Scotia is a Python package for inferring ligand–receptor interactions from spatial imaging data.
This package requires Python >=3.6. Before installing scotia, it is highly recommended to create a new environment using conda.
conda create -n scotia_env python=3.9
conda activate scotia_env
In this new conda environment, you can git clone this repository to your workspace, and then install it with
python setup.py install
After installation, you can test it by
python
import scotia
Check out this notebook for a quick start tutorial. The example data used in the tutorial were included in the example folder.
scotia.dbscan_ff_cell(X_pos)
Required inputs:
X_pos: cell coordinates array.
Key parameters:
-min_cluster_size: the minimum number of cells in the clusters identified by DBSCAN, default: 10.
-eps_l: the maximum distance between neighboring cells within each cluster, default: range(15,150,5).
scotia.sel_pot_inter_cluster_pairs(dis_arr,cluster_cell_df)
Required inputs:
dis_arr: cell by cell spatial distance array.
cluster_cell_df: cell clustering results.
scotia.source_target_ot(filtered_dis_arr, exp_df, meta_df, known_lr_pairs)
Required inputs:
dis_arr: cell by cell spatial distance array (get from function sel_pot_inter_cluster_pairs, Inf for excluded cluster pairs).
exp_df: gene expression dataframe.
meta_df: metadata, including annotation information.
known_lr_pairs: ligand-receptor pairs.
scotia.post_ot(ot_data_df, label)
Required inputs:
ot_data_df: cell-cell interaction scores from OT analysis.
label: sample_id for output.
scotia.permutation_test(X_pos)
Required inputs:
X_pos: cell coordinates array.
Key parameters:
-it_n: the total number of permutations.