Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/normalizePlates.R
Platebyplate normalization of the raw data stored in slot assayData
of a cellHTS
object.
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, perplate, perbatch or perexperiment).
1  normalizePlates(object, scale="additive", log=FALSE, method="median", varianceAdjust="none", posControls, negControls,...)

object 
a 
scale 
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. 
log 
logical. If 
method 
character specifying the normalization method to use
for the perplate normalization. Allowed values are 
varianceAdjust 
character specifying the
variance adjustment to perform.
Allowed values are 
posControls 
a vector of regular expressions giving the name of the positive control(s). See details. 
negControls 
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
chosen by 
The function normalizePlates
uses the content of the assayData
slot of object
.
For dualchannel data, a recommended workflow is (i) to correct for
plate effects using the normalizePlates
function, (ii) combine
the two channels using the function summarizeChannels
, and
(iii) finally, if necessary, normalize the summarized intensities
calling normalizePlates
again.
In this function, the normalization is performed in a platebyplate fashion, following this workflow:
Log transformation of the data (optional)
Perplate normalization
Variance adjustment of the plate intensity corrected data (optional)
The argument scale
defines the scale of the data. If the data are on a multiplicative scale
(scale="multiplicative"
), the data can be log2
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 platebyplate basis using the chosen normalization method:
If method="median"
, plates effects are corrected by the
median value across wells that are annotated as sample
in
wellAnno(object)
, for each plate and replicate.
If method="mean"
, the average in the sample
wells is used instead.
If method="shorth"
, the midpoint of the
shorth
of the distribution of values in the wells annotated
as sample
is used.
If 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:
"additive"
).
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
controls.
method="Bscore"
: for each plate and replicate, the
Bscore method
, which is based on a
2way 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
(see spatialNormalization
).
In the final preprocessing step, variance of platecorrected intensities can be adjusted as follows:
varianceAdjust="byPlate"
: per plate normalized intensities are divided by the perplate 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 perbatch 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
(varianceAdjust="none"
).
The arguments posControls
and negControls
are required for
applying the normalization methods based on the control measurements
that is, when method="POC"
, or method="NPI"
, or
method="negatives"
). posControls
and negControls
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))[3]
). 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 posControls
and
negControls
will be passed to regexpr
for pattern matching within the well annotation given in the featureData
slot of object
(which can be accessed via
wellAnno(object)
) (see examples for
summarizeChannels
). The
arguments posControls
and negControls
are particularly
useful in multichannel data since the controls might be
reporterspecific, or after normalizing multichannel 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 perplate median (computed only from sample wells) is subtracted from the measurements, and the result is divided by the perplate 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 state
to [email protected][["normalized"]]=TRUE
.
Additional slots of object
may be updated if
method="Bscore"
or method="locfit"
are used.
Please refer to the help page of
the Bscore
function and
spatialNormalization
functions.
Ligia Bras [email protected], Wolfgang Huber [email protected]
Boutros, M., Bras, L.P. and Huber, W. (2006) Analysis of cellbased RNAi screens, Genome Biology 7, R66.
Bscore
,
spatialNormalization
,
summarizeChannels
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  data(KcViabSmall)
# perplate median scaling of intensities
x1 < normalizePlates(KcViabSmall, scale="multiplicative", log=FALSE, method="median", varianceAdjust="none")
# perplate 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 perplate median on sample wells and divided by the perplate 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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