Plate-by-plate normalization of the raw data stored in slot
assayData of a
Normalization is performed separately for each plate, replicate and channel.
Log2 data transformation can be performed and variance adjustment can be performed in different ways (none, per-plate, per-batch or per-experiment).
a character specifying the scale that the input data are considered to be on: "additive" scale (default) or "multiplicative". The interpretation of this terminology is that data on an additive scale will be normalised by subtraction of a correction offset, whereas data on a multiplicative scale are normalised by division through a correction factor.
character specifying the normalization method to use
for the per-plate normalization. Allowed values are
character specifying the
variance adjustment to perform.
Allowed values are
a vector of regular expressions giving the name of the positive control(s). See details.
a vector of regular expressions giving the name of the negative control(s). See details.
Further arguments that get passed on to the function
implementing the normalization method
normalizePlates uses the content of the
assayData slot of
For dual-channel data, a recommended workflow is (i) to correct for
plate effects using the
normalizePlates function, (ii) combine
the two channels using the function
(iii) finally, if necessary, normalize the summarized intensities
In this function, the normalization is performed in a plate-by-plate fashion, following this workflow:
Log transformation of the data (optional)
Variance adjustment of the plate intensity corrected data (optional)
scale defines the scale of the data. If the data are on a multiplicative scale
scale="multiplicative"), the data can be
transformed by setting
log=TRUE. This then changes the scale of the data to code"additive".
In the next step of preprocessing, intensities are corrected in a plate-by-plate basis using the chosen normalization method:
method="median", plates effects are corrected by the
median value across wells that are annotated as
wellAnno(object), for each plate and replicate.
method="mean", the average in the
wells is used instead.
method="shorth", the midpoint of the
of the distribution of values in the wells annotated
sample is used.
method="negatives", the median of the negative
controls is used.
Depending on the scale of the data prior to normalization, the data are
divided by the above defined correction factors (scale:
"multiplicative"), or the value is subtracted (scale:
Further available normalization methods are:
method="POC" (percent of control): for each plate and
replicate, each measurement is divided by the average of the
measurements on the plate positive controls, and multiplied by 100.
method="NPI" (normalized percent inhibition): each
measurement is subtracted from the average of the intensities on the
plate positive controls, and this result is divided by the difference
between the means of the measurements on the positive and the negative
method="Bscore": for each plate and replicate, the
B-score method, which is based on a
2-way median polish, is applied to remove row and column biases.
method="locfit" (robust local fit regression): for each
plate and replicate, spatial effects are removed by fitting a
bivariate local polynomial regression
In the final preprocessing step, variance of plate-corrected intensities can be adjusted as follows:
varianceAdjust="byPlate": per plate normalized intensities are divided by the per-plate median absolute deviations (MAD) in "sample" wells. This is done separately for each replicate and channel;
varianceAdjust="byBatch": using the content of slot
batch, plates are split according to assay batches and the individual normalized intensities in each group of plates (batch) are divided by the per-batch of plates MAD values (calculated based on "sample" wells). This is done separately for each replicate and channel;
varianceAdjust="byExperiment": each normalized measurement is divided by the overall MAD of normalized values in wells containing "sample". This is done separately for each replicate and channel;
By default, no variance adjustment is performed
negControls are required for
applying the normalization methods based on the control measurements
that is, when
should be vectors of regular expression patterns specifying the name of
the positive(s) and negative(s) controls, respectivey, as provided in
the plate configuration file (and accessed via
wellAnno(object)). The length of these vectors should be equal to
the current number of channels in
object (i.e. to the
dim(Data(object))). By default, if
posControls is not
given, pos will be taken as the name for the wells containing
positive controls. Similarly, if
negControls is missing, by
default neg will be considered as the name used to annotate the
negative controls. The content of
negControls will be passed to
for pattern matching within the well annotation given in the featureData
object (which can be accessed via
wellAnno(object)) (see examples for
negControls are particularly
useful in multi-channel data since the controls might be
reporter-specific, or after normalizing multi-channel data.
See the Examples section for an example on how this function can be used to apply a robust version of the Z score method, whereby, for each plate and replicate, the per-plate median (computed only from sample wells) is subtracted from the measurements, and the result is divided by the per-plate MAD (only from sample wells).
An object of class
cellHTS with the normalized data
stored in slot
assayData (its previous contents were overridden).
The processing status of the
object is updated
in the slot
Additional slots of
object may be updated if
method="locfit" are used.
Please refer to the help page of
Bscore function and
Boutros, M., Bras, L.P. and Huber, W. (2006) Analysis of cell-based RNAi screens, Genome Biology 7, R66.
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data(KcViabSmall) # per-plate median scaling of intensities x1 <- normalizePlates(KcViabSmall, scale="multiplicative", log=FALSE, method="median", varianceAdjust="none") # per-plate median subtraction of log2 transformed intensities x2 <- normalizePlates(KcViabSmall, scale="multiplicative", log=TRUE, method="median", varianceAdjust="none") ## Not run: x3 <- normalizePlates(KcViabSmall, scale="multiplicative", log=TRUE, method="Bscore", varianceAdjust="none", save.model=TRUE) ## End(Not run) ## robust Z score method (plate intensities are subtracted by the per-plate median on sample wells and divided by the per-plate MAD on sample wells): xZ <- normalizePlates(KcViabSmall, scale="additive", log=FALSE, method="median", varianceAdjust="byPlate") ## an example to illustrate the use of slot 'batch': ## Not run: try(xnorm <- normalizePlates(KcViabSmall, scale="multiplicative", method="median", varianceAdjust="byBatch")) # It doesn't work because we need to have slot 'batch'! # For example, we will suppose that a different lot of reagents was used for plate 1: pp <- plate(KcViabSmall) fData(KcViabSmall)$"reagent" <- "lot B" fData(KcViabSmall)$"reagent"[pp==1] <- "lot A" fvarMetadata(KcViabSmall)["reagent",] <- "Lot of reagent used" bb <- as.factor(fData(KcViabSmall)$"reagent") batch(KcViabSmall) <- array(as.integer(bb), dim=dim(Data(KcViabSmall))) ## check number of batches: nbatch(KcViabSmall) x1 <- normalizePlates(KcViabSmall, scale="multiplicative", log = FALSE, method="median", varianceAdjust="byBatch") ## End(Not run)
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