ArticleMethods in molecular biology (Clifton, N.J.)2021
Identification of Gene Regulatory Networks from Single-Cell Expression Data.
Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Transcriptional regulation of plant innate immunity.Essays in biochemistry · 2022Review
- Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.Briefings in bioinformatics · 2022Review
- Cell-Type-Specific Profiling of theMembranes · 2022Article
- Application of High-Throughput Sequencing on the Chinese Herbal Medicine for the Data-Mining of the Bioactive Compounds.Frontiers in plant science · 2022Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNAseq is an emerging technology that allows the quantification of gene expression in individual cells. In plants, single-cell sequencing technology has been applied to generate root cell expression maps under many experimental conditions. DAP-seq and ATAC-seq have also been used to generate genome-scale maps of protein-DNA interactions and open chromatin regions in plants. In this protocol, we describe a multistep computational pipeline for the integration of single-cell RNAseq data with DAP-seq and ATAC-seq data to predict regulatory networks and key regulatory genes. Our approach utilizes machine learning methods including feature selection and stability selection to identify candidate regulatory genes. The network generated by this pipeline can be used to provide a putative annotation of gene regulatory modules and to identify candidate transcription factors that could play a key role in specific cell types.
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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.