olin(object,X=NA,Y=NA,alpha=seq(0.1,1,0.1),iter=3, scale=c(0.05,0.1,0.5,1,2,10,20),OSLIN=FALSE,weights=NA, genepix=FALSE,bg.corr="subtract",...)object. Each column
includes the x-coodinates for the spots of one array. If X=NA, columns on array are used
as proxies for the location in x-directionlocfit).
Rows correspond to the spotted probe sequences, columns to
arrays in the batch.
If the weight of the corresponding spot equals zero, the
spot is not used in the normalisation procedures (except
the genepix argument is set to TRUE.)
If the weight matrix include negative values, these will be
set to zero.
These weight matrix may be derived from the matrix of spot quality weights as defined
for “maRaw” objects (weights=maW(object).
Weights can be also used if the normalisation should
be based on a set of selected genes that are
assumed to be not differentially expressed.genepix is set to
TRUE, spot weights equal zero or larger are set
to one for the local regression whereas negative
spot with negative weights are not used for the
regression. The argument genepix should
be set to TRUE, if
weights=maW(object) is set and spot
quality weights derived by GenePix are stored in
maW(object).locfit function.alpha
that controls the neighbourhood size h of local fitting. The parameter alpha
specifies the fraction of points that are included in the neighbourhood and thus has a value between 0 and 1.
Larger alpha values lead to smoother fits.
Additionally, the setting of scale parameters controls for distinct amount of smoothing in
Y-direction compared to smoothing in X-direction.
The parameter scale can be of arbitrary value.
The choice of model parameters alpha and scale for local regression is crucial for the efficiency and
quality of normalization. To optimize the model parameters, a general cross-validation procedure (GCV) is applied.
The arguments alpha and scale define the parameters values
which are tested in the GCV. OSLIN comprises the OLIN procedure with a
subsequent optimized scaling of the range of logged intensity ratios
across the spatial dimensions of the array. Details concerning the
background correction methods can be found in the help page for backgroundCorrect2.
Detailed information about OLIN and OSLIN can be found in the package documentation and in the reference stated below.
The weights argument specifies the influence of the single spots on the local regression. To exclude
spots being used for the local regression (such as control spots), set their corresponding weight to zero.
Note that OLIN and OSLIN
are based on the assumptions that most genes are not differentially expressed (or up- and down-regulation
is balanced) and that genes are randomly spotted across the array. If these assumptions are not valid, local
regression can lead to an underestimation of differential expression. OSLIN is especially sensitive to violations of these assumptions. However, this
sensitivity can be decreased if the minimal alpha-value is increased. Minimal alpha defines the
smallest scale used for local regression. Increasing alpha can reduce the influence of localised
artifacts as a larger fraction of data points is included. Alternative normalisation functions such
as oin, lin and ino might also be used for a more conservative fit.
If the normalisation should be based on set of genes assumed to be not differentially expressed (house-keeping genes), weights can be used for local regression. In this case, all weights are set to zero except for the house-keeping genes for which weights are set to one. In order to achieve a reliable regression, it is important, however, that there is a sufficient number of house-keeping genes that are distributed over the whole expression range and spotted accross the whole array.
It is also important to note that OLIN/OSLIN is fairly efficient in removing intensity- and spatial-dependent dye bias, so that normalised data will look quite “good” after normalisation independently of the true underlying data quality. Normalisation by local regression assumes smoothness of bias. Therefore, localised artifacts such as scratches, edge effects or bubbles should be avoided. Spots of these areas should be flagged (before normalisation is applied) to ensure data integrity. To stringently detect artifacts, the OLIN functions fdr.int, fdr.spatial, p.int and p.spatial can be used.
maNorm, locfit, gcv, oin,
lin
# LOADING DATA
data(sw)
data(sw.xy)
# OPTIMISED LOCAL INTENSITY-DEPENDENT NORMALISATION OF FIRST ARRAY
norm.olin <- olin(sw[,1],X=sw.xy$X[,1],Y=sw.xy$Y[,1])
# MA-PLOT OF NORMALISATION RESULTS OF FIRST ARRAY
plot(maA(norm.olin),maM(norm.olin),main="OLIN")
# CORRESPONDING MXY-PLOT
mxy.plot(maM(norm.olin)[,1],Ngc=maNgc(norm.olin),Ngr=maNgr(norm.olin),
Nsc=maNsc(norm.olin),Nsr=maNsr(norm.olin),main="OLIN")
# OPTIMISED SCALED LOCAL INTENSITY-DEPENDENT NORMALISATION
norm.oslin <- olin(sw[,1],X=sw.xy$X[,1],Y=sw.xy$Y[,1],OSLIN=TRUE)
# MA-PLOT
plot(maA(norm.oslin),maM(norm.oslin),main="OSLIN")
# MXY-PLOT
mxy.plot(maM(norm.oslin)[,1],Ngc=maNgc(norm.oslin),Ngr=maNgr(norm.oslin),
Nsc=maNsc(norm.oslin),Nsr=maNsr(norm.oslin),main="OSLIN")
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