Algorithm for the inference of cell types and lineage trees from single-cell RNA-seq data. Please download the R package from the RaceID3_StemID2_package repository, which contains the current version as an R package. This repository will not be updated anymore.

7 Research products, page 1 of 1

Temporal expression of MOF acetyltransferase primes transcription factor networks for erythroid fate
2020IsRelatedTo
SAREV: A review on statistical analytics of single‐cell RNA sequencing data
2021IsRelatedTo
FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
2017IsRelatedTo
Hemogenic Endothelial Cells Can Transition to Hematopoietic Stem Cells through a B-1 Lymphocyte-Biased State during Maturation in the Mouse Embryo
2019IsRelatedTo
BMP gradient along the intestinal villus axis controls zonated enterocyte and goblet cell states
2022IsRelatedTo
Whole-organism clone tracing using single-cell sequencing
2018IsRelatedTo
Challenges in unsupervised clustering of single-cell RNA-seq data
2019IsRelatedTo

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RaceID3_StemID2 software on GitHub

RaceID3_StemID2 software on GitHub

7 Research products, page 1 of 1

Temporal expression of MOF acetyltransferase primes transcription factor networks for erythroid fate

SAREV: A review on statistical analytics of single‐cell RNA sequencing data

FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data

Hemogenic Endothelial Cells Can Transition to Hematopoietic Stem Cells through a B-1 Lymphocyte-Biased State during Maturation in the Mouse Embryo

BMP gradient along the intestinal villus axis controls zonated enterocyte and goblet cell states

Whole-organism clone tracing using single-cell sequencing

Challenges in unsupervised clustering of single-cell RNA-seq data