Joye9285 / scGAC

single-cell graph attentional clustering

scGAC

single-cell graph attentional clustering

Requirements

Python --- 3.6.4

Tensorflow --- 1.12.0

Keras --- 2.1.0

Numpy --- 1.19.5

Scipy --- 1.5.4

Pandas --- 1.1.5

Sklearn --- 0.24.2

Usage

Inputs

All the original tested datasets (Yan, Biase, Klein, Romanov, Muraro, Björklund, PBMC, Zhang, Guo, Brown.1, Brown.2, Chung, Sun.1, Sun.2, Sun.3 and Habib) can be downloaded.

For example, the original expression matrix ori_data.tsv of dataset Biase is downloaded and put into /data/Biase. Before clustering, low-quality cells and genes can be filtered by running the following command:

python preprocess.py Biase

And a pre-processed expression matrix data.tsv is produced under /data/Biase.

Run scGAC

To use scGAC, you should specify the two parameters, dataset_str and n_clusters, and run the following command:

python scGAC.py dataset_str n_clusters

where dataset_str is the name of dataset and n_clusters is the number of clusters.

For example, for dataset Biase, you can run the following command:

python scGAC.py Biase 3

For your own dataset named Dataset_X, you can first create a new folder under /data, and put the expression matrix file data.tsv into /data/Dataset_X, then run scGAC on it.

Please note that we recommend you use the raw count expression matrix as the input of scGAC.

Outputs

You can obtain the predicted clustering result pred_DatasetX.txt and the learned cell embeddings hidden_DatasetX.tsv under the folder /result.

Optional parameters

To see the optional parameters, you can run the following command:

python scGAC.py -h

For example, if you want to evaluate the clustering results (by specifing --subtype_path) and change the number of nearest neighbors (by specifing --k), you can run the following command:

python scGAC.py Biase 3 --subtype_path data/Biase/subtype.ann --k 4

Results in the paper were obtained with default parameters.