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Model registry

Models

Scaden

Paper: Deep learning–based cell composition analysis from tissue expression profiles

from pydeconv.model import Scaden
model = Scaden()
model.transform(adata, layer="tpm", ratio=True)

Registered models

  • cti_dirichlet_2nd_granularity

    ... details dataset and model ...

    Output Celltypes

    B, CD4T, CD8T, DC, Mast, Mono, NK, Plasma, Tregs

TAPE

Paper: Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

from pydeconv.model import Tape
model = Tape()
model.transform(adata, layer="tpm", ratio=True)

Registered models

  • cti_dirichlet_2nd_granularity

    ... details dataset and model ...

    Output Celltypes

    B, CD4T, CD8T, DC, Mast, Mono, NK, Plasma, Tregs

Signature based models

OLS

Paper:

from pydeconv.model import OLS
model = OLS(signature_matrix)
model.transform(adata, layer="tpm", ratio=True)

RLR

Paper:

from pydeconv.model import RLR
model = RLR(signature_matrix)
model.transform(adata, layer="tpm", ratio=True)

NNLS

Paper: Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus

from pydeconv.model import NNLS
model = NNLS(signature_matrix)
model.transform(adata, layer="tpm", ratio=True)

DWLS

Paper: Accurate estimation of cell-type composition from gene expression data

from pydeconv.model import DWLS
model = DWLS(signature_matrix)
model.transform(adata, layer="tpm", ratio=True)

WNNLS (MuSiC)

Paper: Bulk tissue cell type deconvolution with multi-subject single-cell expression reference

from pydeconv.model import WNNLS
model = WNNLS(signature_matrix)
model.transform(adata, layer="tpm", ratio=True)