These functions take an instance of class
"MSnSet" and sets randomly selected values to
NA.
makeNaData(object, nNA, pNA, exclude)
makeNaData2(object, nRows, nNAs, exclude)
whichNA(x)An instance of class MSnSet.
The absolute number of missing values to be assigned.
The proportion of missing values to be assignmed.
A vector to be used to subset object,
defining rows that should not be used to set NAs.
The number of rows for each set.
The number of missing values for each set.
A matrix or an instance of class MSnSet.
An instance of class MSnSet, as object, but
with the appropriate number/proportion of missing values. The
returned object has an additional feature meta-data columns,
nNA
makeNaData randomly selects a number nNA (or a
proportion pNA) of cells in the expression matrix to be set
to NA.
makeNaData2 will select length(nRows) sets of rows
from object, each with nRows[i] rows respectively.
The first set will be assigned nNAs[1] missing values, the
second nNAs[2], ... As opposed to makeNaData, this
permits to control the number of NAs per rows.
The whichNA can be used to extract the indices
of the missing values, as illustrated in the example.
## Example 1
library(pRolocdata)
data(dunkley2006)
sum(is.na(dunkley2006))
#> [1] 0
dunkleyNA <- makeNaData(dunkley2006, nNA = 150)
processingData(dunkleyNA)
#> - - - Processing information - - -
#> Loaded on Thu Jul 16 22:53:08 2015.
#> Normalised to sum of intensities.
#> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015
#> Set 150 values to NA Thu Mar 14 06:11:11 2024
#> MSnbase version: 1.17.12
sum(is.na(dunkleyNA))
#> [1] 150
table(fData(dunkleyNA)$nNA)
#>
#> 0 1 2 3
#> 558 113 17 1
naIdx <- whichNA(dunkleyNA)
head(naIdx)
#> [,1] [,2]
#> [1,] 4 1
#> [2,] 57 1
#> [3,] 110 1
#> [4,] 119 1
#> [5,] 147 1
#> [6,] 273 1
## Example 2
dunkleyNA <- makeNaData(dunkley2006, nNA = 150, exclude = 1:10)
processingData(dunkleyNA)
#> - - - Processing information - - -
#> Set 150 values to NA Thu Mar 14 06:11:11 2024
#> (excluding 10 features)
#> MSnbase version: 1.17.12
table(fData(dunkleyNA)$nNA[1:10])
#>
#> 0
#> 10
table(fData(dunkleyNA)$nNA)
#>
#> 0 1 2
#> 549 130 10
## Example 3
nr <- rep(10, 5)
na <- 1:5
x <- makeNaData2(dunkley2006[1:100, 1:5],
nRows = nr,
nNAs = na)
processingData(x)
#> - - - Processing information - - -
#> Loaded on Thu Jul 16 22:53:08 2015.
#> Normalised to sum of intensities.
#> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015
#> Subset [689,16][100,5] Thu Mar 14 06:11:12 2024
#> Set (1,2,3,4,5) NAs in (10,10,10,10,10) rows,
#> respectively Thu Mar 14 06:11:12 2024
#> MSnbase version: 1.17.12
(res <- table(fData(x)$nNA))
#>
#> 0 1 2 3 4 5
#> 50 10 10 10 10 10
stopifnot(as.numeric(names(res)[-1]) == na)
stopifnot(res[-1] == nr)
## Example 3
nr2 <- c(5, 12, 11, 8)
na2 <- c(3, 8, 1, 4)
x2 <- makeNaData2(dunkley2006[1:100, 1:10],
nRows = nr2,
nNAs = na2)
processingData(x2)
#> - - - Processing information - - -
#> Loaded on Thu Jul 16 22:53:08 2015.
#> Normalised to sum of intensities.
#> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015
#> Subset [689,16][100,10] Thu Mar 14 06:11:12 2024
#> Set (3,8,1,4) NAs in (5,12,11,8) rows,
#> respectively Thu Mar 14 06:11:12 2024
#> MSnbase version: 1.17.12
(res2 <- table(fData(x2)$nNA))
#>
#> 0 1 3 4 8
#> 64 11 5 8 12
stopifnot(as.numeric(names(res2)[-1]) == sort(na2))
stopifnot(res2[-1] == nr2[order(na2)])
## Example 5
nr3 <- c(5, 12, 11, 8)
na3 <- c(3, 8, 1, 3)
x3 <- makeNaData2(dunkley2006[1:100, 1:10],
nRows = nr3,
nNAs = na3)
processingData(x3)
#> - - - Processing information - - -
#> Loaded on Thu Jul 16 22:53:08 2015.
#> Normalised to sum of intensities.
#> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015
#> Subset [689,16][100,10] Thu Mar 14 06:11:12 2024
#> Set (3,8,1,3) NAs in (5,12,11,8) rows,
#> respectively Thu Mar 14 06:11:12 2024
#> MSnbase version: 1.17.12
(res3 <- table(fData(x3)$nNA))
#>
#> 0 1 3 8
#> 64 11 13 12