prior_flowclust {openCyto} | R Documentation

## Elicits data-driven priors from a flowSet object for specified channels

### Description

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.

### Usage

    prior_flowclust(
      flow_set,
      channels,
      prior_method = c("kmeans"),
      K = 2,
      nu0 = 4,
      w0 = c(10, 10),
      shrink = 1e-06,
      ...
    )

### Arguments

`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

### Details

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`.

### Value

list of the necessary prior parameters

[Package openCyto version 2.0.0 Index]