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MapBatch: Conservative Batch Normalization for scRNAseq Enables Discovery of Rare Cell Populations
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What is MapBatch for scRNAseq?

MapBatch is a tool designed for conservative batch normalization of single cell RNA sequencing (scRNAseq) data. It helps in identifying rare cell populations by maintaining the biological signal necessary for such discoveries.

Here’s how it works:

  • Autoencoders: MapBatch uses autoencoders trained on individual samples to learn the underlying gene expression structure without batch effects.
  • Ensemble Model: It combines multiple autoencoders, each trained on a single sample, to incorporate multiple samples into the training process.
  • Biological Signal Preservation: By focusing on preserving the biological differences between cells, MapBatch reduces batch effects while maintaining the essential biological signals.

This approach is particularly useful in cancer research, where detecting rare cell populations can be crucial for understanding the disease.

In this webinar, you’ll learn about:

  • Autoencoders for batch normalization
  • Benchmark of MapBatch with other tools
  • Case study of successful identification of rare cell populations

About the speaker

Dr. Chern Han Yong

Chern completed an undergraduate honors thesis on “Cooperative Coevolution of Multi-Agent Systems.” He went on to get an MS in Computer Sciences at the University of Texas at Austin, and then to the PhD program in Computational Biology at the National University of Singapore.

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