Article

Regulatory network-based imputation of dropouts in single-cell RNA sequencing data

2019-04-22

Abstract excerpt

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Further, it is...

Topics

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

Identifiers and source

Literature Corpus work: 4446a5f0-6922-51f1-88cb-bb2d4b1aa6d8
DOI: 10.1101/611517

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Regulatory network-based imputation of dropouts in single-cell RNA sequencing dataDOI 10.1101/611517Read

scImpute: Accurate And Robust Imputation For Single Cell RNA-Seq DataSemantic proximity Read

Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessingSemantic proximity Read

SmartImpute: A Targeted Imputation Framework for Single-cell Transcriptome DataSemantic proximity Read

A United Statistical Framework for Single Cell and Bulk Sequencing DataSemantic proximity Read

scAnnotate: an automated cell type annotation tool for single-cell RNA-sequencing dataSemantic proximity Read

Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq dataSemantic proximity Read

iDESC: Identifying differential expression in single-cell RNA sequencing data with multiple subjectsSemantic proximity Read

A Flexible, Interpretable, and Accurate Approach for Imputing the Expression of Unmeasured GenesSemantic proximity Read

Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlasSemantic proximity Read

A novel graph-based k-partitioning approach improves the detection of gene-gene correlations by single-cell RNA sequencingSemantic proximity Read

Joint Gene Network Construction by Single-Cell RNA Sequencing DataSemantic proximity Read

SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistencySemantic proximity Read