 = colData(ft)$Condition,
##' percent = TRUE,
##' th = 0.8,
##' operator = '<')
##'
##'  filterFeaturesOneSE(feat1, filter)
##'
##'
NULL


##' @exportClass FunctionFilter
##' @rdname QFeatures-filtering-oneSE
setClass("FunctionFilter",
    slots = c(
        name = "character",
        params = "list"
    ),
    prototype = list(
        name = character(1),
        params = list()
    )
)


##' @param name `character(1)` refering to the name of the function 
##' to apply the filter on.
##' @param ... Additional arguments
##'
##' @export FunctionFilter
##' @rdname QFeatures-filtering-oneSE
##'
##' @importFrom methods new
##' 
FunctionFilter <- function(name, ...) {
    new("FunctionFilter",
        name = name,
        params = list(...)
    )
}


##' @param object An instance of class `QFeatures` or `SummarizedExperiment`.
##'
##' @param i The index or name of the assay which features will be
##'     filtered the create the new assay.
##'
##' @param 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`.
##'
##' @param filters A `list()` containing instances of class `AnnotationFilter` 
##' or `FunctionFilter`
##'
##' @exportMethod filterFeaturesOneSE
##' @importFrom S4Vectors metadata
##' @rdname QFeatures-filtering-oneSE
setMethod(
    "filterFeaturesOneSE", "QFeatures",
    function(object, i, name = "newAssay", filters) {
        if (length(object) == 0) {
            return(object)
        }
        if (name %in% names(object)) {
            stop("There's already an assay named '", name, "'.")
        }
        if (missing(i)) {
            i <- length(object)
        }

        if (missing(filters)) {
            return(object)
        }
        ## Create the aggregated assay
        new.se <- filterFeaturesOneSE(object[[i]], filters)
        ## Add the assay to the QFeatures object
        object <- addAssay(object,
            new.se,
            name = name
        )

        if (nrow(new.se) > 0) {
            idcol <- S4Vectors::metadata(object[[i]])$idcol
            if (is.null(idcol)) {
                warning("xxx")
                S4Vectors::metadata(object[[i]])$idcol <- "_temp_ID_"
                idcol <- "_temp_ID_"
            }


            ## Link the input assay to the aggregated assay
            object <- addAssayLink(object,
                from = names(object)[i],
                to = name,
                varFrom = idcol,
                varTo = idcol
            )
        }

        return(object)
    }
)



##' @importFrom BiocGenerics do.call
##' @importFrom AnnotationFilter field
##' @exportMethod filterFeaturesOneSE
##' @rdname QFeatures-filtering-oneSE
setMethod(
    "filterFeaturesOneSE", "SummarizedExperiment",
    function(object, filters) {
        for (x in filters) {
            if (inherits(x, "AnnotationFilter")) {
                .tmp <- rowData(object)
                sel <- if (AnnotationFilter::field(x) %in% names(.tmp)) {
                    do.call(
                        AnnotationFilter::condition(x),
                        list(
                            .tmp[, AnnotationFilter::field(x)],
                            AnnotationFilter::value(x)
                        )
                    )
                } else {
                    rep(FALSE, nrow(.tmp))
                }
                object <- object[sel]
            } else if (inherits(x, "FunctionFilter")) {
                object <- do.call(
                    x@name,
                    append(
                        list(object = object),
                        x@params
                    )
                )
            }
        }


        return(object)
    }
)