scBiMapping

Fast and Accurate Non-linear Dimensionality Reduction and Cell Annotation for Large and High-dimensional Single-Cell Datasets

Install

pip install scBiMapping

note1: the current version (v0.0.8) on PyPI has been compiled in python 3.11, and python 3.11 is necessary for using this package.

note 2: if you are a BGIer, you can directly use the public image (named scBiMapping) on the cloud platform.

How to use

There are two major functions in scBiMapping, scBiMapping_DR and scBiMapping_annotation, corresponding to the following two tasks.

Task 1: Dimension reduction

scBiMapping_DR(adata,n_embedding):

• Input:

  • adata: anndata format (cell-feature sparse matrix is stored in adata.X);

  • n_embedding: an integer, denoting embedding dimensions (default to 30; slight adjustment may lead to better performance in practice);

• Output:

  • Embedded matrix is stored in adata.obsm['U'], where each row denotes the embedded vector of one cell;

Task 2: reference-based cell type annotation

scBiMapping_annotation(adata_ref,adata_query,n_embedding, K, K_majority, CellType_Key_for_ref)

• Key Inputs:

  • adata_ref: referenc dataset (anndata format);

  • adata_query: query dataset (anndata format); Note: the feature set of reference and query datasets should be the same, by using the following setttings for instance

    • intersection_feature = list(set(adata_ref.var_names) & set(adata_query.var_names))
    • adata_ref = adata_ref[:,intersection_feature]
    • adata_query = adata_query[:,intersection_feature]

  • n_embedding: an integer, denoting the number of embeddings (default to 30; slight adjustment may lead to better performance in practice);

  • K: an integer, denoting how many features are used as the new vector representation of each cell in the embedding (default to 30; adjustment may be needed in practice);

  • K_majority: an integer, denoting how many reference cells are used for majority voting (default to 10; adjustment may be needed in practice);

  • CellType_Key_for_ref: key in adata_ref.obs that stores the cell type labels of the reference cells (IMPORTANT!!!);

• Output:

  • the predicted cell types for all query cells are stored in adata_query.obs['cell_type_predicted']

Tutorials for tasks 1 and 2

We provide several demos to further demonstrate how to conduct dimension reduction and reference-based cell type annotation using scBiMapping; Details can be found in the Tutorials folder and https://nbviewer.org/github/scBGI/scBiMapping/tree/main/Turtorials.

Scripts to reproduce primary experimental results

See the corresponding files in this github. See also reproducible program in codeOcean: https://codeocean.com/capsule/3904732/tree.