predict,PseudotimeOrdering-method {slingshot} | R Documentation

## Predict from a Slingshot model

### Description

Map new observations onto simultaneous principal curves fitted by `slingshot`.

### Usage

    ## S4 method for signature 'PseudotimeOrdering'
    predict(object, newdata = NULL)

    ## S4 method for signature 'SlingshotDataSet'
    predict(object, newdata = NULL)

### Arguments

`object` | a `PseudotimeOrdering` or `SlingshotDataSet` containing simultaneous principal curves to use for prediction.
`newdata` | a matrix or data frame of new points in the same reduced-dimensional space as the original input to `slingshot` (or `getLineages`).

### Details

This function is a method for the generic function `predict` with inputs being either a `PseudotimeOrdering` or `SlingshotDataSet`. If no `newdata` argument is provided, it will return the original results, given by `object`.

### Value

An object of the same type as `object`, based on the input `newdata`. New cells are treated as "unclustered", but other metadata is preserved. The `curves` slot represents the projections of each new cell onto the existing curves. As with standard `slingshot` output, the lineage-specific pseudotimes and assignment weights can be accessed via the functions `slingPseudotime` and `slingCurveWeights`.

### See Also

`slingshot`

### Examples

    data("slingshotExample")
    rd <- slingshotExample$rd
    cl <- slingshotExample$cl
    pto <- slingshot(rd, cl, start.clus = '1')

    x <- cbind(runif(100, min = -5, max = 10), runif(100, min = -4, max = 4))
    predict(pto, x)

[Package slingshot version 2.0.0 Index]