Description Usage Arguments Details Value Examples

Before running `spatialCluster()`

, we recommend tuning the choice of
`q`

by choosing the `q`

that maximizes the model's negative log
likelihood over early iterations. `qTune()`

computes the average
negative log likelihood for a range of q values over iterations 100:1000, and
`qPlot()`

displays the results.

1 2 3 |

`sce` |
A SingleCellExperiment object containing the spatial data. |

`qs` |
The values of q to evaluate. |

`force.retune` |
If specified, existing tuning values in |

`...` |
Other parameters are passed to |

`burn.in, nrep` |
Integers specifying the range of repetitions to compute. |

`qTune()`

takes the same parameters as `spatialCluster()`

and will
run the MCMC clustering algorithm up to `nrep`

iterations for each
value of `q`

. The first `burn.in`

iterations are discarded as
burn-in and the log likelihood is averaged over the remaining iterations.

`qPlot()`

plots the computed negative log likelihoods as a function of
q. If `qTune()`

was run previously, i.e. there exists an attribute of
`sce`

named `"q.logliks"`

, the pre-computed results are
displayed. Otherwise, or if `force.retune`

is specified,
`qplot()`

will automatically run `qTune()`

before plotting (and
can take the same parameters as `spatialCluster()`

.

`qTune()`

returns a modified `sce`

with tuning log
likelihoods stored as an attribute named `"q.logliks"`

.

`qPlot()`

returns a ggplot object.

1 2 3 4 | ```
set.seed(149)
sce <- exampleSCE()
sce <- qTune(sce, seq(3, 7), burn.in=10, nrep=100)
qPlot(sce)
``` |

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