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sc-autoencoder

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An autoencoder for single cell data.

This project uses an autoencoder model to learn latent features from single-cell RNA-seq (scRNA-seq) data. Autoencoder models and similar architectures are frequently used for scRNA-seq data. For instance, Eraslan et al. used an autoencoder for denoising of single cell data. In another study, Lotfollahi et al use a variational autoencoder to predict perturbation responses. Here, we have implemented a very simple autoencoder to demonstrate how non-deterministic operations can lead to significant differences in latent space embeddings which affect downstream analysis and hinder reproducibility.

Architecture

The model used in this project follows a standard encoder-encoding-decoder autoencoder architecture. We use layer sizes of 256, 128 and 64 for the encoder and decoder (in reverse) layers, and a encoding size of 32.

Autoencoder architecture

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This package was created with mlf-core using Cookiecutter.

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An autoencoder for single cell data.

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