A Tutorial on the Mathematical Model of Single Cell Variational Inference

TL;DR

This tutorial explains the mathematical foundation of single cell variational inference (scVI), a neural network-based model using variational auto-encoders to analyze large-scale sequencing data for biological insights.

Contribution

It provides a clear, beginner-friendly introduction to the mathematical model of scVI, encouraging more researchers to adopt this approach.

Findings

Demonstrates how scVI models single-cell sequencing data effectively.

Provides detailed derivations to facilitate understanding of the variational auto-encoder in this context.

Encourages broader adoption of neural network models in single-cell data analysis.

Abstract

As the large amount of sequencing data accumulated in past decades and it is still accumulating, we need to handle the more and more sequencing data. As the fast development of the computing technologies, we now can handle a large amount of data by a reasonable of time using the neural network based model. This tutorial will introduce the the mathematical model of the single cell variational inference (scVI), which use the variational auto-encoder (building on the neural networks) to learn the distribution of the data to gain insights. It was written for beginners in the simple and intuitive way with many deduction details to encourage more researchers into this field.