Article

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

2021-08-09

Abstract excerpt

<title>Abstract</title> <p>A fundamental problem of downstream analysis of scRNA-seq data is the unavailability of enough cell samples compare to the feature size. This is mostly due to the budgetary constraint of single cell experiments or simply because of the small number of available patient samples. Here, we present an improved version of generative adversarial network (GAN) called LSH-GAN to address this is...

Topics

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

Identifiers and source

Literature Corpus work: a5368626-743d-5a0c-a583-2bb7f8dd1442
DOI: 10.21203/rs.3.rs-736403/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

LSH-GAN: in-silico generation of cells for small sample high dimensional scRNA-seq dataDOI 10.21203/rs.3.rs-736403/v1Read

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

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

Hidden Knowledge Recovery from GAN-generated Single-cell RNA-seq DataSemantic proximity Read

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

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

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

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

RNAGAN: Train One and Get Four, Multipurpose Human RNA-Seq Analysis Tool with Enhanced Interpretability and Small Data Size CapabilitySemantic proximity Read

Predicting LncRNA-Disease Association Based on Generative Adversarial Network.Semantic proximity Read

Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN.Semantic proximity Read

Highly Realistic Whole Transcriptome Synthesis through Generative Adversarial NetworksSemantic proximity Read

Generating hard-to-obtain information from easy-to-obtain information: applications in drug discovery and clinical inferenceSemantic proximity Read