Article

<i>ACTIVA</i> : realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders

2021-01-30

Abstract excerpt

<h4>Motivation</h4> Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps, or identifying rare subpopulations. However, a critical complication remains the low number of single-cell observations due to limitations by the rarity of a subpop...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 55cf4da9-e7a2-5a26-a91f-3b451e67b3c7
DOI: 10.1101/2021.01.28.428725

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

<i>ACTIVA</i> : realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencodersDOI 10.1101/2021.01.28.428725Read

Generating Synthetic Single Cell Data from Bulk RNA-seq Using a Pretrained Variational AutoencoderSemantic proximity Read

Evaluation of Generative AI Models for Processing Single-Cell RNA-Sequencing Data in Human Pancreatic TissueSemantic proximity Read

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

scDREAMER: atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifierSemantic proximity Read

scGFT: single-cell RNA-seq data augmentation using generative Fourier transformerSemantic proximity Read

Designing Single Cell RNA-Sequencing Experiments for Learning Latent RepresentationsSemantic proximity Read

LSH-GAN: in-silico generation of cells for small sample high dimensional scRNA-seq dataSemantic proximity Read

SIDISH Identifies High-Risk Disease-Associated Cells and Biomarkers by Integrating Single-Cell Depth and Bulk BreadthSemantic proximity Read

ARTEMIS integrates autoencoders and Schrödinger Bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell dataSemantic proximity Read

mcBERT: Patient-Level Single-cell Transcriptomics Data RepresentationSemantic proximity Read

A Diffusion-Based Autoencoder for Learning Patient-Level Representations from Single-Cell DataSemantic proximity Read