select_methods {scone} | R Documentation

## Get a subset of normalizations from a SconeExperiment object

### Description

This method let a user extract a subset of normalizations. This is useful when the original dataset is large and/or many normalization schemes have been applied.

In such cases, the user may want to run scone in mode `return_norm = "no"`, explore the results, and then select the top performing methods for additional exploration.

### Usage

    select_methods(x, methods)

    ## S4 method for signature 'SconeExperiment,character'
    select_methods(x, methods)

    ## S4 method for signature 'SconeExperiment,numeric'
    select_methods(x, methods)

### Arguments

`x` | a `SconeExperiment` object.
`methods` | either character or numeric specifying the normalizations to select.

### Details

The numeric method will always return the normalization corresponding to the `methods` rows of the `scone_params` slot. This means that if `scone` was run with `eval=TRUE`, `select_methods(x, 1:3)` will return the top three ranked method. If `scone` was run with `eval=FALSE`, it will return the first three normalization in the order saved by scone.

### Value

A `SconeExperiment` object with selected method data.

### Functions

- `select_methods,SconeExperiment,character-method`: If `methods` is a character, it will return the subset of methods named in `methods` (only perfect match). The string must be a subset of the `row.names` of the slot `scone_params`.

- `select_methods,SconeExperiment,numeric-method`: If `methods` is a numeric, it will return the subset of methods according to the scone ranking.

### Examples

    set.seed(42)
    mat <- matrix(rpois(500, lambda = 5), ncol=10)
    colnames(mat) <- paste("X", 1:ncol(mat), sep="")
    obj <- SconeExperiment(mat)
    res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),
               evaluate=TRUE, k_ruv=0, k_qc=0,
               eval_kclust=2, bpparam = BiocParallel::SerialParam())
    select_res = select_methods(res,1:2)

[Package scone version 1.16.1 Index]