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Compute the sample over carrier ratio (SCR)

Source: R/compute_metrics.R
computeSCR.Rd

The function computes the ratio of the intensities of sample channels over the intentisty of the carrier channel for each feature. The ratios are averaged within the assay.

Usage

computeSCR(
  object,
  i,
  colvar,
  samplePattern,
  sampleFUN = "mean",
  carrierPattern,
  carrierFUN = sampleFUN,
  rowDataName = "SCR"
)

Arguments

object

A QFeatures object.

i

A character() or integer() indicating for which assay(s) the SCR needs to be computed.

colvar

A character(1) indicating the variable to take from colData(object) that gives the sample annotation.

samplePattern

A character(1) pattern that matches the sample encoding in colvar.

sampleFUN

A character(1) or function that provides the summarization function to use (eg mean, sum, media, max, ...). Only used when the pattern matches multiple samples. Default is mean. Note for custom function, na.rm = TRUE is passed to sampleFUN to ignore missing values, make sure to provide a function that accepts this argument.

carrierPattern

A character(1) pattern that matches the carrier encoding in colvar. Only one match per assay is allowed, otherwise only the first match is taken

carrierFUN

A character(1) or function that provides the summarization function to use (eg mean, sum, media, max, ...). Only used when the pattern matches multiple carriers. Default is the same function as sampleFUN. Note for custom function, na.rm = TRUE is passed to carrierFUN to ignore missing values, make sure to provide a function that accepts this argument.

rowDataName

A character(1) giving the name of the new variable in the rowData where the computed SCR will be stored. The name cannot already exist in any of the assay rowData.

Value

A QFeatures object for which the rowData of the given assay(s) is augmented with the mean SCR.

Examples

data("scp1")
scp1 <- computeSCR(scp1,
                   i = 1,
                   colvar = "SampleType",
                   carrierPattern = "Carrier",
                   samplePattern = "Blank|Macrophage|Monocyte",
                   sampleFUN = "mean",
                   rowDataName = "MeanSCR")
## Check results
rowData(scp1)[[1]][, "MeanSCR"]
#>   [1] 0.015510472 0.017444270 0.013998780 0.032842883         NaN 0.019183768
#>   [7] 0.035322014 0.011685058 0.018249260 0.027776749 0.017919113 0.011189229
#>  [13] 0.006934865 0.042799880 0.019657274 0.000000000 0.022816835 0.045264655
#>  [19] 0.026838068 0.006634830 0.008038560 0.007882511 0.007558454 0.022777305
#>  [25] 0.021362267 0.942637117 0.000000000 0.047027150 0.110357190 0.011164589
#>  [31] 0.027721278 0.047673647 0.011990475 0.016613643 0.015244373 0.009326254
#>  [37] 0.000000000 0.022829389 0.034530363 0.007661202 0.025641138 0.038001092
#>  [43] 0.018844896 0.008099232 0.097323303 0.007196344 0.067520833 0.007050806
#>  [49] 0.018265529 0.028173254 0.012425863 0.246754068 0.098868861 0.056916766
#>  [55] 0.972946418 0.077164878 0.011155643 0.014888172 0.015396338 0.023861222
#>  [61] 0.016795817 0.013005209 0.014811532 0.019834972 0.023055615 0.033241575
#>  [67] 0.007923177 0.016450749         NaN         NaN 0.154418700 0.166120958
#>  [73] 0.024312229 0.028761939 0.021799714 0.016790123         NaN         NaN
#>  [79] 0.032421331 0.023590284 0.009421714 0.008141877 0.209825122         NaN
#>  [85] 0.056103552 0.016963036 0.007254255 0.015776270 0.026212478 0.008341729
#>  [91] 0.013683271 0.127146548 0.262428332 0.010815081         NaN 0.021939122
#>  [97] 0.010556431 0.011106046         NaN         NaN 1.421039985 0.290833213
#> [103] 0.006530733 0.014220928 0.020974690 0.111953099 0.018738790 0.014850186
#> [109] 0.211281919 0.007306382 0.058905617 0.024971824 0.102530538 0.020856375
#> [115] 0.042998307 0.020087350 0.051781427 0.011624164 0.041935595 0.011965542
#> [121] 0.044674680 0.017633856 0.054716089 0.447498978 0.032647408 2.315250048
#> [127] 1.186082092 0.391160056 0.061278850         NaN 0.021773007 0.020502310
#> [133]         NaN 0.090177289 0.056202829         NaN         NaN         NaN
#> [139] 0.019264467 0.011998890         NaN 0.016139295 0.012450886 0.029591602
#> [145] 0.013236440 0.038606232 0.025146765         NaN 0.010298813 0.076093736
#> [151] 0.025677348 0.021617243 0.597881028         NaN 0.930486819 0.030299867
#> [157] 0.002346206 0.024708650 0.012180817 0.019880893 0.011504703 0.022299321
#> [163] 0.030614294 0.006889869 0.120408305 0.037997229