pegasus.predict_scarches_scanvi
- pegasus.predict_scarches_scanvi(data, dir_path, label, predictions='predictions', matkey='counts', n_jobs=-1, random_state=0, max_epochs=None, batch=None, categorical_covariate_keys=None, continuous_covariate_keys=None, use_gpu=None)[source]
Run scArches training.
This is a wrapper of scvitools package.
- Parameters
data (
MultimodalData
.) – Annotated data matrix with rows for cells and columns for genes.dir_path (
str
.) – Save the model to this directory.label (
str
.) – The obs key representing labels.predictions (
str
, , optional, default:"predictions"
) – The obs key to store predicted labels.matkey (
str
, optional, default:"counts"
) – Matrix key for the raw countn_jobs (
int
, optional, default:-1
.) – Number of threads to use.-1
refers to using all physical CPU cores.random_state (
int
, optional, default:0
.) – Seed for random number generatormax_epochs (
int | None
, optional, default:None
.) – Maximum number of training epochs. Defaults to np.min([round((20000 / n_cells) * 100), 100])batch (
str
, optional, default:None
.) – If only one categorical covariate, the obs key representing batches that should be corrected for, default isNone
.categorical_covariate_keys (
List[str]
) – If multiple categorical covariates, a list of obs keys listing categorical covariates that should be corrected for, default isNone
.continuous_covariate_keys (
List[str]
) – A list of obs keys listing continuous covariates that should be corrected for, default isNone
.use_gpu (
str | int | bool | None
) – Use default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str, e.g., ‘cuda:0’), or use CPU (if False).
- Returns
data.obsm['X_scanVI']
: The embedding calculated by scanVI.data.obsm[predictions]
: The predicted labels by scanVI.
- Return type
Update
data.obsm
Examples
>>> pg.predict_scarches_scanvi(data, dir_path="scanvi_model/", label="celltype", matkey="counts", batch="tech")