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Remove outlier points per row by MAD factor threshold

Source: R/jamba-rowgroupmeans-madoutliers.R
rowRmMadOutliers.Rd

Remove outlier points per row by MAD factor threshold

Usage

rowRmMadOutliers(
  x,
  madFactor = 5,
  na.rm = TRUE,
  minDiff = 0,
  minReps = 3,
  includeAttributes = FALSE,
  rowMadValues = NULL,
  verbose = FALSE,
  ...
)

Arguments

x

numeric matrix

madFactor

numeric value to multiply by each row MAD to define the threshold for outlier detection.

na.rm

logical indicating whether to ignore NA values when calculating the MAD value. It should probably always be TRUE, however setting to FALSE will prevent any calculations in rows that contain NA values, which could be useful.

minDiff

numeric value indicating the minimum difference from median to qualify as an outlier. This value protects against removing outliers which are already extremely similar. Consider this example:

  • Three numeric values: c(10.0001, 10.0002, 10.001).

  • The third value differs from median by only 0.0008.

  • The third value 10.001 is 5x MAD factor away from median.

  • minDiff = 0.01 would require the minimum difference from median to be at least 0.01 to be eligible to be an outlier point.

minReps

numeric minimum number of non-NA values per row for outliers to be filtered on the row. This argument is typically only relevant for rows with n=2 non-NA values, and when rowMadValues is supplied and may define a threshold less than half the difference in the two points on the given row. Otherwise, n=2 defines each point at exactly 1x MAD from median, and would therefore never be considered an outlier.

includeAttributes

logical indicating whether to return attributes that describe the threshold and type of threshold used per row, in addition to the madFactor and minDiff values defined.

rowMadValues

numeric optional set of row MAD values to use, which is mostly helpful when combining MAD values across multiple samples groups on each row of data, where the combined MAD values may be more reliable than individual group MAD values.

verbose

logical indicating whether to print verbose output.

...

additional parameters are ignored.

Value

numeric matrix with the same dimensions as the input x matrix. Outliers are replaced with NA.

If includeAttributes=TRUE then attributes will be included:

  • outlierDF which is a data.frame with colnames

    • rowMedians: numeric median on each row

    • rowMadValues: numeric MAD for each row

    • rowThresholds: numeric threshold after applying madFactor and minDiff

    • rowReps: integer number of non-NA values in the input data

    • rowTypes: factor indicating the type of threshold: "madFactor" means the row applied the normal MAD * madFactor threshold; "minDiff" means the row applied the minDiff threshold which was the larger threshold.

  • minDiff with the numeric value supplied

  • madFactor with the numeric MAD factor threshold supplied

  • outliersRemoved with the integer total number of new NA values produced by the outlier removal process.

Details

This function applies outlier detection and removal per row of the input numeric matrix.

  • It first calculates MAD per row.

  • The MAD threshold cutoff is a multiple of the MAD value, defined by madFactor, multiplying the per-row MAD by the madFactor.

  • The absolute difference from median is calculated for each point.

  • Outlier points are defined:

    1. Points with MAD above the MAD threshold, and

    2. Points with difference from median at or above minDiff

The minDiff parameter affects cases such as 3 replicates, where all replicates are well within a known threshold indicating low variance, but where two replicates might be nearly identical. Consider:

  • Three numeric values: c(10.0001, 10.0002, 10.001).

  • The third value differs from median by only 0.0008.

  • The third value 10.001 is 5x MAD factor away from median.

  • minDiff = 0.01 would require the minimum difference from median to be at least 0.01 to be eligible to be an outlier point.

One option to define minDiff from the data is to use: minDiff <- stats::median(rowMads(x))

In this case, the threshold is defined by the median difference from median across all rows. This type of threshold will only be reasonable if the variance across all rows is expected to be fairly similar.

This function is substantially faster when the matrixStats package is installed, but will use the apply(x, 1, mad) format as a last option.

Assumptions

  1. This function assumes the input data is appropriate for the use of MAD as a summary statistic.

  2. Specifically, numeric values per row are expected to be roughly normally distributed.

  3. Outlier points are assumed to be present in less than half overall non-NA data.

  4. Outlier points are assumed to be technical outliers, and therefore not the direct result of the experimental measurements being studied. Technical outliers are often caused by some instrument measurement, methodological failure, or other upstream protocol failure.

The default threshold of 5x MAD factor is a fairly lenient criteria, above which the data may even be assumed not to conform to most downstream statistical techniques.

For measurements considered to be more robust, or required to be more robust, the threshold 2x MAD is applied. This criteria is usually a reasonable expectation of housekeeper gene expression across replicates within each sample group.

See also

Other jam numeric functions: deg2rad(), noiseFloor(), normScale(), rad2deg(), rowGroupMeans(), warpAroundZero()

Examples

set.seed(123);
x <- matrix(ncol=5, stats::rnorm(25))*5 + 10;
## Define some outlier points
x[1:2,3] <- x[1:2,3]*5 + 50;
x[2:3,2] <- x[2:3,2]*5 - 100;
rownames(x) <- head(letters, nrow(x));

rowRmMadOutliers(x, madFactor=5);
#>        [,1]      [,2]      [,3]       [,4]     [,5]
#> a  7.197622 18.575325        NA 18.9345657 4.660881
#> b  8.849113        NA        NA 12.4892524 8.910125
#> c 17.793542        NA 12.003857  0.1669142 4.869978
#> d 10.352542  6.565736 10.553414 13.5067795 6.355544
#> e 10.646439  7.771690  7.220794  7.6360430 6.874804

x2 <- rowRmMadOutliers(x, madFactor=2,
   includeAttributes=TRUE);
x2
#>        [,1]      [,2]      [,3]       [,4]     [,5]
#> a  7.197622 18.575325        NA 18.9345657 4.660881
#> b  8.849113        NA        NA 12.4892524 8.910125
#> c 17.793542        NA 12.003857  0.1669142 4.869978
#> d 10.352542  6.565736 10.553414 13.5067795 6.355544
#> e        NA  7.771690  7.220794  7.6360430 6.874804
#> attr(,"outlierDF")
#>   rowMedians rowMadValues rowThresholds rowReps  rowTypes
#> a  18.575325   16.8685827     33.737165       5 madFactor
#> b   8.910125    5.3064136     10.612827       5 madFactor
#> c   4.869978   10.5766897     21.153379       5 madFactor
#> d  10.352542    4.6764726      9.352945       5 madFactor
#> e   7.636043    0.6156476      1.231295       5 madFactor
#> attr(,"minDiff")
#> [1] 0
#> attr(,"madFactor")
#> [1] 2
#> attr(,"outliersRemoved")
#> [1] 5

x3 <- rowRmMadOutliers(x2,
   madFactor=2,
   rowMadValues=attr(x2, "outlierDF")$rowMadValues,
   includeAttributes=TRUE);
x3
#>        [,1]      [,2]      [,3]       [,4]     [,5]
#> a  7.197622 18.575325        NA 18.9345657 4.660881
#> b  8.849113        NA        NA 12.4892524 8.910125
#> c 17.793542        NA 12.003857  0.1669142 4.869978
#> d 10.352542  6.565736 10.553414 13.5067795 6.355544
#> e        NA  7.771690  7.220794  7.6360430 6.874804
#> attr(,"outlierDF")
#>   rowMedians rowMadValues rowThresholds rowReps  rowTypes
#> a  12.886473   16.8685827     33.737165       4 madFactor
#> b   8.910125    5.3064136     10.612827       3 madFactor
#> c   8.436918   10.5766897     21.153379       4 madFactor
#> d  10.352542    4.6764726      9.352945       5 madFactor
#> e   7.428419    0.6156476      1.231295       4 madFactor
#> attr(,"minDiff")
#> [1] 0
#> attr(,"madFactor")
#> [1] 2
#> attr(,"outliersRemoved")
#> [1] 0