Schematic overview of sc-SynO
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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 to sc-SynO for training. b) Further information for cluster annotation and processed count data are serving as input for the core algorithm. c) Based on the data input, we utilize the LoRAS synthetic oversampling algorithms to generate new cells around the former origin of cells to increase the size of the minority sample. The trained Machine Learning classifier is validated on the trained, pre-annotated dataset to evaluate the performance metrics of the actual model. The model is now ready to identify the learned rare-cell type in novel data.

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Created: 16th Oct 2020 at 13:24

Last updated: 21st Jan 2021 at 20:39

Last used: 28th Nov 2022 at 16:03

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Version 2 (latest) Created 20th Jan 2021 at 21:04 by Markus Wolfien

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