CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells

Single-cell sequencing provides transcriptomic profiling at single-cell resolution, uncovering cellular heterogeneity with unprecedented precision. Yet, current single cell data analysis suffers from the inherent data noises, batch effects, and sparsity, highlighting the requirement of a unified model to represent cellular states. To circumvent this problem, many recent efforts focus on training single-cell foundation models based on large datasets. However, current human foundation models are still limited by the sizes of training data and model parameters. Here, we have collected a diverse dataset of 100 million human cells, on which we train a single-cell foundation model (CellFM) containing 800 million…

• NS
  National Science Foundation
• NN
  National Natural Science Foundation of China

  Awards: T2394502, 62402071
• CP
  China Postdoctoral Science Foundation

  Award: 2024M763866
• GO
  Government of Jiangsu Province
• FR
  Fundamental Research Funds for the Central Universities

  Award: 2024IAIS-QN020