Title Significance of Periodic Expression Pattern in Time-series Data

Description Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models License GPL-2 Index 8 ar1analysis Performs AR1 fitting Description Calculation of the autocorrelation coefficients genes and variance of corresponding random variables to fit gene expression time series by AR1 processes Usage ar1analysis(eset) 1 2 backgroundData Arguments eset object of the class " ExpressionSet " Value List of fitted autocorrelation coefficients (alpha) for ExpressionSet features and variance (sigma2) of corresponding random variables obtained using the ar function of the stats package. Note Note that this function evaluates soley the exprs matrix and no information is used from the phenoData. In particular, the ordering of samples (arrays) is the same as the ordering of the columns in the exprs matrix. Also, replicated arrays in the exprs matrix are treated as independent i.e. they should be averagered prior to analysis or placed into different distinct " ExpressionSet " objects. See Also ar Examples data(yeast) # loading the reduced CDC28 yeast set (from the Mfuzz package) # Data preprocessing if (interactive()){ data(yeast) yeast <-filter.NA(yeast) # filters genes with more than 25% of the expression values missing yeast <-fill.NA(yeast) # for illustration only; rather use knn method for replacing missing values tmp <-ar1analysis(yeast) # fits AR1 process autocorrelation coefficients plot(density(tmp$alpha),main="Autocorrelation") } backgroundData Generation of background expression set Description The function generates background expression sets using different methods (permutation within rows, Gaussian distribution, auto-regressive models) Usage backgroundData(eset,model=c("rr", "gauss", "ar1")) backgroundData