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0 reviewsSingle-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, batcheffects, and sparsity, highlighting the requirement of a unified model torepresent cellular states. To circumvent this problem, many recent effortsfocus on training single-cell foundation models based on large datasets.However, current human foundation models are still limited by the sizes oftraining data and model parameters. Here, we have collected a diverse datasetof 100 million human cells, on which we train a single-cell foundation model(CellFM) containing 800 million parameters. To balance efficiency and performance, the model is trained through a modified RetNet framework on theMindSpore. Extensive experiments have shown that CellFM outperformsexisting models in cell annotation, perturbation prediction, gene functionprediction, and gene-gene relationship capturing.
