Index

A

atlas2target() (in module scnym.api), [1], [2], [3]

augment_pseudolabels (scnym.trainer.MixMatchLoss attribute), [1], [2], [3]

B

backward() (scnym.model.GradReverse static method), [1], [2], [3]

balance_classes() (in module scnym.dataprep), [1]

C

CellTypeCLF (class in scnym.model), [1], [2], [3] CellTypeCLFConditional (class in scnym.model), [1], [2], [3]

criterion (scnym.trainer.Trainer attribute), [1], [2], [3] cross_entropy() (in module scnym.trainer), [1], [2], [3]

D

DANLoss (class in scnym.trainer), [1], [2], [3]

DANN (class in scnym.model), [1], [2], [3] dataloaders (scnym.trainer.Trainer attribute), [1], [2], [3]

E

ExpMinusOne (class in scnym.dataprep), [1]

F

forward() (scnym.model.CellTypeCLF method), [1], [2], [3] (scnym.model.CellTypeCLFConditional method), [1], [2], [3] (scnym.model.DANN method), [1], [2], [3] (scnym.model.GradReverse static method), [1], [2], [3] (scnym.model.ResBlock method), [1], [2], [3]

G

GeneMasking (class in scnym.dataprep), [1] get_class_weight() (in module scnym.trainer), [1], [2], [3]

get_pretrained_weights() (in module scnym.api), [1], [2], [3] get_saliency() (scnym.interpret.Salience method), [1], [2], [3] GradReverse (class in scnym.model), [1], [2], [3]

I

ICLWeight (class in scnym.trainer), [1], [2], [3] identity() (in module scnym.dataprep), [1]

init_dropout (scnym.model.CellTypeCLF attribute), [1], [2], [3] InputDropout (class in scnym.dataprep), [1] InterpolationConsistencyLoss (class in scnym.trainer), [1], [2], [3]

L

LibrarySizeNormalize (class in scnym.dataprep), [1]

M

MixMatchLoss (class in scnym.trainer), [1], [2], [3] mixup() (in module scnym.dataprep), [1] model (scnym.trainer.Trainer attribute), [1], [2], [3] module scnym, [1] scnym.api, [1], [2], [3], [4], [5] scnym.dataprep, [1] scnym.interpret, [1], [2], [3] scnym.model, [1], [2], [3] scnym.trainer, [1], [2], [3]

MultinomialSample (class in scnym.dataprep), [1]

N

n_augmentations (scnym.trainer.MixMatchLoss attribute), [1], [2], [3] n_batches_to_store (scnym.trainer.MixMatchLoss attribute), [1], [2], [3] n_cell_types (scnym.model.CellTypeCLF attribute), [1], [2], [3] n_epochs (scnym.trainer.Trainer attribute), [1], [2], [3] n_genes (scnym.model.CellTypeCLF attribute), [1], [2], [3] (scnym.model.CellTypeCLFConditional attribute), [1], [2], [3]

n_hidden (scnym.model.CellTypeCLF attribute), [1], [2], [3] n_layers (scnym.model.CellTypeCLF attribute), [1], [2], [3] n_tissues (scnym.model.CellTypeCLFConditional attribute), [1], [2], [3] num_classes (scnym.dataprep.SingleCellDS attribute), [1]

O

optimizer (scnym.trainer.Trainer attribute), [1], [2], [3]

out_path (scnym.trainer.Trainer attribute), [1], [2], [3]

P

PoissonSample (class in scnym.dataprep), [1]

pseudolabel_min_confidence (scnym.trainer.MixMatchLoss attribute), [1], [2], [3]

R

rank_genes_by_saliency() (scnym.interpret.Salience method), [1], [2], [3] reg_criterion (scnym.trainer.Trainer attribute), [1], [2], [3]

ResBlock (class in scnym.model), [1], [2], [3] residual (scnym.model.CellTypeCLF attribute), [1], [2], [3] running_confidence_scores (scnym.trainer.MixMatchLoss attribute), [1], [2], [3]

S

Salience (class in scnym.interpret), [1], [2], [3] SampleMixUp (class in scnym.dataprep), [1] save_freq (scnym.trainer.Trainer attribute), [1], [2], [3] scheduler (scnym.trainer.Trainer attribute), [1], [2], [3] scnym module, [1] scnym.api module, [1], [2], [3], [4], [5] scnym.dataprep module, [1] scnym.interpret module, [1], [2], [3]

scnym.model module, [1], [2], [3] scnym.trainer module, [1], [2], [3] scnym_api() (in module scnym.api), [1], [2], [3] scnym_predict() (in module scnym.api), [1], [2], [3] scnym_train() (in module scnym.api), [1], [2], [3] SemiSupervisedTrainer (class in scnym.trainer), [1], [2], [3] set_rev_grad_weight() (scnym.model.DANN method), [1], [2], [3] sharpen_labels() (in module scnym.trainer), [1], [2], [3] SingleCellDS (class in scnym.dataprep), [1]

T

T (scnym.trainer.MixMatchLoss attribute), [1], [2], [3] teacher (scnym.trainer.MixMatchLoss attribute), [1], [2], [3] train() (scnym.trainer.Trainer method), [1], [2], [3]

train_epoch() (scnym.trainer.SemiSupervisedTrainer method), [1], [2], [3] (scnym.trainer.Trainer method), [1], [2], [3] Trainer (class in scnym.trainer), [1], [2], [3] transform (scnym.dataprep.SingleCellDS attribute), [1]

U

use_gpu (scnym.trainer.Trainer attribute), [1], [2], [3]

V

val_epoch() (scnym.trainer.Trainer method), [1], [2], [3]

verbose (scnym.trainer.Trainer attribute), [1], [2], [3]

X

X (scnym.dataprep.SingleCellDS attribute), [1]

Y

y (scnym.dataprep.SingleCellDS attribute), [1]

y_labels (scnym.dataprep.SingleCellDS attribute), [1]