ArticleBioinformatics (Oxford, England)2017
SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 240 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
240 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming.bioRxiv : the preprint server for biology · 2026Article
- mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.Bioinformatics (Oxford, England) · 2026Article
- PROFET predicts continuous gene expression dynamics from scRNA-seq data to elucidate heterogeneity of cancer treatment responses.Cell systems · 2026Article
- Multiscale learning of gene network-driven phenotypic dynamics of single cells.Molecular systems biology · 2026Article
- Inferring cell-specific gene regulatory networks based on causal graph embedding.Cell reports methods · 2026Article
- Inference of spatial chromatin accessibility via integration of spatial transcriptomics and single-cell multi-omics data.Nature communications · 2026Article
- CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.Bioinformatics (Oxford, England) · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Combinatorial transcription factor interactions drive modular gene regulatory networks.bioRxiv : the preprint server for biology · 2026Article
- From time-course expression to gene regulation: direct linear ODE inference without finite-difference approximation.bioRxiv : the preprint server for biology · 2026Article
- Unveiling gene perturbation effects through gene regulatory networks inference from single-cell transcriptomic data.PLoS computational biology · 2026Article
- PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data.PLoS computational biology · 2026Article
- Foundation Model for Biological Temporal Data Dynamics with Experimental Validation.Research square · 2026Article
- Inference of Genetic Networks from Pseudo Time Series of Single-cell Gene Expression Data using Modified Random Forests.Bulletin of mathematical biology · 2026Article
- EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq.Computational biology and chemistry · 2026Article
- Revealing hidden regulatory dependencies: multi-perspective graph learning for single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- CellCraft: an extensible visual programming application for gene regulatory network inference.Bioinformatics (Oxford, England) · 2026Article
- TDAGENE: Inference of Gene Regulatory Network Based on Topological Data Analysis and Graph Attention Network for Single-Cell RNA Sequencing Data.Computational and structural biotechnology journal · 2026Article
180 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationThe analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.
resultsIn this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses. AVAILABILITY AND IMPLEMENTATION: The R source code of SCODE is available at https://github.com/hmatsu1226/SCODE. CONTACT: hirotaka.matsumoto@riken.jp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.