ArticlePLoS computational biology2022
IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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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.
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Who cites it
19 citing papers in PubMed, 25 citations in OpenAlex.
- ZEB1 drives terminal erythroid maturation by controlling the GATA2-KLF1 regulatory switch.Nucleic acids research · 2026Article
- Deciphering Cell-Type-Specific Transcriptional Regulation in Tomato Leaves Through Ensemble Machine Learning and Single-Cell Transcriptomics.Plants (Basel, Switzerland) · 2026Article
- From genes to germ layers: virtual twins of gastruloids.NPJ systems biology and applications · 2026Review
- Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming.NPJ systems biology and applications · 2025Article
- Attractor Landscape Analysis Reveals a Reversion Switch in the Transition of Colorectal Tumorigenesis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Control of Cellular Differentiation Trajectories for Cancer Reversion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- OneSC: a computational platform for recapitulating cell state transitions.Bioinformatics (Oxford, England) · 2024Article
- OneSC: A computational platform for recapitulating cell state transitions.bioRxiv : the preprint server for biology · 2024Article
- Inferring Gene Regulatory Networks and Predicting the Effect of Gene Perturbations via IQCELL.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Learning single-cell perturbation responses using neural optimal transport.Nature methods · 2023Article
- Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector.Nature communications · 2023Article
- Virtual cells in a virtual microenvironment recapitulate early development-like patterns in human pluripotent stem cell colonies.Stem cell reports · 2023Article
- Systems bioengineering approaches for developmental toxicology.Computational and structural biotechnology journal · 2023Review
- Transcriptional networks of transient cell states during human prefrontal cortex development.Frontiers in molecular neuroscience · 2023Article
- Editorial: Transcription regulation - Brain development and homeostasis - A finely tuned and orchestrated scenario in physiology and pathology, volume II.Frontiers in molecular neuroscience · 2023Article
- spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data.Molecular systems biology · 2022Article
- Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era.Briefings in functional genomics · 2022Article
- scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model.NAR genomics and bioinformatics · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to scRNA-seq datasets from early mouse T-cell and red blood cell development, and show that the platform can infer overall over 74% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of known gene perturbations. Finally, we implement an IQCELL gene selection pipeline that allows us to identify candidate genes, without prior knowledge. We demonstrate that GRN simulations based on the inferred set yield results similar to the original curated lists. In summary, the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems.
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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 Socratic graph.