LVSmiRNA (version 1.22.0)

estVC: Robust Linear Model to Estimate Residual Variance and Array Effect

Description

Given intensities from microRNA data, fits a robust linear model at probe level and return the residual standard deviations and the array effects.

Usage

estVC(object,method=c("joint","rlm"),cov.formula=c("weighted","asymptotic"),clName,verbose=FALSE)
"estVC"(object,method=c("joint","rlm"),cov.formula=c("weighted","asymptotic"),clName,verbose=FALSE)
"estVC"(object,method=c("joint","rlm"),cov.formula=c("weighted","asymptotic"),clName,verbose=FALSE)

Arguments

object

an object of class EList or RGList.

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".

clName

Cluster object produced by makeCluster function. Used only if snow is loaded.

verbose

Print some debug messages.

Value

An object of class RA containing three components as follows:

ArrayEffects

a matrix containing the array effect with samples as columns and miRNAs as rows.

ArrayChi2

vector giving chi-square statisitcs of the miRNAs as a measure of array-to-array variability.

logStdDev

vector giving standard deviations of the genes on log scale.

Details

estVC is the first step in LVS normalization. It fits a robust linear model at the probe-level data in order to estimate the variability of probe intensities due to array-to-array variability. Depending on whether probes show considerable differences in within-probe variance, user can choose the more complex joint model to accommodate the potential heteroscedasticity or standard robust linear model if within-probe variance can be ignored.

The array effects are then captured by the chi-square statistic. The covariance matrix can be estimated based either on the sandwich form of weighted covariance matrix or an asymptotic form.

References

Calza et al., 'Normalization of oligonucleotide arrays based on the least variant set of genes', (2008, BMCBioinformatics); Pawitan, Y. 'In All Likelihood: Statistical Modeling and Inference Using Likelihood', (2001, Oxford University Press); Huber, P. J., 'Robust estimation of a location parameter', (1964, Annuas of Mathematical Statistics).

See Also

read.mir, lvs

Examples

## Not run: 
# 
# # Starting from an EList object called MIR
# data("MIR-spike-in")
# AA <- estVC(MIR,method="joint")
# 
# # Parellel execution using multicore
# 
# library(multicore)
# 
# # use this to set the desided number of
# #cores. Otherwise multicore would use all the available
# options(cores=8)
# 
# AA <- estVC(MIR,method="joint")
# 
# detach('package:multicore')
# 
# # Parellel execution using snow
# 
# 
# library(snow)
# 
# cl <- makeCluster(8,type="SOCK")
# 
# # Or also...see ?makeCluster
# # cl <- makeCluster(8,type="MPI")
# 
# AA <- estVC(MIR,method="joint",clName=cl)
# 
# ## End(Not run)