scMEDAL for the interpretable analysis of single-cell transcriptomics data with batch effect visualization using a deep mixed effects autoencoder

arXiv:2411.06635 · cs.LG, q-bio.GN · Submitted 2024-11-11 · Read on arXiv

cs.LG, q-bio.GN

Submitted: 2024-11-11

Updated: 2025-11-05

Terminology

• Latent Space — 64× in this paper · explained in 7 episodes
• Clustering — 38× in this paper · explained in 2 episodes
• Accuracy — 37× in this paper · explained in 1 episode
• Embeddings — 27× in this paper · explained in 2 episodes
• Latent Representation — 15× in this paper · explained in 2 episodes
• Loss Function — 13× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Autism Spectrum Disorder — 6× in this paper
• Average Silhouette Width — 5× in this paper
• Regularization — 5× in this paper · explained in 3 episodes
• Acute Myeloid Leukemia — 4× in this paper
• Benchmarking — 4× in this paper · explained in 3 episodes
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Latent Representations — 3× in this paper · explained in 3 episodes
• Principal Component Analysis — 3× in this paper
• Uniform Manifold Approximation and Projection — 3× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Explainability — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• APEX — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Abundance — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Alignment Methods — 1× in this paper · explained in 1 episode
• Autoencoder-based Batch Correction — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Unique Molecular Identifiers — 1× in this paper
• Unlike the Calinski-Harabasz — 1× in this paper
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

Sources

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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