BOMS is a tool for cell segmentation in imaging-based Spatial Transcriptomics datasets. It takes as input the gene locations and labels. It assumes that a cell body is homogenous in its transcriptional signature and uses the similarity of these neighborhoods to cluster them together as one cell. The method can also incorporate the flows obtained from Cellpose Segmentation on DAPI/Cell Membrane channels to improve its cell segmentation.
The package requires Python > 3.9. The package can be installed using pip as follows:
pip install bomsThe data for the method is provided in the form of three numpy arrays : x representing the x coordinates of the mRNA spots, y representing the y coordinates of the mRNA spots and g representing the labels of the mRNA spots. The cell segmentation can be performed as follows:
from boms import run_boms
"""
:param epochs: Number of iterations for the BOMS algorithm. Recommendation: 30
:param h_s: Spatial Bandwidth. Recommendation: Roughly equal to the radius of the cell body.
:param h_r: Range Bandwidth. Recommendation: 0.3 - 0.5
:param K: Number of Nearest Neighbors to form the Neighborhood Gene Expression Profile. Recommendation: 30
:return modes: N x (2 + no. of genes) array containing the final modes.
:return seg: N x 1 array containing the final segmentation.
:return count_mat: (no. of cells) x (no. of genes) array containing the gene expression counts for each cell.
:return cell_loc: (no. of cells) x 2 array containing the locations of the cells.
:return coords: N x 2 array containing the locations of the mRNA spots for which the modes have been calculated.
In case no fov is specified, this is the same as the input x and y.
"""
modes, seg, count_mat, cell_loc, coords = run_boms(x, y, g, epochs=30, h_s=10, h_r=0.3, K=30)A demo notebook is available to run on Google Colab - [BOMS Demo](https://colab.research.google.com/drive/16YgR92sc3ai9mheYUb8_SCdo9hjc3-xZ?usp=sharing