summarize.batch: summarize.batch
In RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

Description Usage Arguments Author(s) References Examples

Summarize batch.

1 2	summarize.batch(q, set.inds, probe.parameters = list(), epsilon, verbose = FALSE, mc.cores = 1, summarize.with.affinities = FALSE)

`q`	Background corrected, quantile-normalized, log2 probes x samples matrix
`set.inds`	Indices for each probeset, corresponding to q matrix
`probe.parameters`	A list, each element corresponding to a probe set. Each probeset element has the following elements: affinity, variance and optionally alpha and beta priors. Each of these elements contains a vector over the probeset probes, specifying the probe parameters according to the RPA model. If variances are given, that overrides the priors.
`epsilon`	Convergence tolerance. The iteration is deemed converged when the change in all parameters is < epsilon.
`verbose`	Print progress information during computation.
`mc.cores`	Number of cores for parallel processing
`summarize.with.affinities`	Use affinity estimates in probe summarization step. Default: FALSE.

Leo Lahti leo.lahti@iki.fi

See citation("RPA")

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RPA Copyright (C) 2008-2016 Leo Lahti.
This program comes with ABSOLUTELY NO WARRANTY.
This is free software, and you are welcome to redistribute it under the FreeBSD open source license.

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