predict_exposure {musicatk} | R Documentation

## Prediction of exposures in new samples using pre-existing signatures

### Description

Exposures for samples will be predicted using an existing set of signatures stored in a `musica_result` object. Algorithms available for prediction include a modify version of `"lda"`, `"decompTumor2Sig"`, and `"deconstructSigs"`.

### Usage

    predict_exposure(
      musica,
      g,
      table_name,
      signature_res,
      algorithm,
      signatures_to_use = seq_len(ncol(signatures(signature_res))),
      verbose = FALSE
    )

### Arguments

`musica` | A `musica` object.
`g` | A BSgenome object indicating which genome reference the variants and their coordinates were derived from. Only used if `algorithm = "deconstructSigs"`
`table_name` | Name of table used for posterior prediction. Must match the table type used to generate the prediction signatures
`signature_res` | Signatures used to predict exposures for the samples `musica` object. Existing signatures need to stored in a `musica_result` object.
`algorithm` | Algorithm to use for prediction of exposures. One of `"lda"`, `"decompTumor2Sig"`, or `"deconstructSigs"`.
`signatures_to_use` | Which signatures in the `signature_res` result object to use. Default is to use all signatures.
`verbose` | If `TRUE`, progress will be printing. Only used if `algorithm = "lda"`. Default `FALSE`.

### Value

Returns a A `musica_result` object containing signatures given by the `signature_res` parameter and exposures predicted from these signatures.

### Examples

    data(musica)
    data(cosmic_v2_sigs)
    g <- select_genome("19")
    build_standard_table(musica, g, "SBS96", overwrite = TRUE)
    result <- predict_exposure(musica = musica, table_name = "SBS96",
    signature_res = cosmic_v2_sigs, algorithm = "lda")

    # Predict using LDA-like algorithm with seed set to 1
    set.seed(1)
    predict_exposure(musica = musica, table_name = "SBS96",
    signature_res = cosmic_v2_sigs, algorithm = "lda")

[Package musicatk version 1.0.0 Index]