Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network
Python 3.6.9
PyTorch 1.1.0
The datasets we used in this study can be available at https://hemberg-lab.github.io/scRNA.seq.datasets/
For the simulated datasets, we normalized them using transcripts per million (TPM) method and then scaled the value of each gene to [0, 1]. For real datasets, we employed the procedure suggested by Seurat3.0 to normalize and select top 2000 highly variable genes for scRNA-seq data, then to scale the value of each gene to [0,1]. Note that for real datasets normalized by FPKM, we first converted them to TPM.
python GraphSCC.py --name [goolam|baron_mouse]