Article

Benchmarking and new generative methods for single-cell transcriptome data in bulk RNA sequence deconvolution

2023-09-18

Abstract excerpt

Numerous methods for bulk RNA sequence deconvolution have been developed to identify cellular targets of diseases by understanding the composition of cell types in disease-related tissues. However, issues of heterogeneity in gene expression between subjects and the shortage of reference single-cell RNA sequence (scRNAseq) data remain to achieve accurate bulk deconvolution. In our study, we investigated whether a n...

Topics

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

Identifiers and source

Literature Corpus work: 95ae53f2-5b77-5600-b9e4-85744ed7d733
DOI: 10.21203/rs.3.rs-3338396/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Benchmarking and new generative methods for single-cell transcriptome data in bulk RNA sequence deconvolutionDOI 10.21203/rs.3.rs-3338396/v1Read

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

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

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

Interpretable deep generative ensemble learning for single-cell omics with HydraSemantic proximity Read

Deep-learning-based cell composition analysis from tissue expression profilesSemantic proximity Read

SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing ReferencesSemantic proximity Read

Parameter-free representations outperform single-cell foundation models on downstream benchmarksSemantic proximity Read

Generative Augmentation Reveals Previously Overlooked Signals in Transcriptomic DatasetsSemantic proximity Read

scSemiProfiler: Advancing Large-scale Single-cell Studies through Semi-profiling with Deep Generative Models and Active LearningSemantic proximity Read

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