Article

Sfaira accelerates data and model reuse in single cell genomics

2020-12-16

Abstract excerpt

Exploratory analysis of single-cell RNA-seq data sets is currently based on statistical and machine learning models that are adapted to each new data set from scratch. A typical analysis workflow includes a choice of dimensionality reduction, selection of clustering parameters, and mapping of prior annotation. These steps typically require several iterations and can take up significant time in many single-cell RNA...

Topics

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

Identifiers and source

Literature Corpus work: 88515dae-f08c-5995-99c2-d73c6dddc41b
DOI: 10.1101/2020.12.16.419036

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Sfaira accelerates data and model reuse in single cell genomicsDOI 10.1101/2020.12.16.419036Read

Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding spaceSemantic proximity Read

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding spaceSemantic proximity Read

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankingsSemantic proximity Read

GLUER: integrative analysis of single-cell omics and imaging data by deep neural networkSemantic proximity Read

Enhancement of Network Architecture Alignment in Comparative Single-Cell StudiesSemantic proximity Read

Interpretable joint clustering of single-cell transcriptomesSemantic proximity Read

UniCell: Towards a Unified Solution for Cell Annotation, Nomenclature Harmonization, Atlas Construction in Single-Cell TranscriptomicsSemantic proximity Read

Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease statesSemantic proximity Read

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

Scaling cross-tissue single-cell annotation modelsSemantic proximity Read

High order expression dependencies finely resolve cryptic states and subtypes in single cell dataSemantic proximity Read

SMaSH: a scalable, general marker gene identification framework for single-cell RNA-sequencingSemantic proximity Read