Row-by-Row Linear-Model Residuals for Gene Expression (or similar) Data Structures
This produces residuals of an identical linear model applied to each row of a gene expression matrix (or similar dataset). Computation speed is achieved via straightforward matrix algebra. Most commonly-used residual types are available.
An object produced by function
A string indicating the type of residual requeseted (defaults to externally-Studentized).
Types of residuals now available:
Response residuals, observed minus fitted
Response residuals divided by the estimated residual S.E.
Internally Studentized residuals, often referred to as "Standardized"
Externally Studentized residuals, which can be used directly for outlier identification
Returns a instance of
ExpressionSet where the expression matrix
contains the residuals. The
phenoData are inherited from
Robert Gentleman, Assaf Oron
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data(sample.ExpressionSet) lm1 = lmPerGene(sample.ExpressionSet,~sex) r1 = getResidPerGene(lm1) ### now a boxplot of all residuals by sample resplot(resmat=exprs(r1),fac=sample.ExpressionSet$sex) ### This plot is not very informative because of some gross outliers; ### try this instead resplot(resmat=exprs(r1),fac=sample.ExpressionSet$sex,lims=c(-5,5))
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