dupcor: Correlation Between Duplicates or Within Blocks

In hdeberg/limma: Linear Models for Microarray Data

Description

Estimate the intra-block correlation given a block structure for the arrays or samples.

Usage

duplicateCorrelation(object, design=NULL, ndups=2, spacing=1, block=NULL,
                     trim=0.15, weights=NULL)

Arguments

object	A matrix-like data object containing log-ratios or log-expression values for a series of samples, with rows corresponding to genes and columns to samples. Any type of data object that can be processed by getEAWP is acceptable.
design	the design matrix of the microarray experiment, with rows corresponding to arrays and columns to comparisons to be estimated. The number of rows must match the number of columns of object. Defaults to the unit vector meaning that the arrays are treated as replicates.
ndups	a positive integer giving the number of times each gene is printed on an array. nrow(object) must be divisible by ndups. Ignored if block is specified.
spacing	the spacing between the rows of object corresponding to duplicate spots, spacing=1 for consecutive spots
block	vector or factor specifying a blocking variable
trim	the fraction of observations to be trimmed from each end of tanh(all.correlations) when computing the trimmed mean.
weights	an optional numeric matrix of the same dimension as object containing weights for each spot. If smaller than object then it will be filled out to the same size.

Details

When block=NULL, this function estimates the correlation between duplicate spots (regularly spaced within-array replicate spots). If block is not null, this function estimates the correlation between repeated observations on the blocking variable. Typically the blocks are biological replicates and repeated observations on the same block may be correlated. In either case, the correlation is estimated by fitting a mixed linear model by REML individually for each gene. The function also returns a consensus correlation, which is a robust average of the individual correlations, intended for input to functions such as lmFit, gls.series or voom.

It is not possible to estimate correlations between duplicate spots and with sample blocks simultaneously. If block is not null, then the function will set ndups=1, which is equivalent to ignoring duplicate spots.

For this function to return statistically useful results, there must be at least two more arrays than the number of coefficients to be estimated, i.e., two more than the column rank of design.

The function may take long time to execute as it fits a mixed linear model for each gene using an iterative algorithm.

If present, ndups and spacing will be extracted from object$printer$ndups and object$printer$spacing.

Value

A list with components

consensus.correlation	the average estimated inter-duplicate correlation. The average is the trimmed mean of the individual correlations on the atanh-transformed scale.
cor	same as consensus.correlation, for compatibility with earlier versions of the software
atanh.correlations	numeric vector of length nrow(object)/ndups giving the individual genewise atanh-transformed correlations.

Author(s)

Gordon Smyth

References

Smyth, G. K., Michaud, J., and Scott, H. (2005). The use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21(9), 2067-2075. [http://bioinformatics.oxfordjournals.org/content/21/9/2067] [Preprint with corrections: http://www.statsci.org/smyth/pubs/dupcor.pdf]

See Also

These functions use mixedModel2Fit from the statmod package.

An overview of linear model functions in limma is given by 06.LinearModels.

Examples

# Simulate a paired experiment with incomplete blocks
Block <- c(1,1,2,2,3,3,4,4,5,6,7,8)
Treat <- factor(c(1,2,1,2,1,2,1,2,1,2,1,2))
design <- model.matrix(~Treat)

ngenes <- 50
nsamples <- 12
y <- matrix(rnorm(ngenes*nsamples),ngenes,nsamples)
rownames(y) <- paste0("Gene",1:ngenes)

# Estimate the within-block correlation
dupcor <- duplicateCorrelation(y,design,block=Block)
dupcor$consensus.correlation

# Estimate the treatment effect using both complete and incomplete blocks
fit <- lmFit(y,design,block=Block,correlation=dupcor$consensus)
fit <- eBayes(fit)
topTable(fit,coef=2)

hdeberg/limma documentation built on Dec. 20, 2021, 3:43 p.m.