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Showing 1–10 of 10 results
Advanced filters: Author: Wouter Saelens Clear advanced filters

  • This study introduces single-cell transcription factor (TF) sequencing, a single-cell barcoded and doxycycline-inducible TF overexpression approach that reveals dose-sensitive functional classes of TFs and cellular heterogeneity by mapping TF dose-dependent transcriptomic changes during the reprogramming of mouse embryonic multipotent stromal cells.

    • Wangjie Liu
    • Wouter Saelens
    • Bart Deplancke
    ResearchOpen Access
    Nature Genetics
    Volume: 57, P: 2522-2535

  • Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. Here, Van den Berge et al. develop tradeSeq, a framework for the inference of within and between-lineage differential expression, based on negative binomial generalized additive models.

    • Koen Van den Berge
    • Hector Roux de Bézieux
    • Lieven Clement
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-13

  • Modules composed of groups of genes with similar expression profiles tend to be functionally related and co-regulated. Here, Saelens et al evaluate the performance of 42 computational methods and provide practical guidelines for module detection in gene expression data.

    • Wouter Saelens
    • Robrecht Cannoodt
    • Yvan Saeys
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-12

  • To benchmark single cell bioinformatics tools, data simulators can provide a robust ground truth. Here the authors present dyngen, a multi-modal simulator, and apply it to aligning cell developmental trajectories, cell-specific regulatory network inference and estimation of RNA velocity.

    • Robrecht Cannoodt
    • Wouter Saelens
    • Yvan Saeys
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-9

  • The authors comprehensively benchmark the accuracy, scalability, stability and usability of 45 single-cell trajectory inference methods.

    • Wouter Saelens
    • Robrecht Cannoodt
    • Yvan Saeys
    Research
    Nature Biotechnology
    Volume: 37, P: 547-554

  • Live-seq, a single-cell transcriptome profiling approach that preserves cell viability during RNA extraction using fluidic force microscopy, can address a range of biological questions by transforming scRNA-seq from an end-point to a temporal analysis approach.

    • Wanze Chen
    • Orane Guillaume-Gentil
    • Bart Deplancke
    ResearchOpen Access
    Nature
    Volume: 608, P: 733-740

  • SCENIC is a computational pipeline to predict cell-type-specific transcription factors through network inference and motif enrichment. Here the authors describe a detailed protocol for pySCENIC: a faster, container-based implementation in Python.

    • Bram Van de Sande
    • Christopher Flerin
    • Stein Aerts
    Protocols
    Nature Protocols
    Volume: 15, P: 2247-2276