Get Flagged Ids From MAD Outlier or Missingness Maps

Return the IDs of flagged samples for objects of the outlier_map or missingness_map classes. Samples are flagged based on the percent analytes (RFU columns) for a given sample that were identified as outliers using the median absolute deviation (MAD), or the percent of "missingness" (NAs) of clinical meta data of an ADAT.

Usage

getFlaggedIds(x, flags = 0.05, data = NULL, include = NULL)

Arguments

x

An object of class:

outlier_map - from calcOutlierMap()
missingness_map - from calcMissingnessMap()

flags

Numeric in [0, 1]. For an "outlier_map", the proportion of the analytes (columns) for a given sample that must be outliers for a flag to be placed at the right-axis, thus flagging that sample. For "missingness_map" class, the percent of "missingness" (NAs) of the clinical meta data. If NULL (default), 0.05 (5%) is selected.

data

Optional. The data originally used to create the map x. If omitted, a single column data frame is returned.

include

Optional. Character vector of column name(s) in data to include in the resulting data frame. Ignored if data = NULL.

Value

A data.frame of the indices (idx) of flagged samples, along with any additional variables as specified by include.

Author

Caleb Scheidel

Examples

# flagged outliers
# create a single sample outlier (12)
out_adat <- SomaDataIO::example_data
apts     <- SomaDataIO::getAnalytes(out_adat)
out_adat[12, apts] <- out_adat[12, apts] * 10

om <- calcOutlierMap(out_adat)
getFlaggedIds(om, out_adat, flags = 0.05, include = c("Sex", "Subarray"))
#>   idx  Sex Subarray
#> 1  12    M        5
#> 2  13 <NA>        4
#> 3  43 <NA>        8
#> 4  87 <NA>        6
#> 5 143 <NA>        4
#> 6 151 <NA>        4
#> 7 173 <NA>        4

# flagged missingness
sample.adat <- SomaDataIO::example_data
nc   <- SomaDataIO::getMeta(sample.adat, n = TRUE)
meta <- sample.adat[, SomaDataIO::getMeta(sample.adat)]

# random assign NAs to df b/c currently none missing
cols <- rep(1:nc, each = 3L)
rows <- withr::with_seed(1, as.integer(replicate(nc, sample(1:nrow(meta), 3))))
meta[cbind(rows, cols)] <- NA

mm <- calcMissingnessMap(meta)
getFlaggedIds(mm, meta, flags = 0.2)
#>     idx
#> 1     1
#> 2     2
#> 3     3
#> 4     4
#> 5     5
#> 6     6
#> 7     7
#> 8     8
#> 9     9
#> 10   10
#> 11   11
#> 12   12
#> 13   13
#> 14   14
#> 15   15
#> 16   16
#> 17   17
#> 18   18
#> 19   19
#> 20   20
#> 21   21
#> 22   22
#> 23   23
#> 24   24
#> 25   25
#> 26   26
#> 27   27
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#> 30   30
#> 31   31
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#> 184 184
#> 185 185
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#> 187 187
#> 188 188
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#> 190 190
#> 191 191
#> 192 192