Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Reversed graph embedding resolves complex single-cell trajectories.

Nature methods - 1 Oct 2017

Qiu Xiaojie, Mao Qi, Tang Ying, Wang Li, Chawla Raghav, Pliner Hannah A, Trapnell Cole

Abstract excerpt

Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Cell Differentiation
Share to Computer Simulation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Gene Expression Regulation, Developmental

Share to Models, Biological

Share to Mutation

Share to Transcription Factors

Share to Transcriptome

Source

Open in PubMed Open full text

Identifiers

PMID: 28825705
DOI: 10.1038/nmeth.4402
PMCID: PMC5764547

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Reversed graph embedding resolves complex single-cell trajectories.PMID 28825705Read ↗

DyMoTree decodes early cell state transitions and drivers from single-cell transcriptomes using a tree-structured neural networkSemantic proximity Read ↗

Directing cellular transitions on gene graph-enhanced cell state manifoldSemantic proximity Read ↗

Multiscale learning of gene network-driven phenotypic dynamics of single cells.Semantic proximity Read ↗

DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq dataSemantic proximity Read ↗

Learning dynamics by computational integration of single cell genomic and lineage informationSemantic proximity Read ↗

Learning dynamics by computational integration of single cell genomic and lineage informationSemantic proximity Read ↗

CycleGRN: Inferring Gene Regulatory Networks from Cyclic Flow Dynamics in Single-Cell RNA-seqSemantic proximity Read ↗

CellRank for directed single-cell fate mappingSemantic proximity Read ↗

Cell-DRL Reconstructs Unseen Cellular Paths in Health and DiseaseSemantic proximity Read ↗

CellRank for directed single-cell fate mappingSemantic proximity Read ↗

StaVia: Spatially and temporally aware cartography with higher order random walks for cell atlasesSemantic proximity Read ↗

Benchmarking zero-shot single-cell foundation model embeddings for cellular dynamics reconstructionSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.