Article

Dimension reduction and denoising of single-cell RNA sequencing data in the presence of observed confounding variables

2020-08-04

Abstract excerpt

Confounding variation, such as batch effects, are a pervasive issue in single-cell RNA sequencing experiments. While methods exist for aligning cells across batches, it is yet unclear how to correct for other types of confounding variation which may be observed at the subject level, such as age and sex, and at the cell level, such as library size and other measures of cell quality. On the specific problem of batch...

Topics

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

Identifiers and source

Literature Corpus work: 5627e973-0a03-51b8-afa0-83e27bc6e3d3
DOI: 10.1101/2020.08.03.234765

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Dimension reduction and denoising of single-cell RNA sequencing data in the presence of observed confounding variablesDOI 10.1101/2020.08.03.234765Read

A Joint Deep Learning Model for Simultaneous Batch Effect Correction, Denoising and Clustering in Single-Cell TranscriptomicsSemantic proximity Read

Assessment of batch-correction methods for scRNA-seq data with a new test metricSemantic proximity Read

A Fully Automated, Data-Driven Approach for Dimensionality Reduction and Clustering in Single-Cell RNA-seq AnalysisSemantic proximity Read

BRACE: A novel Bayesian-based imputation approach for dimension reduction analysis of alternative splicing at single-cell resolutionSemantic proximity Read

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

<b>McImpute</b> : Matrix completion based imputation for single cell RNA-seq dataSemantic proximity Read

Varying-Censoring Aware Matrix Factorization for Single Cell RNA-SequencingSemantic proximity Read

scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing dataSemantic proximity Read

Accurate estimation of cell-type resolution transcriptome in bulk tissue through matrix completionSemantic proximity Read

Data Matrix Normalization and Merging Strategies Minimize Batch-specific Systemic Variation in scRNA-Seq DataSemantic proximity Read

Recommendations of Scrna-Seq Differential Gene Expression Analysis Based on Comprehensive BenchmarkingSemantic proximity Read

Single-cell-level condition-related signal estimation with batch effect removal through neural discrete representation learningSemantic proximity Read