Quickstart

The goal of this project is to have a robust python implementation of deconvolution methods for bulk RNA-seq data.

Concepts

• Introduction to deconvolution

1. Load an already registered signature matrix

from pydeconv.signature_matrix.registry import sig_matrix_laughney_lung_cancer, sig_matrix_laughney_lung_cancer
signature_matrix = sig_matrix_laughney_lung_cancer()

Checkout here for more description of other registered signature matrix.

2. Load a custom signature matrix

from pydeconv import SignatureMatrix
signature_matrix = SignatureMatrix.load("path/to/signature_matrix.csv") #index: gene names, column: cell types

Note

For the moment only .csv format is supported. You can add any kwargs arguments from pd.read_csv after the path.

3. Predict

from pydeconv.model import Tape, Scaden

adata = AnnData("path/to/adata.h5ad") # index: sample_id, columns: gene_names
adata.layers["relative_counts"] = ...

solver = Scaden()
cell_prop = solver.transform(adata, layer="relative_counts", ratio=True)

Note

The model will check that you have the corresponding gene names in your input data.

4. Predict (signature based method)

from pydeconv.model import OLS, NNLS, DWLS

signature_matrix = ...
adata = AnnData("path/to/adata.h5ad")
adata.layers["relative_counts"] = ...

solver = DWLS(signature_matrix)
cell_prop = solver.transform(adata, layer="relative_counts", ratio=True)