ArticleBMC systems biology2018
scdNet: a computational tool for single-cell differential network analysis.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Prioritizing Autism Risk Genes using Personalized Graphical Models Estimated from Single Cell RNA-seq Data.Journal of the American Statistical Association · 2022Article
- Disentangling the Molecular Pathways of Parkinson's Disease using Multiscale Network Modeling.Trends in neurosciences · 2021Review
- Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge.Frontiers in genetics · 2021Article
- Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models.Frontiers in genetics · 2021Article
- High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery.Frontiers in genetics · 2020Review
- Joint learning of multiple gene networks from single-cell gene expression data.Computational and structural biotechnology journal · 2020Article
- Single-Cell Analysis of Circulating Tumor Cells: How Far Have We Come in the -Omics Era?Frontiers in genetics · 2019Review
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: systems biology on diverse data types.BMC systems biology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundSingle-cell RNA sequencing (scRNA-Seq) is an emerging technology that has revolutionized the research of the tumor heterogeneity. However, the highly sparse data matrices generated by the technology have posed an obstacle to the analysis of differential gene regulatory networks.
resultsAddressing the challenges, this study presents, as far as we know, the first bioinformatics tool for scRNA-Seq-based differential network analysis (scdNet). The tool features a sample size adjustment of gene-gene correlation, comparison of inter-state correlations, and construction of differential networks. A simulation analysis demonstrated the power of scdNet in the analyses of sparse scRNA-Seq data matrices, with low requirement on the sample size, high computation efficiency, and tolerance of sequencing noises. Applying the tool to analyze two datasets of single circulating tumor cells (CTCs) of prostate cancer and early mouse embryos, our data demonstrated that differential gene regulation plays crucial roles in anti-androgen resistance and early embryonic development.
conclusionsOverall, the tool is widely applicable to datasets generated by the emerging technology to bring biological insights into tumor heterogeneity and other studies. MATLAB implementation of scdNet is available at https://github.com/ChenLabGCCRI/scdNet .
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.