pyBCS

This is a python library to create a BioTuring Compressed Study (bcs) file from an AnnData (scanpy) object.

bcs files can be imported directly into BBrowser, a software for single-cell data.

Visit our github for more detail.

Installation

pip install pyBCS-bioturing

Example

Scanpy

from pyBCS import scanpy2bcs
scanpy2bcs.format_data("/mnt/example/data.h5ad", "/mnt/example/data.bcs",
                        input_format="h5ad", graph_based="louvain")

If your data has antibody-derived tags (ADT), you can put ADT expression data in the obs as cell metadata with a distinguishable suffix. For example, ADT expression of CD45 will be CD45_TotalSeqC. In such cases, you can declare cite_seq_suffix when using format_data():

scanpy2bcs.format_data("/mnt/example/data.h5ad", "/mnt/example/data.bcs",
                       input_format="h5ad", graph_based="louvain", cite_seq_suffix="_TotalSeqC")

SPRING

from pyBCS import scanpy2bcs
scanpy2bcs.format_data("/mnt/example/spring_study", "/mnt/example/data.bcs",
                        input_format="spring",
                        graph_based="louvain")

Loom

from pyBCS import scanpy2bcs
scanpy2bcs.format_data("/mnt/example/data.loom", "/mnt/example/data.bcs",
                        input_format="loom",
                        barcode_name="CellID",
                        feature_name="Gene",
                        dimred_keys={"tsne":["tsne1", "tsne2"]})

Abloom

from pyBCS import scanpy2bcs
scanpy2bcs.format_data("/mnt/example/data.loom", "/mnt/example/data.bcs",
                        input_format="abloom",
                        barcode_name="observation_id",
                        feature_name="accession_id",
                        graph_based="cluster")