prior_flowclust: Elicits data-driven priors from a flowSet object for...
In openCyto: Hierarchical Gating Pipeline for flow cytometry data

Description Usage Arguments Details Value

We elicit data-driven prior parameters from a flowSet object for specified channels. For each sample in the flowSet object, we apply the given prior_method to elicit the priors parameters.

prior_flowclust(
  flow_set,
  channels,
  prior_method = c("kmeans"),
  K = 2,
  nu0 = 4,
  w0 = c(10, 10),
  shrink = 0.000001,
  ...
)

`flow_set`	a `flowSet` object
`channels`	a character vector containing the channels in the `flowSet` from which we elicit the prior parameters for the Student's t mixture
`prior_method`	the method to elicit the prior parameters
`K`	the number of mixture components to identify
`nu0`	prior degrees of freedom of the Student's t mixture components.
`w0`	the number of prior pseudocounts of the Student's t mixture components. (only the first element is used and the rest is ignored at the moment)
`shrink`	the amount of eigenvalue shrinkage to add in the case the prior covariance matrices are singular. See details.
`...`	Additional arguments passed to the prior elicitation method selected

Currently, we have implemented only two methods. In the case that one channel is given, we use the kernel-density estimator (KDE) approach for each sample to obtain K peaks from which we elicit prior parameters. Otherwise, if more than one channel is specified, we apply K-Means to each of the samples in the flowSet and aggregate the clusters to elicit the prior parameters.

In the rare case that a prior covariance matrix is singular, we shrink the eigenvalues of the matrix slightly to ensure that it is positive definite. For instance, if the flow_set has two samples, this case can occur. The amount of shrinkage is controlled in shrink.

list of the necessary prior parameters

openCyto documentation built on Nov. 8, 2020, 5:40 p.m.