estimate.affinities: estimate.affinities
In RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

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

Probe affinity estimation. Estimates probe-specific affinity parameters.

1	estimate.affinities(dat, a)

`dat`	Input data set: probes x samples.
`a`	Estimated expression signal from RPA model.

To estimate means in the original data domain let us assume that each probe-level observation x is of the following form: x = d + v + noise, where x and d are vectors over samples, v is a scalar (vector with identical elements) noise is Gaussian with zero mean and probe-specific variance parameters tau2 Then the parameter mu will indicate how much probe-level observation deviates from the estimated signal shape d. This deviation is further decomposed as mu = mu.real + mu.probe, where mu.real describes the 'real' signal level, common for all probes mu.probe describes probe affinity effect Let us now assume that mu.probe ~ N(0, sigma.probe). This encodes the assumption that in general the affinity effect of each probe tends to be close to zero. Then we just calculate ML estimates of mu.real and mu.probe based on particular assumptions. Note that this part of the algorithm has not been defined in full probabilistic terms yet, just calculating the point estimates. Note that while tau2 in RPA measures stochastic noise, and NOT the affinity effect, we use it here as a heuristic solution to weigh the probes according to how much they contribute to the overall signal shape. Intuitively, probes that have little effect on the signal shape (i.e. are very noisy and likely to be contaminated by many unrelated signals) should also contribute less to the absolute signal estimate. If no other prior information is available, using stochastic parameters tau2 to determine probe weights is likely to work better than simple averaging of the probes without weights. Also in this case the probe affinities sum close to zero but there is some flexibility, and more noisy probes can be downweighted.

A vector with probe-specific affinities.

Leo Lahti leo.lahti@iki.fi

See citation("RPA")

rpa.fit

1	# mu <- estimate.affinities(dat, a)

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Loading required package: Biobase
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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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