ArticleGenomics, proteomics & bioinformatics2021
c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network.
Article in Genomics, proteomics & bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- CSCN: inference of cell-specific causal networks using single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2026Article
- Combining Spatial Multi-Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self-Supervised Graph Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Inferring cell-specific gene regulatory networks based on causal graph embedding.Cell reports methods · 2026Article
- CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.Bioinformatics (Oxford, England) · 2026Article
- scSCCNIA: similarity matrix based contrastive clustering with neighbor information aggregation for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Spatially varying gene regulation network inference from spatial transcriptomics.Bioinformatics advances · 2026Article
- Distilling Direct Effects via Conditional Differential Gene Expression Analysis.bioRxiv : the preprint server for biology · 2025Article
- Review
- Revealing the critical state and identifying individualized dynamic network biomarker for type 2 diabetes through advanced analysis methods on individual basis.Scientific reports · 2025Article
- Disease prediction by network information gain on a single sample basis.Fundamental research · 2025Article
- WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.Frontiers in genetics · 2025Article
- Epigenetic characterization of adult rhesus monkey spermatogonial stem cells identifies key regulators of stem cell homeostasis.Nucleic acids research · 2024Article
- The network structural entropy for single-cell RNA sequencing data during skin aging.Briefings in bioinformatics · 2024Article
- Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data.BMC biology · 2024Article
- Modelling cell type-specific lncRNA regulatory network in autism with Cycle.BMC bioinformatics · 2024Article
- A python library for the fast and scalable computation of biologically meaningful individual specific networks.Scientific reports · 2024Article
- Detecting tipping points of complex diseases by network information entropy.Briefings in bioinformatics · 2024Article
- scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks.Briefings in bioinformatics · 2024Article
- Potential network markers and signaling pathways for B cells of COVID-19 based on single-cell condition-specific networks.BMC genomics · 2023Article
- Edge and modular significance assessment in individual-specific networks.Scientific reports · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the c-CSN method, which can construct the conditional cell-specific network (CCSN) for each cell. c-CSN method can measure the direct associations between genes by eliminating the indirect associations. c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less "reliable" gene expression to more "reliable" gene-gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach. 1) One direct association network is generated for one cell. 2) Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices. 3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. c-CSN is publicly available at https://github.com/LinLi-0909/c-CSN.
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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.