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,
modality,
signature_res,
algorithm,
model_id = NULL,
result_name = "result",
sample_annotation = NULL,
min_exists = 0.05,
proportion_samples = 0.25,
rare_exposure = 0.4,
verbose = TRUE,
combine_res = TRUE,
make_copy = FALSE,
table_name = NULL
)
musica |
Input samples to predict signature weights |
modality |
Modality 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 |
model_id |
Name of model |
result_name |
Name for result_list entry to save the results to. Default
|
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 |
make_copy |
If |
table_name |
Use modality instead |
Returns nothing or a new musica
object,
depending on the make_copy
parameter.
data(musica_annot)
data(cosmic_v2_sigs)
auto_predict_grid(
musica = musica_annot, modality = "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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