cellHTS
object, and calculates a single z-score value for each probe.Currently this function is implemented only for single-color data.
summarizeReplicates(x, zscore, summary = "min")
cellHTS
, which is a copy of the argument
x
plus the slot score
,
a numeric vector containing the z-factor for each well in
every plate. The length of this vector is therefore equal to the product
between the plateSize and the number of plates.
Moreover, the processing status of the cellHTS
object is updated
in the slot state
to state["scored"]= TRUE
.xnorm
of x
, a single z-score is calculated for each probe. The argument zscore
indicates the state of the normalized replicate measurements: if zscore
is missing, it
is assumed that the replicates have been scored, by calling normalizePlates
with the argument zscore
equal to "-" or "+"; Otherwise, zscore
should be given, so that a robust z-score
is calculated for each plate and each well by subtracting the overall median and dividing by the overall mad. The overall median and
mad are taken by considering the distribution of intensities (over all plates) in the wells whose content is annotated as sample
.
The allowed values for zscore
("+" or "-") are used to set the sign of
the calculated z-scores. For example, with a zscore="-"
a strong decrease in the signal will be represented
by a positive z-score, whereas setting zscore="+"
,
such a phenotype will be represented by a negative z-score.
This option can be set to calculate the results to the commonly used convention.
Finally, a single z-score per probe is calculated by summarizing between scored replicates. If summary="mean"
, the average of replicate values is considered; if
summary="max"
, then the maximum of replicate
intensities is taken; if summary="min"
, the minimum is considered, instead (conservative); if summary="rms"
, the square root of the mean squared value of the replicates (root mean square) is taken as a summary function;
if summary="closestToZero"
, the value closest to zero is taken as a summary (ueful when both sides of the distribution of z-score values are of interest);
if summary="furthestFromZero"
, the value furthest from zero is taken as a summary (ueful when both sides of the distribution of z-score values are of interest)
normalizePlates
,
summarizeChannels
data(KcViabSmall)
x <- normalizePlates(KcViabSmall, normalizationMethod="median")
x <- summarizeReplicates(x, zscore="-", summary="min")
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