Compute the median coefficient of variation (CV) per cell

Source: R/compute_metrics.R

The function computes for each cell the median CV and stores them accordingly in the colData of the QFeatures object. The CVs in each cell are computed from a group of features. The grouping is defined by a variable in the rowData. The function can be applied to one or more assays, as long as the samples (column names) are not duplicated. Also, the user can supply a minimal number of observations required to compute a CV to avoid that CVs computed on too few observations influence the distribution within a cell. The quantification matrix can be optionally normalized before computing the CVs. Multiple normalizations are possible.

medianCVperCell(
  object,
  i,
  groupBy,
  nobs = 5,
  na.rm = TRUE,
  colDataName = "MedianCV",
  norm = "none",
  ...
)

Arguments

object

A QFeatures object

i

A numeric() or character() vector indicating from which assays the rowData should be taken.

groupBy

A character(1) indicating the variable name in the rowData that contains the feature grouping.

nobs

An integer(1) indicating how many observations (features) should at least be considered for computing the CV. Since no CV can be computed for less than 2 observations, nobs should at least be 2.

na.rm

A logical(1) indicating whether missing data should be removed before computation.

colDataName

A character(1) giving the name of the new variable in the colData where the computed CVs will be stored. The name cannot already exist in the colData.

norm

A character() of normalization methods that will be sequentially applied to each feature (row) in each assay. Available methods and additional information about normalization can be found in MsCoreUtils::normalizeMethods. You can also specify norm = "SCoPE2" to reproduce the normalization performed before computing the CVs as suggested by Specht et al. norm = "none" will not normalize the data (default)

...

Additional arguments that are passed to the normalization method.

Value

A QFeatures object.

Details

A new column is added to the colData of the object. The samples (columns) that are not present in the selection i will get assigned an NA.

References

Specht, Harrison, Edward Emmott, Aleksandra A. Petelski, R. Gray Huffman, David H. Perlman, Marco Serra, Peter Kharchenko, Antonius Koller, and Nikolai Slavov. 2021. “Single-Cell Proteomic and Transcriptomic Analysis of Macrophage Heterogeneity Using SCoPE2.” Genome Biology 22 (1): 50.

Examples

data("scp1")
scp1 <- filterFeatures(scp1, ~ !is.na(Proteins))
#> 'Proteins' found in 4 out of 5 assay(s)
#> No filter applied to the following assay(s) because one or more filtering variables are missing in the rowData: proteins.
#> You can control whether to remove or keep the features using the 'keep' argument (see '?filterFeature').
scp1 <- medianCVperCell(scp1, 
                        i = 1:3,
                        groupBy = "Proteins",
                        nobs = 5,
                        na.rm = TRUE,
                        colDataName = "MedianCV",
                        norm = "div.median")
#> Warning: The median CV could not be computed for one or more samples. You may want to try a smaller value for 'nobs'.
## Check results
hist(scp1$MedianCV)