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Accurate and fast cell marker gene identification with COSG

Overview

COSG is a cosine similarity-based method for more accurate and scalable marker gene identification.

• COSG is a general method for cell marker gene identification across different data modalities, e.g., scRNA-seq, scATAC-seq and spatially resolved transcriptome data.
• Marker genes or genomic regions identified by COSG are more indicative and with greater cell-type specificity.
• COSG is ultrafast for large-scale datasets, and is capable of identifying marker genes for one million cells in less than two minutes.

The method and benchmarking results are described in Dai et al., (2022)_.

Documentation

The documentation for COSG is available here <https://cosg.readthedocs.io/en/latest/>_.

Tutorial

The COSG tutorial <https://nbviewer.jupyter.org/github/genecell/COSG/blob/main/tutorials/COSG-tutorial.ipynb>_ provides a quick-start guide for using COSG and demonstrates the superior performance of COSG as compared with other methods, and the Jupyter notebook <https://github.com/genecell/COSG/blob/main/tutorials/COSG-tutorial.ipynb>_ is also available.

Question

For questions about the code and tutorial, please contact Min Dai, [email protected].

Example

Run COSG:

.. code-block:: python

import cosg n_gene=30 groupby='CellTypes' cosg.cosg(adata, key_added='cosg', # use_raw=False, layer='log1p', ## e.g., if you want to use the log1p layer in adata mu=100, expressed_pct=0.1, remove_lowly_expressed=True, n_genes_user=100, groupby=groupby)

Draw the dot plot:

.. code-block:: python

sc.tl.dendrogram(adata,groupby=groupby,use_rep='X_pca') df_tmp=pd.DataFrame(adata.uns['cosg']['names'][:3,]).T df_tmp=df_tmp.reindex(adata.uns['dendrogram_'+groupby]['categories_ordered']) marker_genes_list={idx: list(row.values) for idx, row in df_tmp.iterrows()} marker_genes_list = {k: v for k, v in marker_genes_list.items() if not any(isinstance(x, float) for x in v)}

sc.pl.dotplot(adata, marker_genes_list, groupby=groupby,
dendrogram=True, swap_axes=False, standard_scale='var', cmap='Spectral_r')

Output the marker list as pandas dataframe:

.. code-block:: python

marker_gene=pd.DataFrame(adata.uns['cosg']['names']) marker_gene.head()

You could also check the COSG scores:

.. code-block:: python

marker_gene_scores=pd.DataFrame(adata.uns['cosg']['scores']) marker_gene_scores.head()

Citation

If COSG is useful for your research, please consider citing Dai et al., (2022)_.

.. _Dai et al., (2022): https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbab579/6511197?redirectedFrom=fulltext