siVAE: interpretable deep generative models for single-cell transcriptomes
Neural networks such as variational autoencoders (VAE) perform dimensionality reduction
for the visualization and analysis of genomic data, but are limited in their interpretability: it is …
for the visualization and analysis of genomic data, but are limited in their interpretability: it is …
VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
Deep learning architectures such as variational autoencoders have revolutionized the
analysis of transcriptomics data. However, the latent space of these variational …
analysis of transcriptomics data. However, the latent space of these variational …
MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks
Deep generative models such as variational autoencoders (VAEs) and generative
adversarial networks (GANs) generate and manipulate high-dimensional images. We …
adversarial networks (GANs) generate and manipulate high-dimensional images. We …
Interpretable generative deep learning: an illustration with single cell gene expression data
M Treppner, H Binder, M Hess - Human Genetics, 2022 - Springer
Deep generative models can learn the underlying structure, such as pathways or gene
programs, from omics data. We provide an introduction as well as an overview of such …
programs, from omics data. We provide an introduction as well as an overview of such …
De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc
R Li, X Yang - Genome Biology, 2022 - Springer
Based on a deep generative model of variational graph autoencoder (VGAE), we develop a
new method, DeepLinc (deep learning framework for Landscapes of Interacting Cells), for …
new method, DeepLinc (deep learning framework for Landscapes of Interacting Cells), for …
RVAgene: generative modeling of gene expression time series data
R Mitra, AL MacLean - Bioinformatics, 2021 - academic.oup.com
Motivation Methods to model dynamic changes in gene expression at a genome-wide level
are not currently sufficient for large (temporally rich or single-cell) datasets. Variational …
are not currently sufficient for large (temporally rich or single-cell) datasets. Variational …
Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics
Single-cell RNA sequencing (scRNA-seq) is a powerful tool to profile the transcriptomes of a
large number of individual cells at a high resolution. These data usually contain …
large number of individual cells at a high resolution. These data usually contain …
Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data
Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the
precise gene expression of individual cells and identify cell heterogeneity and …
precise gene expression of individual cells and identify cell heterogeneity and …
Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder
Single-cell RNA sequencing (scRNA-seq) is a revolutionary breakthrough that determines
the precise gene expressions on individual cells and deciphers cell heterogeneity and …
the precise gene expressions on individual cells and deciphers cell heterogeneity and …
Semisupervised generative autoencoder for single-cell data
Single-cell transcriptomics offers a tool to study the diversity of cell phenotypes through
snapshots of the abundance of mRNA in individual cells. Often there is additional …
snapshots of the abundance of mRNA in individual cells. Often there is additional …