topCounts: Get the top counts corresponding to some group from a...
In baySeq: Empirical Bayesian analysis of patterns of differential expression in count data

Description Usage Arguments Details Value Author(s) See Also Examples

Takes posterior likelihoods and returns the counts with highest (or lowest) likelihood of association with a given group.

1 2	topCounts(cD, group, ordering, decreasing = TRUE, number = 10, likelihood, FDR, FWER, normaliseData = FALSE, posteriors)

`cD`	`countData` object, containing posterior likelihoods for each group (unless ‘posteriors’ is specified; see below).
`group`	Which group should we give the counts for? See Details.
`ordering`	If specified, restricts the analysis to a particlar ordering on the group.
`decreasing`	Ordering on posterior likelihoods.
`number`	How many results should be returned?
`likelihood`	If given, ignores ‘number’ and returns all results above a certain likelihood (and FDR, and FWER, if given).
`FDR`	If given, ignores ‘number’ and returns all results with an FDR lower than this threshold (and likelihood, and FWER, if given).
`FWER`	If given, ignores ‘number’ and returns all results with an FWER lower than this threshold (and likelihood, and FDR, if given).
`normaliseData`	Should the displayed counts be normalised? See details. Defaults to FALSE.
`posteriors`	If given, a vector of log-posterior likelihoods to use instead of those in the ‘@posteriors’ slot of the ‘cD’ object.

The argument 'group' can be specified either as a number, giving the index of an element in the cD@groups list, or as a character string identifying an element by name. Partial matching is allowed. If group = NULL, then the function looks at the posterior likelihoods that the data have no true differential expression (if calculated).

If a countData object is given, the returned dataframe will contain either the raw counts for that object, or (if 'normaliseData = TRUE' the counts normalised by library size.

A dataframe of the top counts associated with some model (group), described by annotation drawn from the '@annotation' slot of the 'cD' object and the raw data from the '@data' slot, together with the posterior likelihoods and false discovery rates.

Thomas J. Hardcastle

countData

# We load in a `countData' object containing the estimated posterior
# likelihoods of expression (see `getLikelihoods').

data(CDPost)

# Report the top ten rows of data that have highest likelihood of belonging to
# group 2 of the data (i.e., differentially expressed)

topCounts(CDPost, group = "DE", number = 10)

# equivalently...
topCounts(CDPost, group = 2, number = 10)


# Report the top ten rows of data that have highest likelihood of belonging to
# group 2 of the data (i.e., differentially expressed), with group 1
# being overexpressed compared to group 2.

topCounts(CDPost, group = "DE", ordering = "1>2", number = 10)