rpa.fit: RPA fit

In microbiome/RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

RPA fit

Description

Fit the RPA model.

Usage

rpa.fit(
  dat,
  epsilon = 0.01,
  alpha = NULL,
  beta = NULL,
  tau2.method = "robust",
  d.method = "fast",
  summarize.with.affinities = FALSE
)

Arguments

dat	Original data: probes x samples.
epsilon	Convergence tolerance. The iteration is deemed converged when the change in all parameters is < epsilon.
alpha	alpha prior for inverse Gamma distribution of probe-specific variances. Noninformative prior is obtained with alpha, beta -> 0. Not used with tau2.method 'var'. Scalar alpha and beta are specify equal inverse Gamma prior for all probes to regularize the solution. The defaults depend on the method.
beta	beta prior for inverse Gamma distribution of probe-specific variances. Noninformative prior is obtained with alpha, beta -> 0. Not used with tau2.method 'var'. Scalar alpha and beta are specify equal inverse Gamma prior for all probes to regularize the solution. The defaults depend on the method.
tau2.method	Optimization method for tau2 (probe-specific variances); "robust": (default) update tau2 by posterior mean, regularized by informative priors that are identical for all probes (user-specified by setting scalar values for alpha, beta). This regularizes the solution, and avoids overfitting where a single probe obtains infinite reliability. This is a potential problem in the other tau2 update methods with non-informative variance priors. The default values alpha = 2; beta = 1 are used if alpha and beta are not specified. "mode": update tau2 with posterior mean "mean": update tau2 with posterior mean "var": update tau2 with variance around d. Applies the fact that tau2 cost function converges to variance with large sample sizes.
d.method	Method used to optimize d. Options: "fast": (default) weighted mean over the probes, weighted by probe variances The solution converges to this with large sample size. "basic": optimization scheme to find a mode used in Lahti et al. TCBB/IEEE; relatively slow; preferred with small sample size.
summarize.with.affinities	Use affinity estimates in probe summarization step. Default: FALSE.

Details

Fits the RPA model, including estimation of probe-specific affinity parameters. First learns a point estimate for the RPA model in terms of differential expression values w.r.t. reference sample. After this, probe affinities are estimated by comparing original data and differential expression shape, and setting prior assumptions concerning probe affinities.

Value

mu: Fitted signal in original data: mu.real + d; mu.real: Shifting parameter of the reference sample; tau2: Probe-specific stochastic noise; affinity: Probe-specific affinities; data: Probeset data matrix; alpha, beta: prior parameters

Author(s)

Leo Lahti [e-mail]

References

See citation("RPA")

See Also

rpa, estimate.affinities

Examples

# res <- rpa.fit(dat, epsilon, alpha, beta, tau2.method, d.method, affinity.method)

microbiome/RPA documentation built on April 9, 2023, 10:59 a.m.