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from pathlib import Path
from typing import Optional, List
from mcp.server.fastmcp import FastMCP
SERVER_NAME = 'local_bin2cell'
mcp = FastMCP(SERVER_NAME)
@mcp.tool()
def bin2cell_prepare_bins(
input_dir: str,
output_h5ad: str,
bin_size: int = 2,
destripe: bool = True,
):
"""
Reads Visium HD data from a SpaceRanger output directory and optionally performs destriping
to correct for technical effects in 2um bin data.
Args:
input_dir: Path to the SpaceRanger output directory (containing 'outs' folder).
output_h5ad: Path where the processed bin-level AnnData object will be saved.
bin_size: Resolution of bins to load (default is 2um).
destripe: Whether to apply the destriping correction for variable bin dimensions.
"""
# Input validation
input_path = Path(input_dir)
if not input_path.exists():
return {"error": f"Input directory {input_dir} does not exist."}
output_path = Path(output_h5ad)
if not output_path.parent.exists():
output_path.parent.mkdir(parents=True, exist_ok=True)
if bin_size <= 0:
return {"error": "bin_size must be a positive integer."}
# Construct Python command
# We use a python script string to execute the library functions
destripe_cmd = "b2c.pp.destripe(adata)" if destripe else "pass"
python_script = f"""
import bin2cell as b2c
import scanpy as sc
import os
try:
# Load Visium HD data
adata = b2c.pp.read_visium_hd_folder('{input_dir}', bin_size={bin_size})
# Perform destriping if requested
if {destripe}:
b2c.pp.destripe(adata)
# Save the result
adata.write('{output_h5ad}')
print("Successfully prepared bin data.")
except Exception as e:
print(f"Error: {{str(e)}}")
exit(1)
"""
try:
result = subprocess.run(
["python", "-c", python_script],
capture_output=True,
text=True,
check=True
)
return {
"command_executed": f"bin2cell.pp.read_visium_hd_folder and destripe={destripe}",
"stdout": result.stdout,
"stderr": result.stderr,
"output_files": [output_h5ad]
}
except subprocess.CalledProcessError as e:
return {
"error": "Failed to prepare bin data",
"stdout": e.stdout,
"stderr": e.stderr,
"command_executed": e.cmd
}
@mcp.tool()
def bin2cell_run_stardist(
image_path: str,
output_mask_path: str,
model_name: str = "2D_versatile_he",
prob_thresh: float = 0.5,
nms_thresh: float = 0.3,
):
"""
Performs cell segmentation on a morphology image using StarDist.
Args:
image_path: Path to the high-resolution morphology image (e.g., tissue_hires_image.png).
output_mask_path: Path where the resulting segmentation mask (.tif) will be saved.
model_name: StarDist model to use (default: '2D_versatile_he').
prob_thresh: Probability threshold for StarDist detection.
nms_thresh: Non-maximum suppression threshold for StarDist.
"""
# Input validation
img_path = Path(image_path)
if not img_path.exists():
return {"error": f"Image file {image_path} does not exist."}
out_mask = Path(output_mask_path)
if not out_mask.parent.exists():
out_mask.parent.mkdir(parents=True, exist_ok=True)
python_script = f"""
import bin2cell as b2c
import cv2
import numpy as np
try:
# Run StarDist segmentation via bin2cell wrapper
# Note: bin2cell.tl.stardist handles the model loading and prediction
b2c.tl.stardist(
'{image_path}',
'{output_mask_path}',
model='{model_name}',
prob_thresh={prob_thresh},
nms_thresh={nms_thresh}
)
print("Successfully generated segmentation mask.")
except Exception as e:
print(f"Error: {{str(e)}}")
exit(1)
"""
try:
result = subprocess.run(
["python", "-c", python_script],
capture_output=True,
text=True,
check=True
)
return {
"command_executed": f"bin2cell.tl.stardist on {image_path}",
"stdout": result.stdout,
"stderr": result.stderr,
"output_files": [output_mask_path]
}
except subprocess.CalledProcessError as e:
return {
"error": "StarDist segmentation failed",
"stdout": e.stdout,
"stderr": e.stderr,
"command_executed": e.cmd
}
@mcp.tool()
def bin2cell_extract_cells(
bin_h5ad_path: str,
mask_path: str,
output_cell_h5ad_path: str,
qc_metrics: bool = True,
):
"""
Groups subcellular bins into cells based on a segmentation mask and generates a cell-level AnnData object.
Args:
bin_h5ad_path: Path to the bin-level AnnData object (output from bin2cell_prepare_bins).
mask_path: Path to the segmentation mask file (output from bin2cell_run_stardist).
output_cell_h5ad_path: Path where the final cell-level AnnData object will be saved.
qc_metrics: Whether to calculate standard scanpy QC metrics for the new cell object.
"""
# Input validation
bin_path = Path(bin_h5ad_path)
if not bin_path.exists():
return {"error": f"Bin h5ad file {bin_h5ad_path} does not exist."}
m_path = Path(mask_path)
if not m_path.exists():
return {"error": f"Mask file {mask_path} does not exist."}
out_cell_path = Path(output_cell_h5ad_path)
if not out_cell_path.parent.exists():
out_cell_path.parent.mkdir(parents=True, exist_ok=True)
python_script = f"""
import bin2cell as b2c
import scanpy as sc
try:
# Load the bin-level data
adata_bins = sc.read_h5ad('{bin_h5ad_path}')
# Extract cells based on the mask
adata_cells = b2c.tl.extract_cells(adata_bins, '{mask_path}')
# Calculate QC metrics if requested
if {qc_metrics}:
sc.pp.calculate_qc_metrics(adata_cells, inplace=True)
# Save the cell-level object
adata_cells.write('{output_cell_h5ad_path}')
print("Successfully extracted cells from bins.")
except Exception as e:
print(f"Error: {{str(e)}}")
exit(1)
"""
try:
result = subprocess.run(
["python", "-c", python_script],
capture_output=True,
text=True,
check=True
)
return {
"command_executed": f"bin2cell.tl.extract_cells using mask {mask_path}",
"stdout": result.stdout,
"stderr": result.stderr,
"output_files": [output_cell_h5ad_path]
}
except subprocess.CalledProcessError as e:
return {
"error": "Cell extraction failed",
"stdout": e.stdout,
"stderr": e.stderr,
"command_executed": e.cmd
}
@mcp.tool()
def bin2cell_visualize_segmentation(
bin_h5ad_path: str,
output_image_path: str,
basis: str = "spatial",
):
"""
Generates a visualization of the bin-to-cell assignments.
Args:
bin_h5ad_path: Path to the bin-level AnnData object containing cell assignments.
output_image_path: Path to save the visualization plot (e.g., .png or .pdf).
basis: The coordinate system to use for plotting (default: 'spatial').
"""
bin_path = Path(bin_h5ad_path)
if not bin_path.exists():
return {"error": f"Bin h5ad file {bin_h5ad_path} does not exist."}
python_script = f"""
import scanpy as sc
import matplotlib.pyplot as plt
import bin2cell as b2c
try:
adata = sc.read_h5ad('{bin_h5ad_path}')
if 'cell_id' not in adata.obs.columns:
print("Error: cell_id not found in adata.obs. Run extract_cells first.")
exit(1)
# Plotting logic
sc.pl.embedding(adata, basis='{basis}', color='cell_id', show=False)
plt.savefig('{output_image_path}')
print("Successfully saved visualization.")
except Exception as e:
print(f"Error: {{str(e)}}")
exit(1)
"""
try:
result = subprocess.run(
["python", "-c", python_script],
capture_output=True,
text=True,
check=True
)
return {
"command_executed": f"Visualization of cell_id on {basis}",
"stdout": result.stdout,
"stderr": result.stderr,
"output_files": [output_image_path]
}
except subprocess.CalledProcessError as e:
return {
"error": "Visualization failed",
"stdout": e.stdout,
"stderr": e.stderr,
"command_executed": e.cmd
}
if __name__ == "__main__":
mcp.run(transport="stdio")
|