Description Usage Arguments Details Value Author(s) References See Also Examples
Selects the Least Variant Set of mircoRNAs, according to the chosen proportion of miRNAs expected not to vary between arrays. Then performs normalization.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  lvs(RG,RA,ref,proportion=0.7,df=3,method=c("joint","rlm"),
cov.formula=c("weighted","asymptotic"),
spar=NULL,normalize.method=c("vsn","smooth.spline","mixed"),
summarize.args=NULL,stratify=TRUE,n.strata=3,
level=c("mir","probe"),Atransf=c("sqrt","log"),keep.iset=FALSE,clName,
verbose=FALSE,...)
## S3 method for class 'RGList'
lvs(RG,RA,ref,proportion=0.7,df=3,method=c("joint","rlm"),
cov.formula=c("weighted","asymptotic"),
spar=NULL,normalize.method=c("vsn","smooth.spline","mixed"),
summarize.args=NULL,stratify=TRUE,n.strata=3,
level=c("mir","probe"),Atransf=c("sqrt","log"),
keep.iset=FALSE,clName,verbose=FALSE,...)
## S3 method for class 'EList'
lvs(RG,RA,ref,proportion=0.7,df=3,method=c("joint","rlm"),
cov.formula=c("weighted","asymptotic"),
spar=NULL,normalize.method=c("vsn","smooth.spline","mixed"),
summarize.args=NULL,stratify=TRUE,n.strata=3,
level=c("mir","probe"),Atransf=c("sqrt","log"),keep.iset=FALSE,clName,
verbose=FALSE,...)

RG 
an object of class 
RA 
a list contaning components residual standard deviations,
chisquare statistics and array effects. It can be computed by

proportion 
the proportion below which miRNAs are expected not to vary between arrays. Default is set to 0.7. 
ref 
reference array to be used for normalization. Default is set to mean of array effects across samples. 
df 
the desired equivalent number of degrees of freedom(trace of the smooth matrix) in smoothing spline. 
method 
character string specifying the estimating algorithm to be used. Choices are "joint" and "rlm". 
cov.formula 
character string specifying the covariance formula to be used. Choices are "weighted" and "asymptotic". 
spar 
smoothing parameter, typicallly in (0,1]. 
normalize.method 
character string specifying the normalization method to be used. Choices are "smooth.spline" and "vsn". 
summarize.args 
a named list containnig components from argument of 
stratify 
logical, if TRUE selection of least variant set will be stratified by expression level. 
n.strata 
integer giving the number of strata. 
level 
character string specifying the normalization performed at miRNA level or probelevel. 
Atransf 
Which transformation to use for Array Effect 
keep.iset 
return the LVS ids 
clName 
Cluster object. See 
verbose 
Verbose computation 
... 

lvs
works by first identifying least variant set (LVS) with the smallest arraytoarray variation. The total information extracted from probelevel intensity data of all samples is modeled as a function of array and probe effect in order to select the reference set for normalization. If the residual variances and array effects are available, lvs
runs faster because the step of robust linear modeling has already been done.
Once the LVS miRNAs are identified, the normalization is performed using VSN
or smooth.spline
.
An object of the same class as RG.
G 
matrix containing the normalized intensities for each array with miRNAs as rows and arrays as columns. 
Gb 
matrix containing the background intensities for each array with probes as rows and arrays as columns. 
targets 
data frame with column 
genes 
data frame containing annotation information about the probes, for examples miRNA names and IDs and positions on the array. 
source 
character string giving the image analysis program name. 
preprocessing 
list with components 
Stefano Calza <stefano.calza@biostatistics.it>, Suo Chen and Yudi Pawitan.
Calza et al., 'Normalization of oligonucleotide arrays based on the least variant set of genes' (2008, BMCBioinformatics).
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