Toolkit/GENIE3

GENIE3

Computational Method·Research

Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.

Summary

The anchor paper and PubMed record explicitly describe GENIE3 as the co-expression/network inference component used in SCENIC.

Usefulness & Problems

No literature-backed usefulness or problem-fit explainer has been materialized for this record yet.

Published Workflows

SCENIC: single-cell regulatory network inference and clustering

2017

Objective: Simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data.

Why it works: The abstract states that cis-regulatory analysis can guide identification of transcription factors and cell states, and the web research summary describes a decomposition into network inference, motif-based pruning, and regulon activity scoring.

cis-regulatory analysis to guide identification of transcription factors and cell statesregulon-based interpretation of cellular heterogeneityco-expression or network inferencemotif enrichment and direct-target pruningper-cell regulon activity scoring

Stages

  1. 1.
    Network inference(broad_screen)

    The web research summary states that SCENIC first uses GENIE3 or GRNBoost for co-expression or network inference.

    Selection: co-expression or network inference from single-cell RNA-seq data

  2. 2.
    Motif-enrichment-based pruning to direct targets or regulons(secondary_characterization)

    The web research summary states that RcisTarget is used for motif-enrichment-based pruning to direct targets or regulons after network inference.

    Selection: motif enrichment and direct-target pruning

  3. 3.
    Per-cell regulon activity scoring(functional_characterization)

    The web research summary states that AUCell is used for per-cell regulon activity scoring in the final stage of SCENIC.

    Selection: regulon activity scoring across individual cells

Steps

  1. 1.
    Infer co-expression or regulatory network candidates from single-cell RNA-seq datanetwork inference component

    Generate candidate regulatory relationships from expression data.

    This step occurs first because downstream motif-based pruning and regulon scoring require an initial set of candidate regulatory relationships.

  2. 2.
    Prune inferred relationships using motif enrichment to identify direct targets or regulonsmotif-enrichment and pruning component

    Refine candidate regulatory relationships toward direct targets and regulons.

    This step follows network inference because motif-based pruning acts on the initially inferred candidate relationships.

  3. 3.
    Score regulon activity in individual cellsregulon activity scoring component

    Quantify regulon activity per cell to support cell-state identification.

    This step occurs after regulon definition because per-cell activity scoring requires the regulons produced by the pruning stage.

Taxonomy & Function

Primary hierarchy

Technique Branch

Method: A concrete computational method used to design, rank, or analyze an engineered system.

Target processes

selection

Validation

Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication

Supporting Sources

Ranked Claims

Claim 1biological insightsupports2017Source 1needs review

SCENIC provides biological insights into mechanisms driving cellular heterogeneity.

SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.
Claim 2mechanistic or analysis principlesupports2017Source 1needs review

Cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states in single-cell data.

we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states.
Claim 3method capabilitysupports2017Source 1needs review

SCENIC is a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data.

We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data.
Claim 4workflow compositionsupports2017Source 1needs review

The SCENIC workflow includes GENIE3 or GRNBoost for co-expression or network inference, RcisTarget for motif-enrichment-based pruning to direct targets or regulons, and AUCell for per-cell regulon activity scoring.

Anchor record on PubMed confirms the paper metadata and explicitly states the SCENIC workflow uses GENIE3 or GRNBoost for co-expression modules, RcisTarget for motif enrichment/direct-target pruning, and AUCell for regulon activity scoring.

Approval Evidence

1 source1 linked approval claimfirst-pass slug genie3
The anchor paper and PubMed record explicitly describe GENIE3 as the co-expression/network inference component used in SCENIC.

Source:

workflow compositionsupports

The SCENIC workflow includes GENIE3 or GRNBoost for co-expression or network inference, RcisTarget for motif-enrichment-based pruning to direct targets or regulons, and AUCell for per-cell regulon activity scoring.

Anchor record on PubMed confirms the paper metadata and explicitly states the SCENIC workflow uses GENIE3 or GRNBoost for co-expression modules, RcisTarget for motif enrichment/direct-target pruning, and AUCell for regulon activity scoring.

Source:

Comparisons

No literature-backed comparison notes have been materialized for this record yet.

Ranked Citations

  1. 1.
    StructuralSource 1Nature Methods2017Claim 1Claim 2Claim 3

    Extracted from this source document.