diff --git a/DESCRIPTION b/DESCRIPTION
index f6aa0c415..005b55868 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: xcms
-Version: 4.1.6
+Version: 4.1.7
 Title: LC-MS and GC-MS Data Analysis
 Description: Framework for processing and visualization of chromatographically
     separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF,
@@ -46,7 +46,7 @@ Imports:
     methods,
     Biobase,
     BiocGenerics,
-    ProtGenerics (>= 1.29.1),
+    ProtGenerics (>= 1.35.2),
     lattice,
     RColorBrewer,
     plyr,
@@ -62,7 +62,8 @@ Imports:
     Spectra (>= 1.13.2),
     progress,
     multtest,
-    jsonlite
+    jsonlite,
+    MetaboCoreUtils (>= 1.11.2)
 Suggests:
     BiocStyle,
     caTools,
@@ -76,7 +77,6 @@ Suggests:
     MALDIquant,
     pheatmap,
     MsBackendMgf,
-    MetaboCoreUtils,
     signal
 Enhances:
     Rgraphviz,
@@ -127,6 +127,7 @@ Collate:
     'init.R'
     'loadXcmsData.R'
     'matchpeaks.R'
+    'method-filterFeatures.R'
     'methods-Chromatogram.R'
     'methods-IO.R'
     'methods-MChromatograms.R'
diff --git a/NAMESPACE b/NAMESPACE
old mode 100755
new mode 100644
index 134ef3f4a..aa3563bf3
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -4,7 +4,7 @@ importFrom("utils", "capture.output", "data")
 import("methods")
 importMethodsFrom("ProtGenerics", "peaks", "chromatogram", "writeMSData",
     "polarity<-", "centroided", "isCentroided", "peaks<-",
-    "isolationWindowTargetMz", "quantify", "bin", "spectrapply")
+    "isolationWindowTargetMz", "quantify", "bin", "spectrapply", "filterFeatures")
 importClassesFrom("ProtGenerics", "Param")
 importFrom("BiocGenerics", "updateObject", "fileName", "subset",
     "dirname", "dirname<-")
@@ -35,6 +35,9 @@ importFrom("S4Vectors", "split", "Rle", "DataFrame", "SimpleList", "List",
 importMethodsFrom("S4Vectors", "as.matrix", "mcols", "mcols<-",
                   "extractROWS", "findMatches")
 importFrom("SummarizedExperiment", "SummarizedExperiment")
+importFrom("SummarizedExperiment", "rowData")
+importFrom("SummarizedExperiment", "rowData<-")
+importFrom("SummarizedExperiment", "assay")
 importFrom("MsCoreUtils", "rbindFill", "closest", "i2index", "sumi", "between",
            "maxi", "breaks_ppm")
 
@@ -594,3 +597,11 @@ importFrom("jsonlite", "serializeJSON", "write_json", "unserializeJSON",
 export("RDataParam")
 export("PlainTextParam")
 exportMethods("storeResults")
+
+## filtering features things
+importFrom("MetaboCoreUtils", "rowRsd", "rowDratio", "rowPercentMissing",
+           "rowBlank")
+export("RsdFilter")
+export("DratioFilter")
+export("PercentMissingFilter")
+export("BlankFlag")
diff --git a/R/DataClasses.R b/R/DataClasses.R
index 5f465bccd..f24cf52b2 100644
--- a/R/DataClasses.R
+++ b/R/DataClasses.R
@@ -190,6 +190,7 @@ setClass("xcmsPeaks", contains = "matrix")
 .PROCSTEP.PEAK.FILLING <- "Missing peak filling"
 .PROCSTEP.CALIBRATION <- "Calibration"
 .PROCSTEP.FEATURE.GROUPING <- "Feature grouping"
+.PROCSTEP.FEATURE.FILTERING <- "Feature filtering"
 .PROCSTEPS <- c(
     .PROCSTEP.UNKNOWN,
     .PROCSTEP.PEAK.DETECTION,
@@ -198,7 +199,8 @@ setClass("xcmsPeaks", contains = "matrix")
     .PROCSTEP.RTIME.CORRECTION,
     .PROCSTEP.PEAK.FILLING,
     .PROCSTEP.CALIBRATION,
-    .PROCSTEP.FEATURE.GROUPING
+    .PROCSTEP.FEATURE.GROUPING,
+    .PROCSTEP.FEATURE.FILTERING
 )
 
 ############################################################
diff --git a/R/method-filterFeatures.R b/R/method-filterFeatures.R
new file mode 100644
index 000000000..8c708680b
--- /dev/null
+++ b/R/method-filterFeatures.R
@@ -0,0 +1,622 @@
+#' @title Filtering of features based on conventional quality assessment
+#'
+#' @description
+#'
+#' When dealing with metabolomics results, it is often necessary to filter
+#' features based on certain criteria. These criteria are typically derived
+#' from statistical formulas applied to full rows of data, where each row
+#' represents a feature and its abundance of signal in each samples.
+#' The `filterFeatures` function filters features based on these conventional
+#' quality assessment criteria. Multiple types of filtering are implemented and
+#' can be defined by the `filter` argument.
+#'
+#' Supported `filter` arguments are:
+#'
+#' - [`RsdFilter`]: Calculates the relative standard deviation
+#'  (i.e. coefficient of variation) in abundance for each feature in QC
+#'  (Quality Control) samples and filters them in the input object according to
+#'  a provided threshold.
+#'
+#' - [`DratioFilter`]: Computes the D-ratio or *dispersion ratio*, defined as
+#'  the standard deviation in abundance for QC samples divided by the standard
+#'  deviation for biological test samples, for each feature and filters them
+#'  according to a provided threshold.
+#'
+#' - [`PercentMissingFilter`]: Determines the percentage of missing values for
+#'  each feature in the various sample groups and filters them according to a
+#'  provided threshold.
+#'
+#' - [`BlankFlag`]: Identifies features where the mean abundance in test samples
+#'  is lower than a specified multiple of the mean abundance of blank samples.
+#'  This can be used to flag features that result from contamination in the
+#'  solvent of the samples. A new column `possible_contaminants` is added to the
+#'  `featureDefinitions` (`XcmsExperiment` object) or `rowData`
+#'  (`SummarizedExperiment` object) reflecting this.
+#'
+#' For specific examples, see the help pages of the individual parameter classes
+#' listed above.
+#'
+#' @param object `XcmsExperiment` or `SummarizedExperiment`. For an
+#' `XcmsExperiment` object, the `featureValues(object)` will be evaluated, and
+#' for `Summarizedesxperiment` the `assay(object, assay)`. The object will be
+#' filtered.
+#'
+#' @param filter The parameter object selecting and configuring the type of
+#' filtering. It can be one of the following classes: [`RsdFilter`],
+#' [`DratioFilter`], [`PercentMissingFilter`] or [`BlankFlag`].
+#'
+#' @param assay For filtering of `SummarizedExperiment` objects only. Indicates
+#' which assay the filtering will be based on. Note that the features for the
+#' entire object will be removed, but the computations are performed on a single
+#' assay. Default is 1, which means the first assay of the `object` will
+#' be evaluated.
+#'
+#' @param ... Optional parameters. For `object` being an `XcmsExperiment`:
+#' parameters for the [featureValues()] call.
+#'
+#' @name filterFeatures
+#'
+#' @author Philippine Louail
+#'
+#' @importFrom MetaboCoreUtils rowRsd rowDratio rowPercentMissing rowBlank
+#'
+#' @importMethodsFrom ProtGenerics filterFeatures
+#'
+#' @references
+#'
+#' Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR,
+#' Dunn WB. Guidelines and considerations for the use of system suitability
+#' and quality control samples in mass spectrometry assays applied in
+#' untargeted clinical metabolomic studies. Metabolomics. 2018;14(6):72.
+#' doi: 10.1007/s11306-018-1367-3. Epub 2018 May 18. PMID: 29805336;
+#' PMCID: PMC5960010.
+#'
+#' @examples
+#' ## See the vignettes for more detailed examples
+#' library(MsExperiment)
+#'
+#' ## Load a test data set with features defined.
+#' test_xcms <- loadXcmsData()
+#' ## Set up parameter to filter based on coefficient of variation. By setting
+#' ## the filter such as below, features that have a coefficient of variation
+#' ## superior to 0.3 in QC samples will be removed from the object `test_xcms`
+#' ## when calling the `filterFeatures` function.
+#'
+#' rsd_filter <- RsdFilter(threshold = 0.3,
+#'                         qcIndex = sampleData(test_xcms)$sample_type == "QC")
+#'
+#' filtered_data_rsd <- filterFeatures(object = test_xcms, filter = rsd_filter)
+#'
+#' ## Set up parameter to filter based on D-ratio. By setting the filter such
+#' ## as below, features that have a D-ratio computed based on their abundance
+#' ## between QC and study samples superior to 0.5 will be removed from the
+#' ## object `test_xcms`.
+#'
+#' dratio_filter <- DratioFilter(threshold = 0.5,
+#'                  qcIndex = sampleData(test_xcms)$sample_type == "QC",
+#'                  studyIndex = sampleData(test_xcms)$sample_type == "study")
+#'
+#' filtered_data_dratio <- filterFeatures(object = test_xcms,
+#'                                        filter = dratio_filter)
+#'
+#' ## Set up parameter to filter based on the percent of missing data.
+#' ## Parameter f should represent the sample group of samples, for which the
+#' ## percentage of missing values will be evaluated. As the setting is defined
+#' ## bellow, if a feature as less (or equal) to 30% missing values in one
+#' ## sample group, it will be kept in the `test_xcms` object.
+#'
+#' missing_data_filter <- PercentMissingFilter(threshold = 30,
+#'                                        f = sampleData(test_xcms)$sample_type)
+#'
+#' filtered_data_missing <- filterFeatures(object = test_xcms,
+#'                                         filter = missing_data_filter)
+#'
+#' ## Set up parameter to flag possible contaminants based on blank samples'
+#' ## abundance. By setting the filter such as below, features that have mean
+#' ## abundance ratio between blank(here use study as an example) and QC
+#' ## samples less than 2 will be marked as `TRUE` in an extra column named
+#' ## `possible_contaminants` in the `featureDefinitions` table of the object
+#' ## `test_xcms`.
+#'
+#' filter <- BlankFlag(threshold = 2,
+#'                     qcIndex = sampleData(test_xcms)$sample_type == "QC",
+#'                     blankIndex = sampleData(test_xcms)$sample_type == "study")
+#' filtered_xmse <- filterFeatures(test_xcms, filter)
+#'
+#' @md
+NULL
+
+#' @title Filter features based on their coefficient of variation
+#'
+#' @name RsdFilter
+#'
+#' @export
+#'
+#' @family Filter features in xcms
+#'
+#' @description
+#' The `RsdFilter` class and methods enable users to filter features from an
+#' `XcmsExperiment` or `SummarizedExperiment` object based on their relative
+#' standard deviation (coefficient of variation) for a specified threshold.
+#'
+#' This `filter` is part of the possible dispatch of the generic function
+#' `filterFeatures`. Features *above* (`>`) the user-input threshold will be
+#' removed from the entire dataset.
+#'
+#' @param threshold `numeric` value representing the threshold. Features with a
+#' coefficient of variation *strictly higher* (`>`) than this will be removed
+#' from the entire dataset.
+#'
+#' @param qcIndex `integer` (or `logical`) vector corresponding to the indices
+#' of QC samples.
+#'
+#' @param na.rm `logical` indicates whether missing values (`NA`) should be
+#' removed prior to the calculations.
+#'
+#' @param mad `logical` indicates whether the *Median Absolute Deviation* (MAD)
+#' should be used instead of the standard deviation. This is suggested for
+#' non-gaussian distributed data.
+#'
+#' @inheritParams filterFeatures
+#'
+#' @return For `RsdFilter`: a `RsdFilter` class. `filterFeatures` return the
+#' input object minus the features that did not met the user input threshold.
+#'
+#' @note
+#' It is assumed that the abundance values are in natural scale. Abundances in
+#' log scale should be first transformed to natural scale before calculating
+#' the RSD.
+#'
+#' @author Philippine Louail
+#'
+#' @importFrom MetaboCoreUtils rowRsd
+#'
+NULL
+
+#' @noRd
+setClass("RsdFilter",
+         slots = c(threshold = "numeric",
+                   qcIndex = "integer",
+                   na.rm = "logical",
+                   mad = "logical"),
+         contains = "Param",
+         prototype = prototype(
+             threshold = numeric(),
+             qcIndex = integer(),
+             na.rm = logical(),
+             mad = logical()),
+         validity = function(object) {
+             msg <- NULL
+             if (length(object@threshold) != 1)
+                 msg <- c("'threshold' has to be a numeric of length 1")
+             msg
+         })
+
+#' @rdname RsdFilter
+#'
+#' @export
+RsdFilter <- function(threshold = 0.3, qcIndex = integer(),
+                      na.rm = TRUE, mad = FALSE) {
+    if (is.logical(qcIndex))
+        qcIndex <- which(qcIndex)
+    if (is.numeric(qcIndex))
+        qcIndex <- as.integer(qcIndex)
+    new("RsdFilter", threshold = threshold, qcIndex = qcIndex, na.rm = na.rm,
+        mad = mad)
+}
+
+#' @rdname RsdFilter
+setMethod("filterFeatures",
+          signature(object = "XcmsResult",
+                    filter = "RsdFilter"),
+          function(object, filter, ...){
+            .check_index_range(filter@qcIndex, length(object), name = "qcIndex")
+            vals <- featureValues(object, ...)[, filter@qcIndex]
+            vals <- rowRsd(x = vals,  na.rm = filter@na.rm, mad = filter@mad)
+            fts_idx <- which(vals <= filter@threshold)
+            message(length(vals) - length(fts_idx), " features were removed")
+            ph <- XProcessHistory(param = filter,
+                                  date. = date(),
+                                  type. = .PROCSTEP.FEATURE.FILTERING,
+                                  fileIndex = seq_along(object))
+            object <- addProcessHistory(object, ph)
+            filterFeatureDefinitions(object, features = fts_idx)
+          }
+)
+
+#' @rdname RsdFilter
+setMethod("filterFeatures",
+          signature(object = "SummarizedExperiment",
+                    filter = "RsdFilter"),
+          function(object, filter, assay = 1){
+              .check_index_range(filter@qcIndex, ncol(object), name = "qcIndex")
+              vals <- assay(object, assay)[, filter@qcIndex]
+              vals <- rowRsd(vals, na.rm = filter@na.rm, mad = filter@mad)
+              fts_idx <- which(vals <= filter@threshold)
+              message(length(vals) - length(fts_idx), " features were removed")
+              object[fts_idx]
+          }
+)
+
+#' @title Filter features based on the dispersion ratio
+#'
+#' @name DratioFilter
+#'
+#' @export
+#'
+#' @family Filter features in xcms
+#'
+#' @description
+#' The `DratioFilter` class and method enable users to filter features from an
+#' `XcmsExperiment` or `SummarizedExperiment` object based on the D-ratio or
+#' *dispersion ratio*. This is defined as the standard deviation for QC
+#' samples divided by the standard deviation for biological test samples, for
+#' each feature of the object (Broadhurst et al.).
+#'
+#' This `filter` is part of the possible dispatch of the generic function
+#' `filterFeatures`.  Features *above* (`>`) the user-input threshold will be
+#' removed from the entire dataset.
+#'
+#' @param threshold `numeric` value representing the threshold. Features with a
+#' D-ratio *strictly higher* (`>`) than this will be removed from the entire
+#' dataset.
+#'
+#' @param qcIndex `integer` (or `logical`) vector corresponding to the indices
+#' of QC samples.
+#'
+#' @param studyIndex `integer` (or `logical`) vector corresponding of the
+#' indices of study samples.
+#'
+#' @param na.rm `logical` Indicates whether missing values (`NA`) should be
+#' removed prior to the calculations.
+#'
+#' @param mad `logical` Indicates whether the *Median Absolute Deviation*
+#' (MAD) should be used instead of the standard deviation. This is suggested
+#' for non-gaussian distributed data.
+#'
+#' @inheritParams filterFeatures
+#'
+#' @return For `DratioFilter`: a `DratioFilter` class. `filterFeatures` return
+#' the input object minus the features that did not met the user input threshold
+#'
+#' @author Philippine Louail
+#'
+#' @importFrom MetaboCoreUtils rowDratio
+#'
+#' @references
+#'
+#' Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR,
+#' Dunn WB. Guidelines and considerations for the use of system suitability
+#' and quality control samples in mass spectrometry assays applied in
+#' untargeted clinical metabolomic studies. Metabolomics. 2018;14(6):72.
+#' doi: 10.1007/s11306-018-1367-3. Epub 2018 May 18. PMID: 29805336;
+#' PMCID: PMC5960010.
+#'
+NULL
+
+#' @noRd
+setClass("DratioFilter",
+         slots = c(threshold = "numeric",
+                   qcIndex = "integer",
+                   studyIndex = "integer",
+                   na.rm = "logical",
+                   mad = "logical"),
+         contains = "Param",
+         prototype = prototype(
+             threshold = numeric(),
+             qcIndex = integer(),
+             studyIndex = integer(),
+             na.rm = logical(),
+             mad = logical()),
+         validity = function(object) {
+             msg <- NULL
+             if (length(object@threshold) != 1)
+                 msg <- c("'threshold' has to be a numeric of length 1")
+             msg
+         })
+
+#' @rdname DratioFilter
+#'
+#' @export
+DratioFilter <- function(threshold = 0.5,
+                         qcIndex = integer(),
+                         studyIndex = integer(),
+                         na.rm = TRUE, mad = FALSE) {
+    if (is.logical(qcIndex))
+        qcIndex <- which(qcIndex)
+    if(is.numeric(qcIndex))
+        qcIndex <- as.integer(qcIndex)
+    if (is.logical(studyIndex))
+        studyIndex <- which(studyIndex)
+    if(is.numeric(studyIndex))
+        studyIndex <- as.integer(studyIndex)
+    new("DratioFilter", threshold = threshold, qcIndex = qcIndex,
+        studyIndex = studyIndex, na.rm = na.rm, mad = mad)
+}
+
+#' @rdname DratioFilter
+setMethod("filterFeatures",
+          signature(object = "XcmsResult",
+                    filter = "DratioFilter"),
+          function(object, filter, ...){
+              .check_index_range(filter@studyIndex, length(object),
+                                 name = "studyIndex")
+              .check_index_range(filter@qcIndex, length(object), name = "qcIndex")
+              x <- featureValues(object, ...)[, filter@studyIndex]
+              y <- featureValues(object, ...)[, filter@qcIndex]
+              vals <- rowDratio(x = x, y = y,
+                                na.rm = filter@na.rm,
+                                mad = filter@mad)
+              fts_idx <- which(vals <= filter@threshold)
+              message(length(vals) - length(fts_idx), " features were removed")
+              ph <- XProcessHistory(param = filter,
+                                    date. = date(),
+                                    type. = .PROCSTEP.FEATURE.FILTERING,
+                                    fileIndex = seq_along(object))
+              object <- addProcessHistory(object, ph)
+              filterFeatureDefinitions(object, features = fts_idx)
+          }
+)
+
+#' @rdname DratioFilter
+setMethod("filterFeatures",
+          signature(object = "SummarizedExperiment",
+                    filter = "DratioFilter"),
+          function(object, filter, assay = 1){
+              .check_index_range(filter@studyIndex, ncol(object),
+                                 name = "studyIndex")
+              .check_index_range(filter@qcIndex, ncol(object), name = "qcIndex")
+              x <- assay(object, assay)[, filter@studyIndex]
+              y <- assay(object, assay)[, filter@qcIndex]
+              vals <- rowDratio(x = x, y = y,
+                                na.rm = filter@na.rm,
+                                mad = filter@mad)
+              fts_idx <- which(vals <= filter@threshold)
+              message(length(vals) - length(fts_idx), " features were removed")
+              object[fts_idx]
+          }
+)
+
+#' @title Filter features based on the percentage of missing data
+#'
+#' @name PercentMissingFilter
+#'
+#' @export
+#'
+#' @family Filter features in xcms
+#'
+#' @description
+#' The `PercentMissingFilter` class and method enable users to filter features
+#' from an `XcmsExperiment` or `SummarizedExperiment` object based on the
+#' percentage (values from 1 to 100) of missing values for each features in
+#' different sample groups and filters them according to a provided threshold.
+#'
+#' This `filter` is part of the possible dispatch of the generic function
+#' `filterFeatures`. Features with a percentage of missing values *higher* (`>`)
+#' than the user input threshold in all sample groups will be removed (i.e.
+#' features for which the proportion of missing values is below (`<=`) the
+#' threshold in at least one sample group will be retained).
+#'
+#' @param f `vector` of the same length as the `object`, specifying the sample
+#' type for each sample in the dataset. The percentage of missing values per
+#' feature will be computed within each of these sample groups. Parameter `f`,
+#' if not already a `factor`, will be converted to one using the factor function.
+#' Samples with an `NA` as their value in `f` will be excluded from calculation.
+#'
+#' @param threshold `numeric` percentage (between 0 and 100) of accepted missing
+#' values for a feature in one sample group.
+#'
+#' @inheritParams filterFeatures
+#'
+#' @return For `PercentMissingFilter`: a `PercentMissingFilter` class.
+#' `filterFeatures` return the input object minus the features that did not met
+#' the user input threshold
+#'
+#' @author Philippine Louail
+#'
+#' @importFrom MetaboCoreUtils rowPercentMissing
+#'
+NULL
+
+#' @noRd
+setClass("PercentMissingFilter",
+         slots = c(threshold = "numeric",
+                   f = "factor"),
+         contains = "Param",
+         prototype = prototype(
+             threshold = numeric(),
+             f = factor()),
+         validity = function(object) {
+             msg <- NULL
+             if (length(object@threshold) != 1)
+                 msg <- c("'threshold' has to be a numeric of length 1")
+             msg
+         })
+
+#' @rdname PercentMissingFilter
+#'
+#' @export
+PercentMissingFilter <- function(threshold = 30, f = factor()) {
+    if (!is.factor(f))
+        f <- factor(f)
+    new("PercentMissingFilter", threshold = threshold, f = f)
+}
+
+#' @rdname PercentMissingFilter
+setMethod("filterFeatures",
+          signature(object = "XcmsResult",
+                    filter = "PercentMissingFilter"),
+          function(object, filter, ...){
+              if (length(filter@f) != length(object))
+                  stop("'f' must be same lenght as object")
+              fts_idx <- c()
+              for (i in levels(filter@f)){
+                  spl_idx <- which(filter@f == i)
+                  vals <- rowPercentMissing(featureValues(object, ...)[, spl_idx])
+                  fts_idx <- c(fts_idx, which(vals <= filter@threshold))
+              }
+              fts_idx <- order(unique(fts_idx))
+              message(length(vals) - length(fts_idx), " features were removed")
+              ph <- XProcessHistory(param = filter,
+                                    date. = date(),
+                                    type. = .PROCSTEP.FEATURE.FILTERING,
+                                    fileIndex = seq_along(object))
+              object <- addProcessHistory(object, ph)
+              filterFeatureDefinitions(object, features = fts_idx)
+          }
+)
+
+#' @rdname PercentMissingFilter
+setMethod("filterFeatures",
+          signature(object = "SummarizedExperiment",
+                    filter = "PercentMissingFilter"),
+          function(object, filter, assay = 1){
+              if (length(filter@f) != ncol(object))
+                  stop("'f' must be same lenght as object")
+              fts_idx <- c()
+              for (i in levels(filter@f)){
+                  spl_idx <- which(filter@f == i)
+                  vals <- rowPercentMissing(assay(object, assay)[, spl_idx])
+                  fts_idx <- c(fts_idx, which(vals <= filter@threshold))
+              }
+              fts_idx <- order(unique(fts_idx))
+              message(length(vals) - length(fts_idx), " features were removed")
+              object[fts_idx]
+          }
+)
+
+#' @title Flag features based on the intensity in blank samples
+#'
+#' @name BlankFlag
+#'
+#' @export
+#'
+#' @family Filter features in xcms
+#'
+#' @description
+#' The `BlankFlag` class and method enable users to flag features of an
+#' `XcmsExperiment` or `SummarizedExperiment` object based on the relationship
+#' between the intensity of a feature in blanks compared to the intensity in the
+#' samples.
+#'
+#' This class and method are part of the possible dispatch of the
+#' generic function `filterFeatures`. Features *below* (`<`) the user-input
+#' threshold will be flagged by calling the `filterFeatures` function. This
+#' means that an extra column will be created in `featureDefinitions` or
+#' `rowData` called `possible_contaminants` with a logical value for each
+#' feature.
+#'
+#' @param threshold `numeric` indicates the minimum difference
+#' required between the mean abundance of a feature in samples compared to the
+#' mean abundance of the same feature in blanks for it to not be considered a
+#' possible contaminant. For example, the default threshold of 2 signifies that
+#' the mean abundance of the features in samples has to be at least twice the
+#' mean abundance in blanks for it to not be flagged as a possible contaminant.
+#'
+#' @param blankIndex `integer` (or `logical`) vector corresponding to the
+#' indices of blank samples.
+#'
+#' @param qcIndex `integer` (or `logical`) vector corresponding to the
+#' indices of quality control (QC) samples.
+#'
+#' @param na.rm `logical` indicates whether missing values (`NA`) should be
+#' removed prior to the calculations.
+#'
+#' @inheritParams filterFeatures
+#'
+#' @return For `BlankFlag`: a `BlankFlag` class. `filterFeatures` returns
+#' the input object with an added column in the features metadata called
+#' `possible_contaminants` with a logical value for each feature. This is added
+#' to `featureDefinitions` for `XcmsExperiment` objects and `rowData` for
+#' `SummarizedExperiment` objects.
+#'
+#' @author Philippine Louail
+#'
+#' @importFrom MetaboCoreUtils rowBlank
+#'
+NULL
+
+#' @noRd
+setClass("BlankFlag",
+         slots = c(threshold = "numeric", blankIndex = "integer",
+                   qcIndex = "integer", na.rm = "logical"),
+         contains = "Param",
+         prototype = prototype(
+             threshold = numeric(),
+             blankIndex = integer(),
+             qcIndex = integer(),
+             na.rm = logical()),
+         validity = function(object) {
+             msg <- NULL
+             if (length(object@threshold) != 1)
+                 msg <- c("'threshold' has to be a numeric of length 1")
+             msg
+         })
+
+#' @rdname BlankFlag
+#'
+#' @export
+BlankFlag <- function(threshold = 2, blankIndex = integer(),
+                         qcIndex = integer(), na.rm = TRUE) {
+    if (is.logical(blankIndex))
+        blankIndex <- which(blankIndex)
+    if(is.numeric(blankIndex))
+        blankIndex <- as.integer(blankIndex)
+    if (is.logical(qcIndex))
+        qcIndex <- which(qcIndex)
+    if(is.numeric(qcIndex))
+        qcIndex <- as.integer(qcIndex)
+    new("BlankFlag", threshold = threshold, blankIndex = blankIndex,
+        qcIndex = qcIndex, na.rm = na.rm)
+}
+
+#' @rdname BlankFlag
+setMethod("filterFeatures",
+          signature(object = "XcmsResult",
+                    filter = "BlankFlag"),
+          function(object, filter, ...){
+              .check_index_range(filter@blankIndex, length(object), name = "blankIndex")
+              .check_index_range(filter@qcIndex, length(object), name = "qcIndex")
+              x <- featureValues(object, ...)[, filter@qcIndex]
+              y <- featureValues(object, ...)[, filter@blankIndex]
+              vals <- rowBlank(x = x, y = y,
+                               na.rm = filter@na.rm,
+                               threshold = filter@threshold)
+              message(sum(vals, na.rm = TRUE) - nrow(featureValues(object)),
+                            " features were flagged")
+              featureDefinitions(object)$possible_contaminants <- vals
+              ph <- XProcessHistory(param = filter,
+                                    date. = date(),
+                                    type. = .PROCSTEP.FEATURE.FILTERING,
+                                    fileIndex = seq_along(object))
+              object <- addProcessHistory(object, ph)
+              validObject(object)
+              object
+          }
+)
+
+#' @rdname BlankFlag
+setMethod("filterFeatures",
+          signature(object = "SummarizedExperiment",
+                    filter = "BlankFlag"),
+          function(object, filter, assay = 1){
+              .check_index_range(filter@blankIndex, ncol(object),
+                                 name = "blankIndex")
+              .check_index_range(filter@qcIndex, ncol(object), name = "qcIndex")
+              x <- assay(object, assay)[, filter@qcIndex]
+              y <- assay(object, assay)[, filter@blankIndex]
+              vals <- rowBlank(x = x, y = y,
+                               na.rm = filter@na.rm,
+                               threshold = filter@threshold)
+              message(sum(vals, na.rm = TRUE) - length(object),
+                      " features were flagged")
+              rowData(object)$possible_contaminants <- vals
+              object
+          }
+)
+
+#' @noRd
+.check_index_range <- function(x, l, name = "") {
+    if (length(x) == 0 || !all(x %in% seq_len(l)))
+        stop(name, " should be between 1 and ", l)
+}
diff --git a/inst/NEWS b/inst/NEWS
index 00cf4c7d5..5fc15922b 100644
--- a/inst/NEWS
+++ b/inst/NEWS
@@ -1,3 +1,11 @@
+Changes in version 4.1.7
+----------------------
+
+- Implementation of `filterFeatures` function with `filter` parameters:
+  `RsdFilter`, `DratioFilter`, `PercentMissingFilter`, `BlankFlag`. They can be
+  used ot filter features from `XcmsResult` and `SummarizedExperiment` objects.
+- Addition of a section in the main xcms vignette to describe how to use it.
+
 Changes in version 4.1.6
 ----------------------
 
diff --git a/man/BlankFlag.Rd b/man/BlankFlag.Rd
new file mode 100644
index 000000000..578b47dc2
--- /dev/null
+++ b/man/BlankFlag.Rd
@@ -0,0 +1,84 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/method-filterFeatures.R
+\name{BlankFlag}
+\alias{BlankFlag}
+\alias{filterFeatures,XcmsResult,BlankFlag-method}
+\alias{filterFeatures,SummarizedExperiment,BlankFlag-method}
+\title{Flag features based on the intensity in blank samples}
+\usage{
+BlankFlag(
+  threshold = 2,
+  blankIndex = integer(),
+  qcIndex = integer(),
+  na.rm = TRUE
+)
+
+\S4method{filterFeatures}{XcmsResult,BlankFlag}(object, filter, ...)
+
+\S4method{filterFeatures}{SummarizedExperiment,BlankFlag}(object, filter, assay = 1)
+}
+\arguments{
+\item{threshold}{`numeric` indicates the minimum difference
+required between the mean abundance of a feature in samples compared to the
+mean abundance of the same feature in blanks for it to not be considered a
+possible contaminant. For example, the default threshold of 2 signifies that
+the mean abundance of the features in samples has to be at least twice the
+mean abundance in blanks for it to not be flagged as a possible contaminant.}
+
+\item{blankIndex}{`integer` (or `logical`) vector corresponding to the
+indices of blank samples.}
+
+\item{qcIndex}{`integer` (or `logical`) vector corresponding to the
+indices of quality control (QC) samples.}
+
+\item{na.rm}{`logical` indicates whether missing values (`NA`) should be
+removed prior to the calculations.}
+
+\item{object}{\code{XcmsExperiment} or \code{SummarizedExperiment}. For an
+\code{XcmsExperiment} object, the \code{featureValues(object)} will be evaluated, and
+for \code{Summarizedesxperiment} the \code{assay(object, assay)}. The object will be
+filtered.}
+
+\item{filter}{The parameter object selecting and configuring the type of
+filtering. It can be one of the following classes: \code{\link{RsdFilter}},
+\code{\link{DratioFilter}}, \code{\link{PercentMissingFilter}} or \code{\link{BlankFlag}}.}
+
+\item{...}{Optional parameters. For \code{object} being an \code{XcmsExperiment}:
+parameters for the \code{\link[=featureValues]{featureValues()}} call.}
+
+\item{assay}{For filtering of \code{SummarizedExperiment} objects only. Indicates
+which assay the filtering will be based on. Note that the features for the
+entire object will be removed, but the computations are performed on a single
+assay. Default is 1, which means the first assay of the \code{object} will
+be evaluated.}
+}
+\value{
+For `BlankFlag`: a `BlankFlag` class. `filterFeatures` returns
+the input object with an added column in the features metadata called
+`possible_contaminants` with a logical value for each feature. This is added
+to `featureDefinitions` for `XcmsExperiment` objects and `rowData` for
+`SummarizedExperiment` objects.
+}
+\description{
+The `BlankFlag` class and method enable users to flag features of an
+`XcmsExperiment` or `SummarizedExperiment` object based on the relationship
+between the intensity of a feature in blanks compared to the intensity in the
+samples.
+
+This class and method are part of the possible dispatch of the
+generic function `filterFeatures`. Features *below* (`<`) the user-input
+threshold will be flagged by calling the `filterFeatures` function. This
+means that an extra column will be created in `featureDefinitions` or
+`rowData` called `possible_contaminants` with a logical value for each
+feature.
+}
+\seealso{
+Other Filter features in xcms: 
+\code{\link{DratioFilter}},
+\code{\link{PercentMissingFilter}},
+\code{\link{RsdFilter}}
+}
+\author{
+Philippine Louail
+}
+\concept{Filter features in xcms}
diff --git a/man/DratioFilter.Rd b/man/DratioFilter.Rd
new file mode 100644
index 000000000..72fb90256
--- /dev/null
+++ b/man/DratioFilter.Rd
@@ -0,0 +1,89 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/method-filterFeatures.R
+\name{DratioFilter}
+\alias{DratioFilter}
+\alias{filterFeatures,XcmsResult,DratioFilter-method}
+\alias{filterFeatures,SummarizedExperiment,DratioFilter-method}
+\title{Filter features based on the dispersion ratio}
+\usage{
+DratioFilter(
+  threshold = 0.5,
+  qcIndex = integer(),
+  studyIndex = integer(),
+  na.rm = TRUE,
+  mad = FALSE
+)
+
+\S4method{filterFeatures}{XcmsResult,DratioFilter}(object, filter, ...)
+
+\S4method{filterFeatures}{SummarizedExperiment,DratioFilter}(object, filter, assay = 1)
+}
+\arguments{
+\item{threshold}{`numeric` value representing the threshold. Features with a
+D-ratio *strictly higher* (`>`) than this will be removed from the entire
+dataset.}
+
+\item{qcIndex}{`integer` (or `logical`) vector corresponding to the indices
+of QC samples.}
+
+\item{studyIndex}{`integer` (or `logical`) vector corresponding of the
+indices of study samples.}
+
+\item{na.rm}{`logical` Indicates whether missing values (`NA`) should be
+removed prior to the calculations.}
+
+\item{mad}{`logical` Indicates whether the *Median Absolute Deviation*
+(MAD) should be used instead of the standard deviation. This is suggested
+for non-gaussian distributed data.}
+
+\item{object}{\code{XcmsExperiment} or \code{SummarizedExperiment}. For an
+\code{XcmsExperiment} object, the \code{featureValues(object)} will be evaluated, and
+for \code{Summarizedesxperiment} the \code{assay(object, assay)}. The object will be
+filtered.}
+
+\item{filter}{The parameter object selecting and configuring the type of
+filtering. It can be one of the following classes: \code{\link{RsdFilter}},
+\code{\link{DratioFilter}}, \code{\link{PercentMissingFilter}} or \code{\link{BlankFlag}}.}
+
+\item{...}{Optional parameters. For \code{object} being an \code{XcmsExperiment}:
+parameters for the \code{\link[=featureValues]{featureValues()}} call.}
+
+\item{assay}{For filtering of \code{SummarizedExperiment} objects only. Indicates
+which assay the filtering will be based on. Note that the features for the
+entire object will be removed, but the computations are performed on a single
+assay. Default is 1, which means the first assay of the \code{object} will
+be evaluated.}
+}
+\value{
+For `DratioFilter`: a `DratioFilter` class. `filterFeatures` return
+the input object minus the features that did not met the user input threshold
+}
+\description{
+The `DratioFilter` class and method enable users to filter features from an
+`XcmsExperiment` or `SummarizedExperiment` object based on the D-ratio or
+*dispersion ratio*. This is defined as the standard deviation for QC
+samples divided by the standard deviation for biological test samples, for
+each feature of the object (Broadhurst et al.).
+
+This `filter` is part of the possible dispatch of the generic function
+`filterFeatures`.  Features *above* (`>`) the user-input threshold will be
+removed from the entire dataset.
+}
+\references{
+Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR,
+Dunn WB. Guidelines and considerations for the use of system suitability
+and quality control samples in mass spectrometry assays applied in
+untargeted clinical metabolomic studies. Metabolomics. 2018;14(6):72.
+doi: 10.1007/s11306-018-1367-3. Epub 2018 May 18. PMID: 29805336;
+PMCID: PMC5960010.
+}
+\seealso{
+Other Filter features in xcms: 
+\code{\link{BlankFlag}},
+\code{\link{PercentMissingFilter}},
+\code{\link{RsdFilter}}
+}
+\author{
+Philippine Louail
+}
+\concept{Filter features in xcms}
diff --git a/man/PercentMissingFilter.Rd b/man/PercentMissingFilter.Rd
new file mode 100644
index 000000000..dc550fe32
--- /dev/null
+++ b/man/PercentMissingFilter.Rd
@@ -0,0 +1,69 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/method-filterFeatures.R
+\name{PercentMissingFilter}
+\alias{PercentMissingFilter}
+\alias{filterFeatures,XcmsResult,PercentMissingFilter-method}
+\alias{filterFeatures,SummarizedExperiment,PercentMissingFilter-method}
+\title{Filter features based on the percentage of missing data}
+\usage{
+PercentMissingFilter(threshold = 30, f = factor())
+
+\S4method{filterFeatures}{XcmsResult,PercentMissingFilter}(object, filter, ...)
+
+\S4method{filterFeatures}{SummarizedExperiment,PercentMissingFilter}(object, filter, assay = 1)
+}
+\arguments{
+\item{threshold}{`numeric` percentage (between 0 and 100) of accepted missing
+values for a feature in one sample group.}
+
+\item{f}{`vector` of the same length as the `object`, specifying the sample
+type for each sample in the dataset. The percentage of missing values per
+feature will be computed within each of these sample groups. Parameter `f`,
+if not already a `factor`, will be converted to one using the factor function.
+Samples with an `NA` as their value in `f` will be excluded from calculation.}
+
+\item{object}{\code{XcmsExperiment} or \code{SummarizedExperiment}. For an
+\code{XcmsExperiment} object, the \code{featureValues(object)} will be evaluated, and
+for \code{Summarizedesxperiment} the \code{assay(object, assay)}. The object will be
+filtered.}
+
+\item{filter}{The parameter object selecting and configuring the type of
+filtering. It can be one of the following classes: \code{\link{RsdFilter}},
+\code{\link{DratioFilter}}, \code{\link{PercentMissingFilter}} or \code{\link{BlankFlag}}.}
+
+\item{...}{Optional parameters. For \code{object} being an \code{XcmsExperiment}:
+parameters for the \code{\link[=featureValues]{featureValues()}} call.}
+
+\item{assay}{For filtering of \code{SummarizedExperiment} objects only. Indicates
+which assay the filtering will be based on. Note that the features for the
+entire object will be removed, but the computations are performed on a single
+assay. Default is 1, which means the first assay of the \code{object} will
+be evaluated.}
+}
+\value{
+For `PercentMissingFilter`: a `PercentMissingFilter` class.
+`filterFeatures` return the input object minus the features that did not met
+the user input threshold
+}
+\description{
+The `PercentMissingFilter` class and method enable users to filter features
+from an `XcmsExperiment` or `SummarizedExperiment` object based on the
+percentage (values from 1 to 100) of missing values for each features in
+different sample groups and filters them according to a provided threshold.
+
+This `filter` is part of the possible dispatch of the generic function
+`filterFeatures`. Features with a percentage of missing values *higher* (`>`)
+than the user input threshold in all sample groups will be removed (i.e.
+features for which the proportion of missing values is below (`<=`) the
+threshold in at least one sample group will be retained).
+}
+\seealso{
+Other Filter features in xcms: 
+\code{\link{BlankFlag}},
+\code{\link{DratioFilter}},
+\code{\link{RsdFilter}}
+}
+\author{
+Philippine Louail
+}
+\concept{Filter features in xcms}
diff --git a/man/RsdFilter.Rd b/man/RsdFilter.Rd
new file mode 100644
index 000000000..24cc3625e
--- /dev/null
+++ b/man/RsdFilter.Rd
@@ -0,0 +1,75 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/method-filterFeatures.R
+\name{RsdFilter}
+\alias{RsdFilter}
+\alias{filterFeatures,XcmsResult,RsdFilter-method}
+\alias{filterFeatures,SummarizedExperiment,RsdFilter-method}
+\title{Filter features based on their coefficient of variation}
+\usage{
+RsdFilter(threshold = 0.3, qcIndex = integer(), na.rm = TRUE, mad = FALSE)
+
+\S4method{filterFeatures}{XcmsResult,RsdFilter}(object, filter, ...)
+
+\S4method{filterFeatures}{SummarizedExperiment,RsdFilter}(object, filter, assay = 1)
+}
+\arguments{
+\item{threshold}{`numeric` value representing the threshold. Features with a
+coefficient of variation *strictly higher* (`>`) than this will be removed
+from the entire dataset.}
+
+\item{qcIndex}{`integer` (or `logical`) vector corresponding to the indices
+of QC samples.}
+
+\item{na.rm}{`logical` indicates whether missing values (`NA`) should be
+removed prior to the calculations.}
+
+\item{mad}{`logical` indicates whether the *Median Absolute Deviation* (MAD)
+should be used instead of the standard deviation. This is suggested for
+non-gaussian distributed data.}
+
+\item{object}{\code{XcmsExperiment} or \code{SummarizedExperiment}. For an
+\code{XcmsExperiment} object, the \code{featureValues(object)} will be evaluated, and
+for \code{Summarizedesxperiment} the \code{assay(object, assay)}. The object will be
+filtered.}
+
+\item{filter}{The parameter object selecting and configuring the type of
+filtering. It can be one of the following classes: \code{\link{RsdFilter}},
+\code{\link{DratioFilter}}, \code{\link{PercentMissingFilter}} or \code{\link{BlankFlag}}.}
+
+\item{...}{Optional parameters. For \code{object} being an \code{XcmsExperiment}:
+parameters for the \code{\link[=featureValues]{featureValues()}} call.}
+
+\item{assay}{For filtering of \code{SummarizedExperiment} objects only. Indicates
+which assay the filtering will be based on. Note that the features for the
+entire object will be removed, but the computations are performed on a single
+assay. Default is 1, which means the first assay of the \code{object} will
+be evaluated.}
+}
+\value{
+For `RsdFilter`: a `RsdFilter` class. `filterFeatures` return the
+input object minus the features that did not met the user input threshold.
+}
+\description{
+The `RsdFilter` class and methods enable users to filter features from an
+`XcmsExperiment` or `SummarizedExperiment` object based on their relative
+standard deviation (coefficient of variation) for a specified threshold.
+
+This `filter` is part of the possible dispatch of the generic function
+`filterFeatures`. Features *above* (`>`) the user-input threshold will be
+removed from the entire dataset.
+}
+\note{
+It is assumed that the abundance values are in natural scale. Abundances in
+log scale should be first transformed to natural scale before calculating
+the RSD.
+}
+\seealso{
+Other Filter features in xcms: 
+\code{\link{BlankFlag}},
+\code{\link{DratioFilter}},
+\code{\link{PercentMissingFilter}}
+}
+\author{
+Philippine Louail
+}
+\concept{Filter features in xcms}
diff --git a/man/filterFeatures.Rd b/man/filterFeatures.Rd
new file mode 100644
index 000000000..d55c057e4
--- /dev/null
+++ b/man/filterFeatures.Rd
@@ -0,0 +1,121 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/method-filterFeatures.R
+\name{filterFeatures}
+\alias{filterFeatures}
+\title{Filtering of features based on conventional quality assessment}
+\arguments{
+\item{object}{\code{XcmsExperiment} or \code{SummarizedExperiment}. For an
+\code{XcmsExperiment} object, the \code{featureValues(object)} will be evaluated, and
+for \code{Summarizedesxperiment} the \code{assay(object, assay)}. The object will be
+filtered.}
+
+\item{filter}{The parameter object selecting and configuring the type of
+filtering. It can be one of the following classes: \code{\link{RsdFilter}},
+\code{\link{DratioFilter}}, \code{\link{PercentMissingFilter}} or \code{\link{BlankFlag}}.}
+
+\item{assay}{For filtering of \code{SummarizedExperiment} objects only. Indicates
+which assay the filtering will be based on. Note that the features for the
+entire object will be removed, but the computations are performed on a single
+assay. Default is 1, which means the first assay of the \code{object} will
+be evaluated.}
+
+\item{...}{Optional parameters. For \code{object} being an \code{XcmsExperiment}:
+parameters for the \code{\link[=featureValues]{featureValues()}} call.}
+}
+\description{
+When dealing with metabolomics results, it is often necessary to filter
+features based on certain criteria. These criteria are typically derived
+from statistical formulas applied to full rows of data, where each row
+represents a feature and its abundance of signal in each samples.
+The \code{filterFeatures} function filters features based on these conventional
+quality assessment criteria. Multiple types of filtering are implemented and
+can be defined by the \code{filter} argument.
+
+Supported \code{filter} arguments are:
+\itemize{
+\item \code{\link{RsdFilter}}: Calculates the relative standard deviation
+(i.e. coefficient of variation) in abundance for each feature in QC
+(Quality Control) samples and filters them in the input object according to
+a provided threshold.
+\item \code{\link{DratioFilter}}: Computes the D-ratio or \emph{dispersion ratio}, defined as
+the standard deviation in abundance for QC samples divided by the standard
+deviation for biological test samples, for each feature and filters them
+according to a provided threshold.
+\item \code{\link{PercentMissingFilter}}: Determines the percentage of missing values for
+each feature in the various sample groups and filters them according to a
+provided threshold.
+\item \code{\link{BlankFlag}}: Identifies features where the mean abundance in test samples
+is lower than a specified multiple of the mean abundance of blank samples.
+This can be used to flag features that result from contamination in the
+solvent of the samples. A new column \code{possible_contaminants} is added to the
+\code{featureDefinitions} (\code{XcmsExperiment} object) or \code{rowData}
+(\code{SummarizedExperiment} object) reflecting this.
+}
+
+For specific examples, see the help pages of the individual parameter classes
+listed above.
+}
+\examples{
+## See the vignettes for more detailed examples
+library(MsExperiment)
+
+## Load a test data set with features defined.
+test_xcms <- loadXcmsData()
+## Set up parameter to filter based on coefficient of variation. By setting
+## the filter such as below, features that have a coefficient of variation
+## superior to 0.3 in QC samples will be removed from the object `test_xcms`
+## when calling the `filterFeatures` function.
+
+rsd_filter <- RsdFilter(threshold = 0.3,
+                        qcIndex = sampleData(test_xcms)$sample_type == "QC")
+
+filtered_data_rsd <- filterFeatures(object = test_xcms, filter = rsd_filter)
+
+## Set up parameter to filter based on D-ratio. By setting the filter such
+## as below, features that have a D-ratio computed based on their abundance
+## between QC and study samples superior to 0.5 will be removed from the
+## object `test_xcms`.
+
+dratio_filter <- DratioFilter(threshold = 0.5,
+                 qcIndex = sampleData(test_xcms)$sample_type == "QC",
+                 studyIndex = sampleData(test_xcms)$sample_type == "study")
+
+filtered_data_dratio <- filterFeatures(object = test_xcms,
+                                       filter = dratio_filter)
+
+## Set up parameter to filter based on the percent of missing data.
+## Parameter f should represent the sample group of samples, for which the
+## percentage of missing values will be evaluated. As the setting is defined
+## bellow, if a feature as less (or equal) to 30\% missing values in one
+## sample group, it will be kept in the `test_xcms` object.
+
+missing_data_filter <- PercentMissingFilter(threshold = 30,
+                                       f = sampleData(test_xcms)$sample_type)
+
+filtered_data_missing <- filterFeatures(object = test_xcms,
+                                        filter = missing_data_filter)
+
+## Set up parameter to flag possible contaminants based on blank samples'
+## abundance. By setting the filter such as below, features that have mean
+## abundance ratio between blank(here use study as an example) and QC
+## samples less than 2 will be marked as `TRUE` in an extra column named
+## `possible_contaminants` in the `featureDefinitions` table of the object
+## `test_xcms`.
+
+filter <- BlankFlag(threshold = 2,
+                    qcIndex = sampleData(test_xcms)$sample_type == "QC",
+                    blankIndex = sampleData(test_xcms)$sample_type == "study")
+filtered_xmse <- filterFeatures(test_xcms, filter)
+
+}
+\references{
+Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR,
+Dunn WB. Guidelines and considerations for the use of system suitability
+and quality control samples in mass spectrometry assays applied in
+untargeted clinical metabolomic studies. Metabolomics. 2018;14(6):72.
+doi: 10.1007/s11306-018-1367-3. Epub 2018 May 18. PMID: 29805336;
+PMCID: PMC5960010.
+}
+\author{
+Philippine Louail
+}
diff --git a/tests/testthat/test_function-filtering.R b/tests/testthat/test_function-filtering.R
new file mode 100644
index 000000000..b7e0220b0
--- /dev/null
+++ b/tests/testthat/test_function-filtering.R
@@ -0,0 +1,119 @@
+xmse_full <- loadXcmsData("xmse")
+
+test_that("RsdFilter", {
+    # Test default parameters
+    filter <- RsdFilter()
+    expect_is(filter, "RsdFilter")
+    expect_equal(filter@threshold, 0.3)
+    expect_equal(filter@qcIndex, integer())
+    expect_equal(filter@na.rm, TRUE)
+    expect_equal(filter@mad, FALSE)
+
+    #test with XcmsExperiment object
+    filter <- RsdFilter(qcIndex = sampleData(xmse_full)$sample_type == "QC")
+    filtered_xmse <- filterFeatures(xmse_full, filter)
+    expect_lte(nrow(featureDefinitions(filtered_xmse)),
+               nrow(featureDefinitions(xmse_full)))
+
+    #test error message when index too small of too large
+    filter_1 <- RsdFilter(qcIndex = integer())
+    filter_2 <- RsdFilter(qcIndex = c(sampleData(xmse_full)$sample_type == "QC",
+                                      TRUE))
+    filter_3 <- RsdFilter(qcIndex = c(2, 15))
+    expect_error(filterFeatures(xmse_full, filter_1))
+    expect_error(filterFeatures(xmse_full, filter_2))
+    expect_error(filterFeatures(xmse_full, filter_3))
+
+    #test in SummarizedExperiment object
+    res <- quantify(xmse_full)
+    filter <- RsdFilter(qcIndex = res$sample_type == "QC")
+    filtered_res <- filterFeatures(res, filter)
+    expect_lte(nrow(filtered_res), nrow(res))
+
+    #test same amount of features in in both object type
+    expect_equal(nrow(featureDefinitions(filtered_xmse)),
+               nrow(filtered_res))
+})
+
+test_that("DratioFilter", {
+    # Test default parameters
+    filter <- DratioFilter()
+    expect_is(filter, "DratioFilter")
+    expect_equal(filter@threshold, 0.5)
+    expect_equal(filter@qcIndex, integer())
+    expect_equal(filter@studyIndex, integer())
+    expect_equal(filter@na.rm, TRUE)
+    expect_equal(filter@mad, FALSE)
+
+    #test with XcmsExperiment object
+    filter <- DratioFilter(
+        qcIndex = sampleData(xmse_full)$sample_type == "QC",
+        studyIndex = sampleData(xmse_full)$sample_type == "study")
+    filtered_xmse <- filterFeatures(xmse_full, filter)
+    expect_lte(nrow(featureDefinitions(filtered_xmse)),
+               nrow(featureDefinitions(xmse_full)))
+
+    #test in SummarizedExperiment object
+    res <- quantify(xmse_full)
+    filter <- DratioFilter(qcIndex = res$sample_type == "QC",
+                           studyIndex = res$sample_type == "study")
+    filtered_res <- filterFeatures(res, filter)
+    expect_lte(nrow(filtered_res), nrow(res))
+
+    #test same amount of features in in both object type
+    expect_equal(nrow(featureDefinitions(filtered_xmse)),
+                 nrow(filtered_res))
+})
+
+test_that("PercentMissingFilter", {
+    # Test default parameters
+    filter <- PercentMissingFilter()
+    expect_is(filter, "PercentMissingFilter")
+    expect_equal(filter@threshold, 30)
+    expect_equal(filter@f, factor())
+
+    #test with XcmsExperiment object
+    filter <- PercentMissingFilter(f = sampleData(xmse_full)$sample_type)
+    filtered_xmse <- filterFeatures(xmse_full, filter)
+    expect_lte(nrow(featureDefinitions(filtered_xmse)),
+               nrow(featureDefinitions(xmse_full)))
+
+    #test in SummarizedExperiment object
+    res <- quantify(xmse_full)
+    filter <- PercentMissingFilter(f = res$sample_type)
+    filtered_res <- filterFeatures(res, filter)
+    expect_lte(nrow(filtered_res), nrow(res))
+
+    #test same amount of features in in both object type
+    expect_equal(nrow(featureDefinitions(filtered_xmse)),
+                 nrow(filtered_res))
+})
+
+test_that("BlankFlag", {
+    # Test default parameters
+    filter <- BlankFlag()
+    expect_is(filter, "BlankFlag")
+    expect_equal(filter@threshold, 2)
+    expect_equal(filter@na.rm, TRUE)
+    expect_equal(filter@qcIndex, integer())
+    expect_equal(filter@blankIndex, integer())
+
+    #test with XcmsExperiment object - using study sample as blanks
+    filter <- BlankFlag(
+        qcIndex = sampleData(xmse_full)$sample_type == "QC",
+        blankIndex = sampleData(xmse_full)$sample_type == "study")
+    filtered_xmse <- filterFeatures(xmse_full, filter)
+    expect_true("possible_contaminants" %in%
+                    colnames(featureDefinitions(filtered_xmse)))
+
+    #test in SummarizedExperiment object - using study sample as blanks
+    res <- quantify(xmse_full)
+    filter <- BlankFlag(qcIndex = res$sample_type == "QC",
+                        blankIndex = res$sample_type == "study")
+    filtered_res <- filterFeatures(res, filter)
+    expect_true("possible_contaminants" %in% colnames(rowData(filtered_res)))
+
+    expect_equal(sum(featureDefinitions(filtered_xmse)$possible_contaminants,
+                     na.rm = TRUE),
+                 sum(rowData(filtered_res)$possible_contaminants, na.rm = TRUE))
+})
diff --git a/vignettes/xcms.Rmd b/vignettes/xcms.Rmd
index e0c2bcbb7..29e76a95b 100644
--- a/vignettes/xcms.Rmd
+++ b/vignettes/xcms.Rmd
@@ -1153,6 +1153,121 @@ properties of the mice analyzed (sex, age, litter mates etc).
 
 # Further data processing and analysis
 
+## Quality-based filtering of features
+
+When dealing with metabolomics results, it is often necessary to filter 
+features based on certain criteria. These criteria are typically derived from
+statistical formulas applied to full rows of data, where each row represents a
+feature. The `filterFeatures` function provides a robust solution for filtering
+features based on these conventional quality assessment criteria. It supports
+multiple types of filtering, allowing users to tailor the filtering process to
+their specific needs, all controlled by the `filter` argument. This function
+and its implementations are applicable to both `XcmsExperiment` results objects
+and `SummarizedExperiment` objects.
+
+We will demonstrate how to use the `filterFeatures` function to perform quality
+assessment and filtering on both the `faahko` and `res` variables defined above.
+The `filter` argument can accommodate various types of input, each determining the
+specific type of quality assessment and filtering to be performed.
+
+The `RsdFilter` enable users to filter features based on their relative
+standard deviation (coefficient of variation) for a specified `threshold`. It
+is recommended to base the computation on quality control (QC) samples,
+as demonstrated below:
+
+```{r}
+# Set up parameters for RsdFilter 
+rsd_filter <- RsdFilter(threshold = 0.3,
+                        qcIndex = sampleData(faahko)$sample_type == "QC")
+
+# Apply the filter to faakho object
+filtered_faahko <- filterFeatures(object = faahko, filter = rsd_filter)
+
+# Now apply the same strategy to the res object
+rsd_filter <- RsdFilter(threshold = 0.3, qcIndex = res$sample_type == "QC")
+filtered_res <- filterFeatures(object = res, filter = rsd_filter, assay = "raw")
+```
+
+All features with an RSD (CV) strictly larger than 0.3 in QC samples were thus
+removed from the data set.
+
+The `DratioFilter`  can be used to filter features based on the D-ratio or
+*dispersion ratio*, which compares the standard deviation in QC samples to that
+in study samples.
+
+```{r}
+# Set up parameters for DratioFilter
+dratio_filter <- DratioFilter(
+    threshold = 0.5,
+    qcIndex = sampleData(filtered_faahko)$sample_type == "QC",
+    studyIndex = sampleData(filtered_faahko)$sample_type == "study")
+
+# Apply the filter to faahko object
+filtered_faakho <- filterFeatures(object = filtered_faahko,
+                                  filter = dratio_filter)
+
+# Now same but for the res object
+dratio_filter <- DratioFilter(
+    threshold = 0.5,
+    qcIndex = filtered_res$sample_type == "QC",
+    studyIndex = filtered_res$sample_type == "study")
+
+filtered_res <- filterFeatures(object = filtered_res,
+                               filter = dratio_filter)
+```
+
+All features with an D-ratio strictly larger than 0.5 were thus removed from
+the data set.
+
+The `PercentMissingFilter` allows to filter features based on the percentage of
+missing values for each feature. This function takes as an input the parameter
+`f` which is supposed to be a vector of length equal to the length of the object
+(i.e. number of samples) with the sample type for each. The function then
+computes the percentage of missing values per sample groups and filters
+features based on this. Features with a percent of missing values larger than
+the threshold in all sample groups will be removed. Another option is to base
+this quality assessment and filtering only on QC samples.
+
+Both examples are shown below: 
+
+```{r}
+# To set up parameter `f` to filter only based on QC samples
+f <- sampleData(filtered_faakho)$sample_type
+f[f != "QC"] <- NA 
+
+# To set up parameter `f` to filter per sample type excluding QC samples
+f <- sampleData(filtered_faakho)$sample_type
+f[f == "QC"] <- NA 
+
+missing_filter <- PercentMissingFilter(threshold = 30,
+                                       f = f)
+
+# Apply the filter to faakho object
+filtered_faakho <- filterFeatures(object = filtered_faakho,
+                                  filter = missing_filter)
+
+# Apply the filter to res object
+missing_filter <- PercentMissingFilter(threshold = 30,
+                                       f = f)
+filtered_res <- filterFeatures(object = filtered_res,
+                               filter = missing_filter)
+```
+
+Here, no feature was removed, meaning that all the features had less than 30% 
+of `NA` values in at least one of the sample type.
+
+Although not directly relevant to this experiment, the `BlankFlag` filter can be
+used to flag features based on the intensity relationship between blank and QC
+samples. More information can be found in the documentation of the filter:
+
+```{r}
+# Retrieve documentation for the main function and the specific filter.
+?filterFeatures
+?BlankFlag
+```
+
+## Normalization
+
 Normalizing features' signal intensities is required, but at present not (yet)
 supported in `xcms` (some methods might be added in near future). It is advised
 to use the `SummarizedExperiment` returned by the `quantify` method for any