getTPs: Gets the number of true positives in the top n counts...
In baySeq: Empirical Bayesian analysis of patterns of differential expression in count data

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

If the true positives are known, this function will return a vector, the ith member of which gives the number of true positives identified if the top i counts, based on estimated posterior likelihoods, are chosen.

1	getTPs(cD, group, decreasing = TRUE, TPs)

`cD`	`countData` object, containing posterior likelihoods for each group.
`group`	Which group should we give the counts for? See Details.
`decreasing`	Ordering on posterior likelihoods.
`TPs`	Known true positives.

In the rare (or simulated) cases where the true positives are known, this function will calculate the number of true positives selected at any cutoff.

The 'group' can be defined either as the number of the element in 'cD@groups' or as a string which will be partially matched to the names of the 'cD@groups' elements.If group = NULL, then the function looks at the posterior likelihoods that the data have no true differential expression (if calculated).

A vector, the ith member of which gives the number of true positives identified if the top i counts are chosen.

Thomas J. Hardcastle

countData

# See vignette for more examples.

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

data(CDPost)

# If the first hundred rows in the 'simData' matrix are known to be
# truly differentially expressed (the second hypothesis defined in the
# 'groups' list) then we find the number of true positives for the top n
# genes selected as the nth member of

getTPs(CDPost, group = "DE", decreasing = TRUE, TPs = 1:100)