View source: R/discovery_prediction.R
auto_predict_grid | R Documentation |
Automatic filtering of signatures for exposure prediction gridded across specific annotation
auto_predict_grid(
musica,
table_name,
signature_res,
algorithm,
sample_annotation = NULL,
min_exists = 0.05,
proportion_samples = 0.25,
rare_exposure = 0.4,
verbose = TRUE,
combine_res = TRUE
)
musica |
Input samples to predit signature weights |
table_name |
Name of table used for posterior prediction (e.g. SBS96) |
signature_res |
Signatures to automatically subset from for prediction |
algorithm |
Algorithm to use for prediction. Choose from "lda_posterior", and decompTumor2Sig |
sample_annotation |
Annotation to grid across, if none given, prediction subsetting on all samples together |
min_exists |
Threshold to consider a signature active in a sample |
proportion_samples |
Threshold of samples to consider a signature active in the cohort |
rare_exposure |
A sample will be considered active in the cohort if at least one sample has more than this threshold proportion |
verbose |
Print current annotation value being predicted on |
combine_res |
Automatically combines a list of annotation results into a single result object with zero exposure values for signatures not found in a given annotation's set of samples |
A list of results, one per unique annotation value, if no annotation value is given, returns a single result for all samples, or combines into a single result if combines_res = TRUE
data(musica_annot)
data(cosmic_v2_sigs)
auto_predict_grid(musica = musica_annot, table_name = "SBS96",
signature_res = cosmic_v2_sigs, algorithm = "lda",
sample_annotation = "Tumor_Subtypes")
auto_predict_grid(musica_annot, "SBS96", cosmic_v2_sigs, "lda")
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