MapCell: Methods to identify cell types automatically including deep learningWatch nowMapCell is a method used in the field of single-cell RNA sequencing (scRNA-seq) to classify cell types and identify similar cells across different experiments. It leverages Siamese Neural Networks (SNNs) to learn a distance metric that can differentiate between cell types. This approach allows for the transfer of cell type annotations from labeled datasets to unlabeled ones and helps in discovering previously unseen cell types.Key Features of MapCell:Few-shot Training: Requires only a small training set to learn the distance metric.Cross-platform Compatibility: Can transfer annotations across different scRNA-seq platforms, batches, and even species.Novel Cell Type Discovery: Aids in identifying new cell types that were not previously annotated.MapCell is particularly useful in the context of large and diverse collections of single-cell data, where traditional clustering methods might be time-consuming and subjective.In this webinar, you will learn about:– Basics of metric learning based neural networks – Performance and benchmarking of MapCell – Case study: Applying MapCell approach in identifying rare cell typesIf you have any specific questions or need more details, feel free to reach out here.Check out our latest webinars Learn more From Epigenome Atlases to Gene Regulatory Codes - by Prof. Bing Ren (UCSD) Read more Single-Cell Decoding of Cellular Lineages and the Evolution of Plant Exceptional Traits Read more In Vitro Modeling of Parkinson’s Disease with Patient Specific Midbrain Organoids Read more