The filterFeatures methods enables users to filter features
based on a variable in their rowData. The features matching the
filter will be returned as a new object of class QFeatures. The
filters can be provided as instances of class AnnotationFilter
(see below) or as formulas.
Usage
VariableFilter(field, value, condition = "==", not = FALSE)
# S4 method for class 'QFeatures,AnnotationFilter'
filterFeatures(object, filter, i, na.rm = FALSE, keep = FALSE, ...)
# S4 method for class 'QFeatures,formula'
filterFeatures(object, filter, i, na.rm = FALSE, keep = FALSE, ...)Arguments
- field
character(1)refering to the name of the variable to apply the filter on.- value
character()orinteger()value for theCharacterVariableFilterandNumericVariableFilterfilters respectively.- condition
character(1)defining the condition to be used in the filter. ForNumericVariableFilter, one of"==","!=",">","<",">="or"<=". ForCharacterVariableFilter, one of"==","!=","startsWith","endsWith"or"contains". Default condition is"==".- not
logical(1)indicating whether the filtering should be negated or not.TRUEindicates is negated (!).FALSEindicates not negated. DefaultnotisFALSE, so no negation.- object
An instance of class QFeatures.
- filter
Either an instance of class AnnotationFilter or a formula.
- i
A numeric, logical or character vector pointing to the assay(s) to be filtered.
- na.rm
logical(1)indicating whether missing values should be removed. Default isFALSE.- keep
logical(1)indicating whether to keep the features of assays for which at least one of the filtering variables are missing in the rowData. WhenFALSE(default), all such assay will contain 0 features; whenTRUE, the assays are untouched.- ...
Additional parameters. Currently ignored.
The filtering procedure
filterFeatures() will go through each assay of the QFeatures
object and apply the filtering on the corresponding rowData.
Features that do not pass the filter condition are removed from
the assay. In some cases, one may want to filter for a variable
present in some assay, but not in other. There are two options:
either provide keep = FALSE to remove all features for those
assays (and thus leaving an empty assay), or provide keep = TRUE
to ignore filtering for those assays.
Because features in a QFeatures object are linked between different
assays with AssayLinks, the links are automatically updated.
However, note that the function doesn't propagate the filter to parent
assays. For example, suppose a peptide assay with 4 peptides is
linked to a protein assay with 2 proteins (2 peptides mapped per
protein) and you apply filterFeatures(). All features pass the
filter except for one protein. The peptides mapped to that protein
will remain in the QFeatures object. If propagation of the
filtering rules to parent assay is desired, you may want to use
x[i, , ] instead (see the Subsetting section in ?QFeature).
Variable filters
The variable filters are filters as defined in the
AnnotationFilter package. In addition to the pre-defined filter,
users can arbitrarily set a field on which to operate. These
arbitrary filters operate either on a character variables (as
CharacterVariableFilter objects) or numerics (as
NumericVariableFilters objects), which can be created with the
VariableFilter constructor.
See also
The QFeatures man page for subsetting and the QFeatures
vignette provides an extended example.
Examples
## ----------------------------------------
## Creating character and numberic
## variable filters
## ----------------------------------------
VariableFilter(field = "my_var",
value = "value_to_keep",
condition = "==")
#> class: CharacterVariableFilter
#> condition: ==
#> value: value_to_keep
VariableFilter(field = "my_num_var",
value = 0.05,
condition = "<=")
#> class: NumericVariableFilter
#> condition: <=
#> value: 0.05
example(aggregateFeatures)
#>
#> aggrgF> ## ---------------------------------------
#> aggrgF> ## An example QFeatures with PSM-level data
#> aggrgF> ## ---------------------------------------
#> aggrgF> data(feat1)
#>
#> aggrgF> feat1
#> An instance of class QFeatures (type: bulk) with 1 set:
#>
#> [1] psms: SummarizedExperiment with 10 rows and 2 columns
#>
#> aggrgF> ## Aggregate PSMs into peptides
#> aggrgF> feat1 <- aggregateFeatures(feat1, "psms", "Sequence", name = "peptides")
#>
Aggregated: 1/1
#>
#> aggrgF> feat1
#> An instance of class QFeatures (type: bulk) with 2 sets:
#>
#> [1] psms: SummarizedExperiment with 10 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 3 rows and 2 columns
#>
#> aggrgF> ## Aggregate peptides into proteins
#> aggrgF> feat1 <- aggregateFeatures(feat1, "peptides", "Protein", name = "proteins")
#>
Aggregated: 1/1
#>
#> aggrgF> feat1
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 10 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 3 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 2 rows and 2 columns
#>
#> aggrgF> assay(feat1[[1]])
#> S1 S2
#> PSM1 1 11
#> PSM2 2 12
#> PSM3 3 13
#> PSM4 4 14
#> PSM5 5 15
#> PSM6 6 16
#> PSM7 7 17
#> PSM8 8 18
#> PSM9 9 19
#> PSM10 10 20
#>
#> aggrgF> assay(feat1[[2]])
#> S1 S2
#> ELGNDAYK 5.0 15.0
#> IAEESNFPFIK 8.5 18.5
#> SYGFNAAR 2.0 12.0
#>
#> aggrgF> aggcounts(feat1[[2]])
#> S1 S2
#> ELGNDAYK 3 3
#> IAEESNFPFIK 4 4
#> SYGFNAAR 3 3
#>
#> aggrgF> assay(feat1[[3]])
#> S1 S2
#> ProtA 3.5 13.5
#> ProtB 8.5 18.5
#>
#> aggrgF> aggcounts(feat1[[3]])
#> S1 S2
#> ProtA 2 2
#> ProtB 1 1
#>
#> aggrgF> ## --------------------------------------------
#> aggrgF> ## Aggregation with missing quantitative values
#> aggrgF> ## --------------------------------------------
#> aggrgF> data(ft_na)
#>
#> aggrgF> ft_na
#> An instance of class QFeatures (type: bulk) with 1 set:
#>
#> [1] na: SummarizedExperiment with 4 rows and 3 columns
#>
#> aggrgF> assay(ft_na[[1]])
#> A B C
#> a NA 5 9
#> b 2 6 10
#> c 3 NA 11
#> d NA 8 12
#>
#> aggrgF> rowData(ft_na[[1]])
#> DataFrame with 4 rows and 2 columns
#> X Y
#> <integer> <character>
#> a 1 A
#> b 2 B
#> c 1 A
#> d 2 B
#>
#> aggrgF> ## By default, missing values are propagated
#> aggrgF> ft2 <- aggregateFeatures(ft_na, 1, fcol = "X", fun = colSums)
#> Your quantitative data contain missing values. Please read the relevant
#> section(s) in the aggregateFeatures manual page regarding the effects
#> of missing values on data aggregation.
#>
Aggregated: 1/1
#>
#> aggrgF> assay(ft2[[2]])
#> A B C
#> 1 NA NA 20
#> 2 NA 14 22
#>
#> aggrgF> aggcounts(ft2[[2]])
#> A B C
#> 1 1 1 2
#> 2 1 2 2
#>
#> aggrgF> ## The rowData .n variable tallies number of initial rows that
#> aggrgF> ## were aggregated (irrespective of NAs) for all the samples.
#> aggrgF> rowData(ft2[[2]])
#> DataFrame with 2 rows and 3 columns
#> X Y .n
#> <integer> <character> <integer>
#> 1 1 A 2
#> 2 2 B 2
#>
#> aggrgF> ## Ignored when setting na.rm = TRUE
#> aggrgF> ft3 <- aggregateFeatures(ft_na, 1, fcol = "X", fun = colSums, na.rm = TRUE)
#> Your quantitative data contain missing values. Please read the relevant
#> section(s) in the aggregateFeatures manual page regarding the effects
#> of missing values on data aggregation.
#>
Aggregated: 1/1
#>
#> aggrgF> assay(ft3[[2]])
#> A B C
#> 1 3 5 20
#> 2 2 14 22
#>
#> aggrgF> aggcounts(ft3[[2]])
#> A B C
#> 1 1 1 2
#> 2 1 2 2
#>
#> aggrgF> ## -----------------------------------------------
#> aggrgF> ## Aggregation with missing values in the row data
#> aggrgF> ## -----------------------------------------------
#> aggrgF> ## Row data results without any NAs, which includes the
#> aggrgF> ## Y variables
#> aggrgF> rowData(ft2[[2]])
#> DataFrame with 2 rows and 3 columns
#> X Y .n
#> <integer> <character> <integer>
#> 1 1 A 2
#> 2 2 B 2
#>
#> aggrgF> ## Missing value in the Y feature variable
#> aggrgF> rowData(ft_na[[1]])[1, "Y"] <- NA
#>
#> aggrgF> rowData(ft_na[[1]])
#> DataFrame with 4 rows and 2 columns
#> X Y
#> <integer> <character>
#> a 1 NA
#> b 2 B
#> c 1 A
#> d 2 B
#>
#> aggrgF> ft3 <- aggregateFeatures(ft_na, 1, fcol = "X", fun = colSums)
#> Your quantitative and row data contain missing values. Please read the
#> relevant section(s) in the aggregateFeatures manual page regarding the
#> effects of missing values on data aggregation.
#>
Aggregated: 1/1
#>
#> aggrgF> ## The Y feature variable has been dropped!
#> aggrgF> assay(ft3[[2]])
#> A B C
#> 1 NA NA 20
#> 2 NA 14 22
#>
#> aggrgF> rowData(ft3[[2]])
#> DataFrame with 2 rows and 2 columns
#> X .n
#> <integer> <integer>
#> 1 1 2
#> 2 2 2
#>
#> aggrgF> ## --------------------------------------------
#> aggrgF> ## Using a peptide-by-proteins adjacency matrix
#> aggrgF> ## --------------------------------------------
#> aggrgF>
#> aggrgF> ## Let's use assay peptides from object feat1 and
#> aggrgF> ## define that peptide SYGFNAAR maps to proteins
#> aggrgF> ## Prot A and B
#> aggrgF>
#> aggrgF> se <- feat1[["peptides"]]
#>
#> aggrgF> rowData(se)$Protein[3] <- c("ProtA;ProtB")
#>
#> aggrgF> rowData(se)
#> DataFrame with 3 rows and 4 columns
#> Sequence Protein location .n
#> <character> <character> <character> <integer>
#> ELGNDAYK ELGNDAYK ProtA Mitochondr... 3
#> IAEESNFPFIK IAEESNFPFI... ProtB unknown 4
#> SYGFNAAR SYGFNAAR ProtA;Prot... Mitochondr... 3
#>
#> aggrgF> ## This can also be defined using anadjacency matrix, manual
#> aggrgF> ## encoding here. See PSMatch::makeAdjacencyMatrix() for a
#> aggrgF> ## function that does it automatically.
#> aggrgF> adj <- matrix(0, nrow = 3, ncol = 2,
#> aggrgF+ dimnames = list(rownames(se),
#> aggrgF+ c("ProtA", "ProtB")))
#>
#> aggrgF> adj[1, 1] <- adj[2, 2] <- adj[3, 1:2] <- 1
#>
#> aggrgF> adj
#> ProtA ProtB
#> ELGNDAYK 1 0
#> IAEESNFPFIK 0 1
#> SYGFNAAR 1 1
#>
#> aggrgF> adjacencyMatrix(se) <- adj
#>
#> aggrgF> rowData(se)
#> DataFrame with 3 rows and 5 columns
#> Sequence Protein location .n adjacencyMatrix
#> <character> <character> <character> <integer> <dgCMatrix>
#> ELGNDAYK ELGNDAYK ProtA Mitochondr... 3 1:0
#> IAEESNFPFIK IAEESNFPFI... ProtB unknown 4 0:1
#> SYGFNAAR SYGFNAAR ProtA;Prot... Mitochondr... 3 1:1
#>
#> aggrgF> adjacencyMatrix(se)
#> 3 x 2 sparse Matrix of class "dgCMatrix"
#> ProtA ProtB
#> ELGNDAYK 1 .
#> IAEESNFPFIK . 1
#> SYGFNAAR 1 1
#>
#> aggrgF> ## Aggregation using the adjacency matrix
#> aggrgF> se2 <- aggregateFeatures(se, fcol = "adjacencyMatrix",
#> aggrgF+ fun = MsCoreUtils::colMeansMat)
#>
#> aggrgF> ## Peptide SYGFNAAR was taken into account in both ProtA and ProtB
#> aggrgF> ## aggregations.
#> aggrgF> assay(se2)
#> S1 S2
#> ProtA 3.50 13.50
#> ProtB 5.25 15.25
#>
#> aggrgF> ## Aggregation by matrix on a QFeature object works as with a
#> aggrgF> ## vector
#> aggrgF> ft <- QFeatures(list(peps = se))
#>
#> aggrgF> ft <- aggregateFeatures(ft, "peps", "adjacencyMatrix", name = "protsByMat",
#> aggrgF+ fun = MsCoreUtils::colMeansMat)
#>
Aggregated: 1/1
#>
#> aggrgF> assay(ft[[2]])
#> S1 S2
#> ProtA 3.50 13.50
#> ProtB 5.25 15.25
#>
#> aggrgF> rowData(ft[[2]])
#> DataFrame with 2 rows and 1 column
#> .n
#> <integer>
#> ProtA 2
#> ProtB 2
## ----------------------------------------------------------------
## Filter all features that are associated to the Mitochondrion in
## the location feature variable. This variable is present in all
## assays.
## ----------------------------------------------------------------
## using the forumla interface, exact mathc
filterFeatures(feat1, ~ location == "Mitochondrion")
#> 'location' found in 3 out of 3 assay(s).
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 6 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 2 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 1 rows and 2 columns
## using the forumula intefrace, martial match
filterFeatures(feat1, ~startsWith(location, "Mito"))
#> 'location' found in 3 out of 3 assay(s).
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 6 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 2 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 1 rows and 2 columns
## using a user-defined character filter
filterFeatures(feat1, VariableFilter("location", "Mitochondrion"))
#> 'location' found in 3 out of 3 assay(s).
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 6 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 2 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 1 rows and 2 columns
## using a user-defined character filter with partial match
filterFeatures(feat1, VariableFilter("location", "Mito", "startsWith"))
#> 'location' found in 3 out of 3 assay(s).
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 6 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 2 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 1 rows and 2 columns
filterFeatures(feat1, VariableFilter("location", "itochon", "contains"))
#> 'location' found in 3 out of 3 assay(s).
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 6 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 2 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 1 rows and 2 columns
## ----------------------------------------------------------------
## Filter all features that aren't marked as unknown (sub-cellular
## location) in the feature variable
## ----------------------------------------------------------------
## using a user-defined character filter
filterFeatures(feat1, VariableFilter("location", "unknown", condition = "!="))
#> 'location' found in 3 out of 3 assay(s).
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 6 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 2 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 1 rows and 2 columns
## using the forumula interface
filterFeatures(feat1, ~ location != "unknown")
#> 'location' found in 3 out of 3 assay(s).
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 6 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 2 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 1 rows and 2 columns
## ----------------------------------------------------------------
## Filter features that have a p-values lower or equal to 0.03
## ----------------------------------------------------------------
## using a user-defined numeric filter
filterFeatures(feat1, VariableFilter("pval", 0.03, "<="))
#> 'pval' found in 1 out of 3 assay(s).
#> No filter applied to the following assay(s) because one or more
#> filtering variables are missing in the rowData: peptides, proteins. You
#> can control whether to remove or keep the features using the 'keep'
#> argument (see '?filterFeature').
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 3 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 0 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 0 rows and 2 columns
## using the formula interface
filterFeatures(feat1, ~ pval <= 0.03)
#> 'pval' found in 1 out of 3 assay(s).
#> No filter applied to the following assay(s) because one or more
#> filtering variables are missing in the rowData: peptides, proteins. You
#> can control whether to remove or keep the features using the 'keep'
#> argument (see '?filterFeature').
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 3 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 0 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 0 rows and 2 columns
## you can also remove all p-values that are NA (if any)
filterFeatures(feat1, ~ !is.na(pval))
#> 'pval' found in 1 out of 3 assay(s).
#> No filter applied to the following assay(s) because one or more
#> filtering variables are missing in the rowData: peptides, proteins. You
#> can control whether to remove or keep the features using the 'keep'
#> argument (see '?filterFeature').
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 10 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 0 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 0 rows and 2 columns
## ----------------------------------------------------------------
## Negative control - filtering for an non-existing markers value,
## returning empty results.
## ----------------------------------------------------------------
filterFeatures(feat1, VariableFilter("location", "not"))
#> 'location' found in 3 out of 3 assay(s).
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 0 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 0 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 0 rows and 2 columns
filterFeatures(feat1, ~ location == "not")
#> 'location' found in 3 out of 3 assay(s).
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] psms: SummarizedExperiment with 0 rows and 2 columns
#> [2] peptides: SummarizedExperiment with 0 rows and 2 columns
#> [3] proteins: SummarizedExperiment with 0 rows and 2 columns
## ----------------------------------------------------------------
## Filtering for a missing feature variable. The outcome is controled
## by keep
## ----------------------------------------------------------------
data(feat2)
filterFeatures(feat2, ~ y < 0)
#> 'y' found in 2 out of 3 assay(s).
#> No filter applied to the following assay(s) because one or more
#> filtering variables are missing in the rowData: assay1. You can control
#> whether to remove or keep the features using the 'keep' argument (see
#> '?filterFeature').
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] assay1: SummarizedExperiment with 0 rows and 4 columns
#> [2] assay2: SummarizedExperiment with 1 rows and 4 columns
#> [3] assay3: SummarizedExperiment with 5 rows and 4 columns
filterFeatures(feat2, ~ y < 0, keep = TRUE)
#> 'y' found in 2 out of 3 assay(s).
#> An instance of class QFeatures (type: bulk) with 3 sets:
#>
#> [1] assay1: SummarizedExperiment with 10 rows and 4 columns
#> [2] assay2: SummarizedExperiment with 1 rows and 4 columns
#> [3] assay3: SummarizedExperiment with 5 rows and 4 columns
## ----------------------------------------------------------------
## Example with missing values
## ----------------------------------------------------------------
data(feat1)
rowData(feat1[[1]])[1, "location"] <- NA
rowData(feat1[[1]])
#> DataFrame with 10 rows and 5 columns
#> Sequence Protein Var location pval
#> <character> <character> <integer> <character> <numeric>
#> PSM1 SYGFNAAR ProtA 1 NA 0.084
#> PSM2 SYGFNAAR ProtA 2 Mitochondr... 0.077
#> PSM3 SYGFNAAR ProtA 3 Mitochondr... 0.063
#> PSM4 ELGNDAYK ProtA 4 Mitochondr... 0.073
#> PSM5 ELGNDAYK ProtA 5 Mitochondr... 0.012
#> PSM6 ELGNDAYK ProtA 6 Mitochondr... 0.011
#> PSM7 IAEESNFPFI... ProtB 7 unknown 0.075
#> PSM8 IAEESNFPFI... ProtB 8 unknown 0.038
#> PSM9 IAEESNFPFI... ProtB 9 unknown 0.028
#> PSM10 IAEESNFPFI... ProtB 10 unknown 0.097
## The row with the NA is not removed
rowData(filterFeatures(feat1, ~ location == "Mitochondrion")[[1]])
#> 'location' found in 1 out of 1 assay(s).
#> DataFrame with 6 rows and 5 columns
#> Sequence Protein Var location pval
#> <character> <character> <integer> <character> <numeric>
#> PSM1 SYGFNAAR ProtA 1 NA 0.084
#> PSM2 SYGFNAAR ProtA 2 Mitochondr... 0.077
#> PSM3 SYGFNAAR ProtA 3 Mitochondr... 0.063
#> PSM4 ELGNDAYK ProtA 4 Mitochondr... 0.073
#> PSM5 ELGNDAYK ProtA 5 Mitochondr... 0.012
#> PSM6 ELGNDAYK ProtA 6 Mitochondr... 0.011
rowData(filterFeatures(feat1, ~ location == "Mitochondrion", na.rm = FALSE)[[1]])
#> 'location' found in 1 out of 1 assay(s).
#> DataFrame with 6 rows and 5 columns
#> Sequence Protein Var location pval
#> <character> <character> <integer> <character> <numeric>
#> PSM1 SYGFNAAR ProtA 1 NA 0.084
#> PSM2 SYGFNAAR ProtA 2 Mitochondr... 0.077
#> PSM3 SYGFNAAR ProtA 3 Mitochondr... 0.063
#> PSM4 ELGNDAYK ProtA 4 Mitochondr... 0.073
#> PSM5 ELGNDAYK ProtA 5 Mitochondr... 0.012
#> PSM6 ELGNDAYK ProtA 6 Mitochondr... 0.011
## The row with the NA is removed
rowData(filterFeatures(feat1, ~ location == "Mitochondrion", na.rm = TRUE)[[1]])
#> 'location' found in 1 out of 1 assay(s).
#> DataFrame with 5 rows and 5 columns
#> Sequence Protein Var location pval
#> <character> <character> <integer> <character> <numeric>
#> PSM2 SYGFNAAR ProtA 2 Mitochondr... 0.077
#> PSM3 SYGFNAAR ProtA 3 Mitochondr... 0.063
#> PSM4 ELGNDAYK ProtA 4 Mitochondr... 0.073
#> PSM5 ELGNDAYK ProtA 5 Mitochondr... 0.012
#> PSM6 ELGNDAYK ProtA 6 Mitochondr... 0.011
## Note that in situations with missing values, it is possible to
## use the `%in%` operator or filter missing values out
## explicitly.
rowData(filterFeatures(feat1, ~ location %in% "Mitochondrion")[[1]])
#> 'location' found in 1 out of 1 assay(s).
#> DataFrame with 5 rows and 5 columns
#> Sequence Protein Var location pval
#> <character> <character> <integer> <character> <numeric>
#> PSM2 SYGFNAAR ProtA 2 Mitochondr... 0.077
#> PSM3 SYGFNAAR ProtA 3 Mitochondr... 0.063
#> PSM4 ELGNDAYK ProtA 4 Mitochondr... 0.073
#> PSM5 ELGNDAYK ProtA 5 Mitochondr... 0.012
#> PSM6 ELGNDAYK ProtA 6 Mitochondr... 0.011
rowData(filterFeatures(feat1, ~ location %in% c(NA, "Mitochondrion"))[[1]])
#> 'location' found in 1 out of 1 assay(s).
#> DataFrame with 6 rows and 5 columns
#> Sequence Protein Var location pval
#> <character> <character> <integer> <character> <numeric>
#> PSM1 SYGFNAAR ProtA 1 NA 0.084
#> PSM2 SYGFNAAR ProtA 2 Mitochondr... 0.077
#> PSM3 SYGFNAAR ProtA 3 Mitochondr... 0.063
#> PSM4 ELGNDAYK ProtA 4 Mitochondr... 0.073
#> PSM5 ELGNDAYK ProtA 5 Mitochondr... 0.012
#> PSM6 ELGNDAYK ProtA 6 Mitochondr... 0.011
## Explicit handling
filterFeatures(feat1, ~ !is.na(location) & location == "Mitochondrion")
#> 'location' found in 1 out of 1 assay(s).
#> An instance of class QFeatures (type: bulk) with 1 set:
#>
#> [1] psms: SummarizedExperiment with 5 rows and 2 columns
## Using the pipe operator
feat1 |>
filterFeatures( ~ !is.na(location)) |>
filterFeatures( ~ location == "Mitochondrion")
#> 'location' found in 1 out of 1 assay(s).
#> 'location' found in 1 out of 1 assay(s).
#> An instance of class QFeatures (type: bulk) with 1 set:
#>
#> [1] psms: SummarizedExperiment with 5 rows and 2 columns