Fig. 4: GeneCompass demonstrates enhanced performance for GRN inference, drug dose response prediction, gene expression profile prediction, and gene dosage sensitivity prediction tasks.

a The workflow of integrating gene embeddings generated from GeneCompass to four downstream tasks: GRN inference, drug dose response prediction, gene expression profiling and gene dosage sensitivity prediction. b Performance comparison of each model on the GRN inference task in terms of AUPRC. The red line denotes the results of GeneCompass trained by different amounts of data. The blue, orange and brown dots represent results of DeepSEM, scGPT and Geneformer, respectively. c Performance comparison, in terms of R-squared value, for each model is conducted on the drug dose response prediction task. The red line denotes the results of GeneCompass trained by different amounts of data. The green and blue dots represent results of scGPT and Geneformer, respectively. d Performance comparison of each model on the gene expression profile prediction task. Root Mean Squared Error is applied as the metric. The red line denotes the results of GeneCompass trained by different amounts of data. The blue, green and brown dots represent results of DeepCE, scGPT and Geneformer, respectively. e Performance comparison of each model on the dosage sensitivity prediction task. We use AUC as the metric. The red and blue lines denote the results of GeneCompass and Geneformer, respectively, trained by different amounts of data. The dashed line represents the result of GeneCompass without pretraining.