scmFormer

Introduction

scmFormer(single-cell multi-modal/multi-task transformer), a Transformer-based model, can be used to integrate and generate single-cell omics data.

Instructions and examples are provided in the following tutorials.

Requirement

Python 3.9.12
PyTorch >= 1.5.0
numpy
pandas
scipy
sklearn
Scanpy
random

Input file

the first  modality(scRNA-seq)  dataset.
the second modality(scATAC-seq)  dataset.

Output file

After the scmFormer model, the model will be save at: "log/scmFormer.tar".
The latent representations for each modality  are saved in the log/mod1.npy,log/mod2.npy

Tutorial

processsed Data

The scRNA-seq datasets pre-processing code

Paper Link