| normalizePlates {cellHTS2} | R Documentation |
Plate-by-plate 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, per-plate, per-batch or per-experiment).
normalizePlates(object, scale="additive", log=FALSE, method="median", varianceAdjust="none", posControls, negControls,...)
object |
a cellHTS object that has already been configured. See details. |
scale |
a character specifying the scale of the raw data: "additive" scale (default) or "multiplicative" scale. |
log |
logical. If log=TRUE, raw data are log2 transformed.
If data are in additive scale (scale="additive"), log can only be set to log=FALSE.
The default is log=FALSE. |
method |
a character specifying the normalization method to use for performing the per-plate normalization. Allowed values are "median" (default), "mean", "shorth", "POC", "NPI", "negatives",
Bscore, loess and locfit. See details. |
varianceAdjust |
character vector of length one indicating the variance adjustment to perform. Allowed values are "none" (default), "byPlate", "byBatch" and "byExperiment". See details. |
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 method. Currently, this is only used for Bscore and loess and locfit. |
Function normalizePlates uses the content of assayData slot of object.
For dual-channel data, the user should first correct for plate effects using normalizePlates function, then combine the two channels using function summarizeChannels, and finally, if necessary, normalize the summarized intensities calling normalizePlates again.
In this function, the normalization is performed in a plate-by-plate fashion, following this workflow:
The argument scale defines the scale of the data. If data are in multiplicative scale
(scale="multiplicative"), data can be log2 transformed by setting log=TRUE. This changes the scale of the data to "additive".
In the next step of preprocessing, intensities are corrected in a plate-by-plate basis using the chosen normalization method:
method="median" (median scaling), plates effects are corrected by dividing each measurement by the median value across wells annotated as sample in wellAnno(object), for each plate and replicate.
method="mean" (mean scaling), the average in the sample wells is consider instead.
method="shorth" (scaling by the midpoint of the shorth), for each plate and replicate, the midpoint of the shorth of the distribution of values in the wells annotated
as sample is calculated. Then, every measurement is divided by this value.
method="negatives" (scaling by the negative controls), for each plate and replicate, each measurement is divided by the median of the measurements on the plate negative controls.
NOTE: Depending on the scale of the data prior to normalization, the above per-plate correction factors are subtracted from each plate measurement, instead.
Other 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" (B score): for each plate and replicate, the B score method (based on a 2-way median polish) is applied to remove plate effects and row and column biases.
method="locfit" (robust local fit regression): for each plate and replicate, spatial effects are removed by fitting a bivariate local regression (see spatial normalization function).
method="loess" (loess regression): for each plate and replicate, spatial effects are removed by fitting a loess curve (see spatial normalization function).
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 (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
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 the measurements of each plate and replicate are substracted by the per-plate median (at sample wells) and then divided by the per-plate MAD (at 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 object@state[["normalized"]]=TRUE.
Additional slots of object may be updated if
method="Bscore", or method="loess" or method="locfit". Please refer to the help page of
the Bscore function and spatialNormalization function.
Ligia Bras ligia@ebi.ac.uk, Wolfgang Huber huber@ebi.ac.uk
Boutros, M., Bras, L.P. and Huber, W. (2006) Analysis of cell-based RNAi screens, Genome Biology 7, R66.
Bscore,
spatialNormalization,
summarizeChannels
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)