This manual page describes the handling of missing values in
QFeatures objects. In the following functions, if object is of
class QFeatures, an optional assay index or name i can be
specified to define the assay (by name of index) on which to
operate.
The following functions are currently available:
zeroIsNA(object, i) replaces all 0 in object by NA. This
is often necessary when third-party software assume that
features that weren't quantified should be assigned an
intensity of 0.
infIsNA(object, i) replaces all infinite values in object by
NA. This is necessary when third-party software divide
expression data by zero values, for instance during custom
normalization.
nNA(object, i) returns a list of missing value summaries. The
first element nNA gives a DataFrame with the number and the
proportion of missing values for the whole assay; the second
element nNArows provides a DataFrame with the number and the
proportion of missing values for the features (rows) of the
assay(s); the third element nNAcols provides the number and
the proportions of missing values in each sample of the
assay(s). When object has class QFeatures and additional
column with the assays is provided in each element's
DataFrame.
filterNA(object, pNA, i) removes features (rows) that contain
a proportion of more missing values of pNA or higher.
See the Processing vignette for examples.
# S4 method for class 'SummarizedExperiment,missing'
zeroIsNA(object, i)
# S4 method for class 'QFeatures,integer'
zeroIsNA(object, i)
# S4 method for class 'QFeatures,numeric'
zeroIsNA(object, i)
# S4 method for class 'QFeatures,character'
zeroIsNA(object, i)
# S4 method for class 'SummarizedExperiment,missing'
infIsNA(object, i)
# S4 method for class 'QFeatures,integer'
infIsNA(object, i)
# S4 method for class 'QFeatures,numeric'
infIsNA(object, i)
# S4 method for class 'QFeatures,character'
infIsNA(object, i)
# S4 method for class 'SummarizedExperiment,missing'
nNA(object, i)
# S4 method for class 'QFeatures,integer'
nNA(object, i)
# S4 method for class 'QFeatures,numeric'
nNA(object, i)
# S4 method for class 'QFeatures,character'
nNA(object, i)
# S4 method for class 'SummarizedExperiment'
filterNA(object, pNA = 0)
# S4 method for class 'QFeatures'
filterNA(object, pNA = 0, i)An object of class QFeatures or SummarizedExperiment.
One or more indices or names of the assay(s) to be processed.
numeric(1) providing the maximum proportion of
missing values per feature (row) that is acceptable. Feature
with higher proportions are removed. If 0 (default), features
that contain any number of NA values are dropped.
An instance of the same class as object.
The impute() for QFeautres instances.
data(ft_na)
## Summary if missing values
nNA(ft_na, 1)
#> $nNA
#> DataFrame with 1 row and 3 columns
#> assay nNA pNA
#> <character> <integer> <numeric>
#> 1 na 3 0.25
#>
#> $nNArows
#> DataFrame with 4 rows and 4 columns
#> assay name nNA pNA
#> <character> <character> <integer> <numeric>
#> 1 na a 1 0.333333
#> 2 na b 0 0.000000
#> 3 na c 1 0.333333
#> 4 na d 1 0.333333
#>
#> $nNAcols
#> DataFrame with 3 rows and 4 columns
#> assay name nNA pNA
#> <character> <character> <integer> <numeric>
#> 1 na A 2 0.50
#> 2 na B 1 0.25
#> 3 na C 0 0.00
#>
## Remove rows with missing values
assay(filterNA(ft_na, i = 1))
#> A B C
#> b 2 6 10
## Replace NAs by zero and back
ft_na <- impute(ft_na, i = 1, method = "zero")
assay(ft_na)
#> A B C
#> a NA 5 9
#> b 2 6 10
#> c 3 NA 11
#> d NA 8 12
ft_na <- zeroIsNA(ft_na, 1)
assay(ft_na)
#> A B C
#> a NA 5 9
#> b 2 6 10
#> c 3 NA 11
#> d NA 8 12