cellbin2: A framework for generating single-cell gene expression data

Introduction

CellBin introduction (Chinese)

Tweets
Stereo-seq CellBin introduction (Chinese)
Stereo-seq CellBin database introduction (Chinese)
Stereo-seq CellBin cell segmentation intro (Chinese)

Paper related
CellBin: a highly accurate single-cell gene expression processing pipeline for high-resolution spatial transcriptomics (GitHub Link)
CellBinDB: A Large-Scale Multimodal Annotated Dataset for Cell Segmentation with Benchmarking of Universal Models (GitHub Link)
Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images (GitHub Link)

Video tutorial
Cell segmentation tool selection and application (Chinese)
One-stop solution for spatial single-cell data acquisition (Chinese)
Single-cell processing framework for high resolution spatial omics (Chinese)

Installation

Linux

git clone https://github.com/STOmics/cellbin2
conda create --name cellbin2 python=3.8
cd cellbin2
python install.py 12  # if your cuda version is 12.x
python install.py 11  # if your cuda version is 11.x
python demo.py  # run 3 demos, approximately 30-40 mins on GPU

Tutorials

KIT_VERSIONS = (
    'Stereo-seq T FF V1.2',
    'Stereo-seq T FF V1.3',
    'Stereo-CITE T FF V1.0',
    'Stereo-CITE T FF V1.1',
    'Stereo-seq N FFPE V1.0',
)

Each product line has the configurations of the product and R&D versions. You can visit config.md to view the detailed configurations.

Official product

case 1: Stereo-seq T FF DAPI + mIF

CUDA_VISIBLE_DEVICES=0 python cellbin2/cellbin_pipeline.py \
-c SS200000045_M5 \
-i /media/Data/dzh/data/cellbin2/demo_data/SS200000045_M5/SS200000045_M5_fov_stitched.tif \
-s DAPI \
-if /media/Data/dzh/data/cellbin2/demo_data/SS200000045_M5/SS200000045_M5_ATP_IF_fov_stitched.tif,/media/Data/dzh/data/cellbin2/demo_data/SS200000045_M5/SS200000045_M5_CD31_IF_fov_stitched.tif,/media/Data/dzh/data/cellbin2/demo_data/SS200000045_M5/SS200000045_M5_NeuN_IF_fov_stitched.tif \
-m /media/Data/dzh/data/cellbin2/demo_data/SS200000045_M5/SS200000045_M5.raw.gef \
-o /media/Data/dzh/data/cellbin2/test/SS200000045_M5_11 \
-k "Stereo-seq T FF V1.2"

case 2: Stereo-seq T FF ssDNA

CUDA_VISIBLE_DEVICES=0 python cellbin2/cellbin_pipeline.py \
-c SS200000135TL_D1 \
-i /media/Data/dzh/data/cellbin2/demo_data/product_demo/SS200000135TL_D1/SS200000135TL_D1.tif \
-s ssDNA \
-m /media/Data/dzh/data/cellbin2/demo_data/product_demo/SS200000135TL_D1/SS200000135TL_D1.raw.gef \
-o /media/Data/dzh/data/cellbin2/test/SS200000135TL_D1 \
-k "Stereo-seq T FF V1.2"

case 3: Stereo-seq T FF H&E

CUDA_VISIBLE_DEVICES=0 python cellbin2/cellbin_pipeline.py \
-c C04042E3 \
-i /media/Data/dzh/data/cellbin2/demo_data/product_demo/C04042E3/C04042E3.tif \
-s HE \
-m /media/Data/dzh/data/cellbin2/demo_data/product_demo/C04042E3/C04042E3.raw.gef \
-o /media/Data/dzh/data/cellbin2/test/C04042E3 \
-k "Stereo-seq T FF V1.2"

Research mode

case 1: Stereo-CITE DAPI + IF + trans gef

CUDA_VISIBLE_DEVICES=0 python cellbin2/cellbin_pipeline.py \
-c A02677B5 \
-i /media/Data/dzh/data/cellbin2/demo_data/product_demo/A02677B5/A02677B5.tif \
-s DAPI \
-if /media/Data/dzh/data/cellbin2/demo_data/product_demo/A02677B5/A02677B5_IF.tif \
-m /media/Data/dzh/data/cellbin2/demo_data/product_demo/A02677B5/A02677B5.raw.gef \
-o /media/Data/dzh/data/cellbin2/test/A02677B5 \
-k "Stereo-CITE T FF V1.1 R"

case 2: Stereo-CITE DAPI + protein gef

CUDA_VISIBLE_DEVICES=0 python cellbin2/cellbin_pipeline.py \
-c A03899A4 \
-i /media/Data/dzh/data/cellbin2/data/A03899A4/A03899A4_fov_stitched.tif \
-s DAPI \
-pr /media/Data/dzh/data/cellbin2/data/A03899A4/A03899A4.protein.tissue.gef \
-o /media/Data/dzh/data/cellbin2/test/A03899A4 \
-k "Stereo-CITE T FF V1.1 R"

case 3: Stereo-CITE DAPI + IF + trans gef + protein gef

CUDA_VISIBLE_DEVICES=0 python cellbin2/cellbin_pipeline.py \
-c A03599D1 \ # chip number
-i /media/Data/A03599D1_DAPI_fov_stitched.tif \  # ssDNA, DAPI, HE data path
-if /media/Data/A03599D1_IF_fov_stitched.tif \  # IF data path
-s DAPI \  # stain type，(ssDNA, DAPI, HE)
-m /media/Data/A03599D1.raw.gef \  # Transcriptomics gef path
-pr /media/Data/A03599D1.protein.raw.gef \  # protein gef path
-o /media/Data/C04042E3_demo \ # output dir
-k "Stereo-CITE T FF V1.1 R"

more examples, please visit example.md

ErrorCode

refer to error.md

Outputs

File Name	Description
A03599D1_cell_mask.tif	Final cell mask
A03599D1_mask.tif	Final nuclear mask
A03599D1_tissue_mask.tif	Final tissue mask
A03599D1_params.json	CellBin 2.0 input params
A03599D1.ipr	Image processing record
metrics.json	CellBin 2.0 Metrics
CellBin_0.0.1_report.html	CellBin 2.0 report
A03599D1.rpi	Recorded image processing (for visualization)
A03599D1_DAPI_mask.tif	Cell mask on registered image
A03599D1_DAPI_regist.tif	Registered image
A03599D1_DAPI_tissue_cut.tif	Tissue mask on registered image
A03599D1_IF_mask.tif	Cell mask on registered image
A03599D1_IF_regist.tif	Registered image
A03599D1_IF_tissue_cut.tif	Tissue mask on registered image

Other content

Reference

https://github.com/STOmics/CellBin
https://github.com/MouseLand/cellpose
https://github.com/matejak/imreg_dft
https://github.com/rezazad68/BCDU-Net
https://github.com/libvips/pyvips
https://github.com/vanvalenlab/deepcell-tf
https://github.com/ultralytics/ultralytics