topCounts {baySeq} | R Documentation |
Takes posterior likelihoods and returns the counts with highest (or lowest) likelihood of association with a given group.
topCounts(cD, group, ordering, decreasing = TRUE, number = 10, likelihood, FDR, FWER, normaliseData = FALSE, posteriors)
cD |
|
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
# 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)