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.
computeSCR(
object,
i,
colvar,
samplePattern,
sampleFUN = "mean",
carrierPattern,
carrierFUN = sampleFUN,
rowDataName = "SCR"
)A QFeatures object.
A character() or integer() indicating for which
assay(s) the SCR needs to be computed.
A character(1) indicating the variable to take
from colData(object) that gives the sample annotation.
A character(1) pattern that matches the
sample encoding in colvar.
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.
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
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.
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.
A QFeatures object for which the rowData of the given
assay(s) is augmented with the mean SCR.
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