Group EICs based on their correlation

groupEicCorrelation groups (extracted ion) chromatograms (EICs) based on their correlation with each other. If this correlation is >= than the provided threshold they are grouped.

If x is a MChromatograms() object with more than one column (sample), pairwise correlations between EICs are first calculated for each column (sample) of x separately and subsequently aggregated across samples using aggregationFun. If x is a MChromatograms with 4 rows (EICs) and 3 columns (samples), pairwise correlations are first calculated between all 4 EICs in each of the 3 columns resulting in 3 correlation matrices (of dimension 4x4). These correlation matrices are combined into a single matrix by combining the 3 correlation values per comparison with aggregationFun. By default the mean of the correlation value between e.g. EIC 1 and EIC 2 in each of the 3 columns is used as the final correlation value. Similar to the one-column case EICs are grouped if their (aggregated) correlation coefficient is larger than threshold.

Two types of groupings are available:

• inclusive = FALSE (the default): the algorithm creates small groups of highly correlated members, all of which have a correlation with each other that are >= threshold. Note that with this algorithm, rows in x could still have a correlation >= threshold with one or more elements of a group they are not part of. See notes below for more information.

• inclusive = TRUE: the algorithm creates large groups containing rows that have a correlation >= threshold with at least one element of that group. For example, if row 1 and 3 have a correlation above the threshold and rows 3 and 5 too (but correlation between 1 and 5 is below the threshold) all 3 are grouped into the same group (i.e. rows 1, 3 and 5).

For more information see groupByCorrelation().

Note that it might be useful to set tolerance = 0 if chromatograms from the same sample are compared. This forces retention times of the compared chromatograms' intensities to be identical.

groupEicCorrelation(
  x,
  aggregationFun = mean,
  threshold = 0.8,
  align = "closest",
  inclusive = FALSE,
  ...
)

Arguments

x	MChromatograms() object of list of Chromatogram() objects.
aggregationFun	function to combine the correlation values between pairs of EICs across samples (columns). See description for details.
threshold	numeric(1) with the threshold for correlation above which EICs are grouped together.
align	character(1) defining the method how chromatograms should be aligned prior correlation. Defaults to align = "closest". See alignRt() for more details.
inclusive	logical(1) defining the grouping approach. With inclusive = FALSE (the default) small groups of highly correlated features are created using the groupSimilarityMatrix() function. With inclusive = TRUE groups are created with features that have at least one correlation with any other member of the group which is higher than threshold.
...	parameters for the correlate() function for MChromatograms() objects, such as tolerance to allow specifying the maximal acceptable difference in retention times between objects. See also alignRt() for more information.

Value

factor same length as nrow(x) (if x is a MChromatograms object) or length(x) (if x is a list) with the group each EIC is assigned to.

See also

Other grouping operations: groupByCorrelation(), groupToSinglePolarityPairs()

Author

Johannes Rainer

Examples

library(MSnbase)
set.seed(123)
chr1 <- Chromatogram(rtime = 1:10 + rnorm(n = 10, sd = 0.3),
    intensity = c(5, 29, 50, NA, 100, 12, 3, 4, 1, 3))
chr2 <- Chromatogram(rtime = 1:10 + rnorm(n = 10, sd = 0.3),
    intensity = c(80, 50, 20, 10, 9, 4, 3, 4, 1, 3))
chr3 <- Chromatogram(rtime = 3:9 + rnorm(7, sd = 0.3),
    intensity = c(53, 80, 130, 15, 5, 3, 2))
chrs <- MChromatograms(list(chr1, chr2, chr3))

groupEicCorrelation(chrs)
#> [1] 1 2 1
#> Levels: 1 2

## With a MChromatograms with two columns, use the maximal correlation
## coefficient found in each of the columns
chrs <- MChromatograms(list(chr1, chr2, chr3, chr1, chr2, chr3), ncol = 2)
groupEicCorrelation(chrs, aggregationFun = max)
#> [1] 1 2 1
#> Levels: 1 2