gls.series {limma} | R Documentation |
Fit linear models for each gene to a series of microarrays. Fit is by generalized least squares allowing for correlation between duplicate spots or between technical replicates.
gls.series(M,design=NULL,ndups=2,spacing=1,block=NULL,correlation=NULL,weights=NULL,...)
M |
a numeric matrix. Usually the log-ratios of expression for a series of cDNA microarrrays with rows corresponding to genes and columns to arrays. |
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 M . 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(M) must be divisible by ndups . |
spacing |
the spacing between the rows of M corresponding to duplicate spots, spacing=1 for consecutive spots |
block |
vector or factor specifying a blocking variable |
correlation |
the inter-duplicate or inter-technical replicate correlation. |
weights |
an optional numeric matrix of the same dimension as M containing weights for each spot. If it is of different dimension to M , it will be filled out to the same size. |
... |
other optional arguments to be passed to dupcor.series . |
This is the low level function for fitting gene-wise linear models when there is a correlation structure arising from duplicate spots or technical replicates.
This function is called by lmFit
if appropriate.
Note that the correlation is assumed to be constant across genes.
If correlation=NULL
then a call is made to duplicateCorrelation
to estimated the correlation.
A list with components
coefficients |
numeric matrix containing the estimated coefficients for each linear model. Same number of rows as M , same number of columns as design . |
stdev.unscaled |
numeric matrix conformal with coef containing the unscaled standard deviations for the coefficient estimators. The standard errors are given by stdev.unscaled * sigma . |
sigma |
numeric vector containing the residual standard deviation for each gene. |
df.residual |
numeric vector giving the degrees of freedom corresponding to sigma . |
correlation |
inter-duplicate correlation. |
Gordon Smyth
An overview of linear model functions in limma is given by 5.LinearModels.
M <- matrix(rnorm(10*6),10,6) dupcor <- duplicateCorrelation(M) fit <- gls.series(M,correlation=dupcor$consensus.correlation)