build.Rnits: Input the RGlist raw data, build a Rnits object and perform...
In Rnits: R Normalization and Inference of Time Series data

Description Usage Arguments Details Value See Also Examples

This function takes high-dimensional expression data as a RGList, creates a Rnits object for subsequent filtering and normalization

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build.Rnits(obj, probedata = NULL, phenodata = NULL, filter = NULL,
  normalize = NULL, normmethod = NULL, plot = FALSE, center = FALSE,
  background = NULL, threshold = 0.8, logscale = FALSE)

`obj`	Raw expression data in `RGlist`, `AffyBatch` or simple data frame format
`probedata`	A data frame containing the probe names that should match the probe names in raw data (optional)
`phenodata`	A data frame with information about sample names. The rownames of the data frame must match column names of the expression values. If input data is data frame of log ratios, this is required.
`filter`	An argument to perform background filtering of probes. If `NULL`, no filtering is done. If an integer (0-500), probes are flagged based on raw channel intensity. If a vector of two numbers is provided, the first will be used for red channel and the second for green channel. If `'background'`, probes whose intensities are lower than 2 standard deviations less than the mean of the background intensity for the channel are flagged.
`normalize`	Character string specifying the normalization method for raw data. If `Intensity`, the reference channels for all arrays are used to construct an array-specific smoothing function which is then applied to normalize the sample channel. If `Between`, the normalization method `normalizeBetweenArrays` in the LIMMA package is used (use `normmethod` to further specify normalization methods. See packaged LIMMA for details.). If `Within`, the normalization method `normalizeWithinArrays` in the LIMMA package is used.
`normmethod`	Normalization method for input data. Default `NULL`. Can be one of 'quantile', 'vsn', 'Between'
`background`	Only for AffyBatch data. If `TRUE`, background filtering will be done on Affy data.
`center`	If `TRUE`, the log-ratio data will be mean centered to 0 in the column space.
`plot`	If `TRUE`, boxplots of normalized channel intensities and log-ratios are drawn.
`threshold`	Default `0.8`. Fraction of samples with missing data for individual probes to be filtered out.
`logscale`	Default `FALSE`. Is the data in logscale? If FALSE, log2 transformation is done on the data.

See the Limma User's Guide for more details on read.maimages, normalizeBetweenArrays, normalizeWithinArrays and RGList. For importing microarray raw data, use the 'Targets file' to specify experimental design. The target file has columns SlideNumber, FileName, Cy3 (description of Cy3 channel ref/control/treatment), Cy5 (description of Cy3 channel ref/control/treatment) and Time. Time values should be identical for control and treatment.

An object of S4 class Rnits (which is derived from class exprSet), containing the probe data, design data, expression data, phenotypical data (i.e. Time).

ExpressionSet

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# load pre-compiled expressionSet object for Ronen and Botstein yeast chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')