Harmonizing heterogeneous single-cell gene expression data with individual-level covariate information

The scINSIGHT2 method has been implemented as a R package, which is available at https://github.com/yudimu/scINSIGHT2/.

Authors

Institutions

Publication Details

Journal
Bioinformatics Advances
Published
2024-12-26
DOI
https://doi.org/10.1093/bioadv/vbaf189
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Harmonizing heterogeneous single-cell gene expression data with individual-level covariate information

Yudi Mu, Wei Vivian Li
Bioinformatics Advances
Single-cell and spatial transcriptomics
article

Harmonizing heterogeneous single-cell gene expression data with individual-level covariate information

Yudi Mu, Wei Vivian Li
article en

Abstract

The scINSIGHT2 method has been implemented as a R package, which is available at https://github.com/yudimu/scINSIGHT2/.

Bioinformatics AdvancesVol. 5(1)
University of California, Riverside (US)
National Science Foundation, National Institutes of Health, University of California, Riverside
Openalex Percentile: Top 77%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Harmonizing heterogeneous single-cell gene expression data with individual-level covariate information — Yudi Mu, Wei Vivian Li · Bioinformatics Advances (2024) | TGRS Research Map | TGRS