QFeatures-filtering: Filter features based on their rowData

In QFeatures: Quantitative features for mass spectrometry data

Description

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 signature 'QFeatures,AnnotationFilter'
filterFeatures(object, filter, na.rm = FALSE, ...)

## S4 method for signature 'QFeatures,formula'
filterFeatures(object, filter, na.rm = FALSE, ...)

Arguments

field	character(1) refering to the name of the variable to apply the filter on.
value	character() or integer() value for the CharacterVariableFilter and NumericVariableFilter filters respectively.
condition	character(1) defining the condition to be used in the filter. For NumericVariableFilter, one of "==", "!=", ">", "<", ">=" or "<=". For CharacterVariableFilter, one of "==", "!=", "startsWith", "endsWith" or "contains". Default condition is "==".
not	logical(1) indicating whether the filtering should be negated or not. TRUE indicates is negated (!). FALSE indicates not negated. Default not is FALSE, so no negation.
object	An instance of class QFeatures.
filter	Either an instance of class AnnotationFilter or a formula.
na.rm	logical(1) indicating whether missing values should be removed. Default is FALSE.
...	Additional parameters. Currently ignored.

Value

An filtered QFeature object.

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.

Author(s)

Laurent Gatto

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 = "==")

VariableFilter(field = "my_num_var",
               value = 0.05,
               condition = "<=")

example(aggregateFeatures)

## ----------------------------------------------------------------
## 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")

## using the forumula intefrace, martial match
filterFeatures(feat1, ~startsWith(location, "Mito"))

## using a user-defined character filter
filterFeatures(feat1, VariableFilter("location", "Mitochondrion"))

## using a user-defined character filter with partial match
filterFeatures(feat1, VariableFilter("location", "Mito", "startsWith"))
filterFeatures(feat1, VariableFilter("location", "itochon", "contains"))

## ----------------------------------------------------------------
## 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 = "!="))

## using the forumula interface
filterFeatures(feat1, ~ location != "unknown")

## ----------------------------------------------------------------
## 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, "<="))

## using the formula interface
filterFeatures(feat1, ~ pval <= 0.03)

## you can also remove all p-values that are NA (if any)
filterFeatures(feat1, ~ !is.na(pval))

## ----------------------------------------------------------------
## Negative control - filtering for an non-existing markers value
## or a missing feature variable, returning empty results
## ----------------------------------------------------------------

filterFeatures(feat1, VariableFilter("location", "not"))

filterFeatures(feat1, ~ location == "not")

filterFeatures(feat1, VariableFilter("foo", "bar"))

filterFeatures(feat1, ~ foo == "bar")

## ----------------------------------------------------------------
## Example with missing values
## ----------------------------------------------------------------

data(feat1)
rowData(feat1[[1]])[1, "location"] <- NA
rowData(feat1[[1]])

## The row with the NA is not removed
rowData(filterFeatures(feat1, ~ location == "Mitochondrion")[[1]])
rowData(filterFeatures(feat1, ~ location == "Mitochondrion", na.rm = FALSE)[[1]])

## The row with the NA is removed
rowData(filterFeatures(feat1, ~ location == "Mitochondrion", na.rm = TRUE)[[1]])

## 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]])
rowData(filterFeatures(feat1, ~ location %in% c(NA, "Mitochondrion"))[[1]])

## Explicit handling
filterFeatures(feat1, ~ !is.na(location) & location == "Mitochondrion")

## Using the pipe operator
library("magrittr")
feat1 %>%
   filterFeatures( ~ !is.na(location)) %>%
   filterFeatures( ~ location == "Mitochondrion")

QFeatures documentation built on Nov. 8, 2020, 6:51 p.m.