topCounts: Get the top counts corresponding to some group from a...

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

Description

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

Usage

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

Arguments

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.

Details

The argument 'group' can be specified either as a number, giving the index of an element in the [email protected] 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.

Value

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.

Author(s)

Thomas J. Hardcastle

See Also

countData

Examples

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# 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)

baySeq documentation built on Nov. 1, 2018, 5:05 a.m.