regionGoodnessOfFit-methods {Genominator} | R Documentation

## Calculate goodness-of-fit statistics

### Description

A generic method for calculating chi-squared goodness-of-fit statistics (See details). Dispatches on either a `data.frame` or and `ExpData` object.

### Usage

    ## 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"))

### Arguments

`obj` | `data.frame` or `ExpData`
`annoData` | A data.frame of annotation.
`groups` | A factor or character vector describing which are the replicates.
`denominator` | How to scale the columns to take into account sequencing depth.
`what` | Which columns to choose from the database. Default is all data columns.
`verbose` | Whether or not debugging / timing info should be printed.

### Details

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.

### Value

An list containing the statistics and degrees of freedom. See details. Technically, an S3 object with class genominator.goodness.of.fit

### Methods

`signature(obj = "ExpData")`

  Here `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: `length(denominator) == ncol(obj)`. Finally, groups determines how columns are aggregated across one another, i.e. which columns are replicates.

`signature(obj = "data.frame")`

  Here `annoData` is an annotation data frame. `groups` is as above. `what` represents the columns to select choose. `denominator` is either the total lane counts, or the lane counts restricted to `annoData`, or a vector of length `length(groups)`

### References

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

### Examples

    ed <- ExpData(system.file(package = "Genominator", "sample.db"),
                  tablename = "raw")
    data("yeastAnno")
    names(regionGoodnessOfFit(ed, yeastAnno))

[Package Genominator version 1.40.0 Index]