This repository contains an available tool for ClusterMap for multi-scale clustering analysis of spatial gene expression, and ClusterMap examples of the 3D STARmap human cardiac organoid dataset, 2D STARmap mouse brain V1 dataset, and 3D STARmap mouse brain V1 dataset.
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Original scripts for generating data at ClusterMap paper are at: https://codeocean.com/capsule/9820099/tree/v1.
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STARmap placenta dataset can be accessed at here.
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Raw fluorescent imaging data can be accessed at here.
git clone https://github.com/wanglab-broad/ClusterMap.git
cd ClusterMap
pip install -e.
For the environment, to use .yml file:
conda env create -f environment.yml. This will create an environment named ClusterMap with required packages.
- We are currently uploading more files for packaging and testing and will finish update soon.
- Read a more detailed tutorial here.
Input data
spots: data matrix of mRNA spots with 2D/3D physical location and gene identity information (pandas dataframe)- Example
| Index | spot_location_1 | spot_location_2 | spot_location_3 | gene | Optional other info: gene_name |
|---|---|---|---|---|---|
| 0 | 105 | 239 | 1 | 1 | Syndig1l |
| 1 | 110 | 243 | 1 | 1 | Syndig1l |
| 2 | 115 | 178 | 1 | 2 | Acot13 |
dapi: a 2D/3D image corronsponding tospots
Input Parameters
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xy_radius: estimation of radius of cells in x-y plane -
z_radius: estimation of radius of cells in z axis; 0 if data is 2D. -
cell_num_threshold: a threshold for deciding the number of cells. A larger value gives more cells; Default: 0.1. -
dapi_grid_interval: sample interval in DAPI image. A large value will consume more computation resources and give more accurate results (most of the time). Default: 3.
Output parameters
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model.cellid_unique: unique cell id values -
model.cellcenter_unique: cell centers in order ofmodel.cellid_unique
If you want to generate mask from points, refer to the notebook here.
- Example file at
ClusterMap_STARmap_human_cardiac_organoid.ipynb
- Example file at
ClusterMap_STARmap_V1_1020.ipynb
- Time is dependent on the number of input spots, and potentially the area the DAPI foreground. Currently testing on several samples:
- 1mins 42s for 49,712 input spots (all 273,242 spots) without GPU, single thread
- 34mins 53s for 471,295 input spots without GPU, single thread
If you find ClusterMap useful for your work, please cite our paper:
He, Y., Tang, X., Huang, J. et al. ClusterMap for multi-scale clustering analysis of spatial gene expression. Nat Commun 12, 5909 (2021). https://doi.org/10.1038/s41467-021-26044-x
Contact us at yichunhe00@gmail.com if you have any issues.
Yichun He, Emma Bou Hanna, Jiahao Huang, Xin Tang, Haowen Zhou