Fig. 1

Visualization of the workflow, demonstrating a step-by-step explanation for a sc-SynO analysis. a Several or one snRNA-Seq or scRNA-Seq fastq datasets can be used as an input. Here, we identify our cell population of interest and provide raw or normalized read counts of this specific population. This can be done with any single-cell analysis workflow, e.g., Seurat. b Further information are extracted for cluster annotation that serve as improved input for the subsequent training with sc-SynO. c Based on the data input, we utilize the underlying LoRAS [8] synthetic oversampling algorithm of sc-SynO to generate new cells around the former origin of cells to increase the size of the minority sample. d The trained Machine Learning classifier is validated on the trained, pre-annotated dataset to evaluate the performance metrics of the actual model. The sc-SynO model is now ready to identify the learned rare-cell type in novel data. This figure was solely created by the authors