normalizePlates {cellHTS} | R Documentation |
Per-plate normalization of the data xraw
of a cellHTS object.
Optionally, a data transformation such as log
, and a
transformation to z-scores can be performed.
normalizePlates(x, normalizationMethod="median", transform, zscore, posControls, negControls, BscoreArgs)
x |
a cellHTS object that has already been configured. See details. |
normalizationMethod |
a character specifying the normalization method
to use for performing the per-plate normalization. Allowed values are "median" (default), "mean" ,
"shorth" , "POC" , "NPI" , "negatives" and "Bscore" . See details. |
transform |
a function that takes a numeric vector and returns a numeric vector of the same length; for example, the logarithm function log . |
zscore |
indicates if the data should be centered and scaled after normalization and transformation. If missing (default), the data will not be centered and scaled. Otherwise, the value of this argument should be a character string, either "+" or "-", which will be used to set the sign for the calculated z-scores. 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. |
BscoreArgs |
a list with parameters for the function that performs B score normalization. See details. |
The normalization is performed in a per-plate fashion.
normalizationMethod="median"
(median scaling), plates effects are corrected by dividing each measurement by the median value across wells annotated as sample
in x$wellAnno
, for each plate and replicate.
normalizationMethod="mean"
(mean scaling), the average in the sample
wells is consider instead.
normalizationMethod="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.
normalizationMethod="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.
normalizationMethod="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.
normalizationMethod="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.
normalizationMethod="Bscore"
(B score), for each plate and replicate, the B score method is applied to remove plate effects and row and column biases.
If transform
is not missing, the chosen data transformation is applied.
Most commonly, this option can be used to apply a log transformation.
If zscore
is not missing, a robust z-score for each individual
measurement will be determined for each plate and each well by
subtracting the overall median
and dividing by the overall
mad
. These 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.
The arguments posControls
and negControls
are required for applying the normalization methods based on the control measurements (that is, when normalizationMethod="POC"
, or normalizationMethod="NPI"
, or normalizationMethod="negatives"
).
posControls
and negControls
should be given as a vector of regular expression patterns specifying the name of the positive(s) and negative(s) controls, respectivey, as provided in the plate configuration file (and stored in x$wellAnno
). The length of these vectors should be equal to the number of reporters used in the screen (dim(x$xraw)[4]
) or to dim(x$xnorm)[4]
, in case x
contains multi-channel data that has been normalized by combining the values from two or more channels.
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 annotated the negative controls.
The content of posControls
and negControls
will be passed to regexpr
for pattern matching within the well annotation given in x$wellAnno
(see examples for normalizeChannels
).
The following elements are recognized for BscoreArgs
and
passed on to Bscore
:
adjustPlateMedian
, a logical value indicating whether the estimated average of each plate should also be substracted to the raw intensity values,
model.log
, a logical value. If TRUE
, the per-plate models will be applied after log2
transformation. However, the outputs of Bscore
function will be given in the original scale,
scale
, a logical value indicating if the per-plate model residuals should be further scaled by their variance,
save.model
, a logical value specifying whether the per-plate models should be saved, and given as output.
An object of class cellHTS
, which is a copy of the argument
x
, plus an additional slot xnorm
containing the normalized
data. This is an array of the same dimensions as xraw
.
Moreover, the processing status of the cellHTS
object is updated
in the slot state
to x$state["normalized"]=TRUE
.
Furthermore, if normalizationMethod="Bscore"
, and save.model=TRUE
was given in BscoreArgs
, the slots residuals
, rowcol.effects
, and overall.effects
(only if adjustPlateMedian
was also set to TRUE
in BscoreArgs
) are added to x
. The latter slots are arrays with the same dimension as x$xraw
, except the
overall.effects
, which have dimensions 1 x nr Plates x nr Replicates x nr Channels
.
Ligia Braz ligia@ebi.ac.uk, Wolfgang Huber huber@ebi.ac.uk
data(KcViabSmall) x1 = normalizePlates(KcViabSmall, normalizationMethod="median", zscore="-") ## Not run: x2 = normalizePlates(KcViabSmall, normalizationMethod="Bscore", zscore="-") ## End(Not run)