A generic method for calculating chi-squared goodness-of-fit
statistics (See details). Dispatches on either a
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## S4 method for signature 'data.frame' regionGoodnessOfFit(obj, denominator = colSums(obj), groups = rep("A", ncol(obj))) ## S4 method for signature 'ExpData' regionGoodnessOfFit(obj, annoData, groups = rep("A", length(what)), what = getColnames(obj, all = FALSE), denominator = c("regions", "lanes"), verbose = getOption("verbose"))
A data.frame of annotation.
A factor or character vector describing which are the replicates.
How to scale the columns to take into account sequencing depth.
Which columns to choose from the database. Default is all data columns.
Whether or not debugging / timing info should be printed.
This function implements the homogenous Poisson model across lanes as described in the article cited below. This model corresponds to common expression parameter across lanes scaled by a lane-specific offset. Goodness of fit to this model across replicates is a good indication of Poisson variation across lanes. Deviation from this is an indication of overdispersion between replicate lanes.
An list containing the statistics and degrees of freedom. See details. Technically, an S3 object with class genominator.goodness.of.fit
signature(obj = "ExpData")
obj represents the results of a call to
summarizeByAnnotation or a data.frame with columns
representing samples and rows representing regions,
i.e. genes. Denominator is how we scale each column, therefore it
this must be true:
ncol(obj). Finally, groups determines how columns are aggregated
across one another, i.e. which columns are replicates.
signature(obj = "data.frame")
annoData is an annotation data frame.
what represents the columns to select
denominator is either the total lane counts, or the
lane counts restricted to
annoData, or a vector of length
James H. Bullard, Elizabeth A. Purdom, Kasper D. Hansen, Steffen Durinck, and Sandrine Dudoit, "Statistical Inference in mRNA-Seq: Exploratory Data Analysis and Differential Expression" (April 2009). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 247. http://www.bepress.com/ucbbiostat/paper247
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