Basic quality assessment functions for metabolomics — quality

The following functions allow to calculate basic quality assessment estimates typically employed in the analysis of metabolomics data. These functions are designed to be applied to entire rows of data, where each row corresponds to a feature. Subsequently, these estimates can serve as a foundation for feature filtering.

rsd and rowRsd are convenience functions to calculate the relative standard deviation (i.e. coefficient of variation) of a numerical vector or for rows of a numerical matrix, respectively.
rowDratio computes the D-ratio or dispersion ratio, defined as the standard deviation for QC (Quality Control) samples divided by the standard deviation for biological test samples, for each feature (row) in the matrix.
percentMissing and rowPercentMissing determine the percentage of missing values in a vector or for each row of a matrix, respectively.
rowBlank identifies rows (i.e., features) where the mean of test samples is lower than a specified multiple (defined by the threshold parameter) of the mean of blank samples. This can be used to flag features that result from contamination in the solvent of the samples.

These functions are based on standard filtering methods described in the literature, and they are implemented to assist in preprocessing metabolomics data.

Usage

rsd(x, na.rm = TRUE, mad = FALSE)

rowRsd(x, na.rm = TRUE, mad = FALSE)

rowDratio(x, y, na.rm = TRUE, mad = FALSE)

percentMissing(x)

rowPercentMissing(x)

rowBlank(x, y, threshold = 2, na.rm = TRUE)

Arguments

x: numeric For rsd, a numeric vector; for rowRsd, rowDratio, percentMissing and rowBlank, a numeric matrix representing the biological samples.
na.rm: logical(1) indicates whether missing values (NA) should be removed prior to the calculations.
mad: logical(1) indicates whether the Median Absolute Deviation (MAD) should be used instead of the standard deviation. This is suggested for non-gaussian distributed data.
y: numeric For rowDratio and rowBlank, a numeric matrix representing feature abundances in QC samples or blank samples, respectively.
threshold: numeric For rowBlank, indicates the minimum difference required between the mean of a feature in samples compared to the mean of the same feature in blanks for it to not be considered a possible contaminant. For example, the default threshold of 2 signifies that the mean of the features in samples has to be at least twice the mean in blanks for it not to be flagged as a possible contaminant.

Value

See individual function description above for details.

Note

For rsd and rowRsd the feature abundances are expected to be provided in natural scale and not e.g. log2 scale as it may lead to incorrect interpretations.

References

Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR, Dunn WB. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics. 2018;14(6):72. doi: 10.1007/s11306-018-1367-3. Epub 2018 May 18. PMID: 29805336; PMCID: PMC5960010.

Author

Philippine Louail, Johannes Rainer

Examples


## coefficient of variation
a <- c(4.3, 4.5, 3.6, 5.3)
rsd(a)
#> [1] 0.1580575

A <- rbind(a, a, a)
rowRsd(A)
#>         a         a         a 
#> 0.1580575 0.1580575 0.1580575 

## Dratio
x <- c(4.3, 4.5, 3.6, 5.3)
X <- rbind(a, a, a)
rowDratio(X, X)

#' ## Percent Missing
b <- c(1, NA, 3, 4, NA)
percentMissing(b)
#> [1] 40

B <- matrix(c(1, 2, 3, NA, 5, 6, 7, 8, 9), nrow = 3)
rowPercentMissing(B)
#> [1] 33.33333  0.00000  0.00000

## Blank Rows
test_samples <- matrix(c(13, 21, 3, 4, 5, 6), nrow = 2)
blank_samples <- matrix(c(0, 1, 2, 3, 4, 5), nrow = 2)
rowBlank(test_samples, blank_samples)
#> [1] FALSE FALSE