LVSmiRNA (version 1.22.0)

lvs: Least Variant Set selection and Normalization Function(s)

Description

Selects the Least Variant Set of mircoRNAs, according to the chosen proportion of miRNAs expected not to vary between arrays. Then performs normalization.

Usage

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,...)
"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,...)
"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,...)

Arguments

RG

an object of class EList or RGList

RA

a list contaning components residual standard deviations, chi-square statistics and array effects. It can be computed by estVC. If not provided it will computed (slower),

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 summarize.

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 probe-level.

Atransf

Which transformation to use for Array Effect

keep.iset

return the LVS ids

clName

Cluster object. See estVC.

verbose

Verbose computation

...

...

Value

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 FileName giving the names of the files read, with column Sample giving the names of the samplse.

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 Background, Normalization, is.log, Summarization indicate which pre-processing step has been done.

Details

lvs works by first identifying least variant set (LVS) with the smallest array-to-array variation. The total information extracted from probe-level 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.

References

Calza et al., 'Normalization of oligonucleotide arrays based on the least variant set of genes' (2008, BMCBioinformatics).

See Also

estVC, summarize

Examples

## Not run: 
# 
# # Starting from an Elist object called MIR
# data("MIR-spike-in")
# AA <- estVC(MIR,method="joint")
# bb <- lvs(MIR,RA=AA,level="probe")
# 
# ##It can also run with object RA missing, but taking longer time
# cc <- lvs(MIR)
#  ## End(Not run)