ArticleBioinformatics (Oxford, England)2020
Causal network perturbations for instance-specific analysis of single cell and disease samples.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Discovering Root Causal Genes with High Throughput Perturbations.bioRxiv : the preprint server for biology · 2024Article
- Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling.EClinicalMedicine · 2024Article
- Transcriptome data are insufficient to control false discoveries in regulatory network inference.Cell systems · 2024Article
- Causal network perturbation analysis identifies known and novel type-2 diabetes driver genes.bioRxiv : the preprint server for biology · 2024Article
- Evaluation of single-sample network inference methods for precision oncology.NPJ systems biology and applications · 2024Article
- Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis.Respiratory research · 2023Article
- Causal Discovery in High-dimensional, Multicollinear Datasets.Frontiers in epidemiology · 2022Article
- Detection of differentially abundant cell subpopulations in scRNA-seq data.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine.Journal of proteome research · 2021Article
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Authors and funding
3 authors.
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Abstract
motivationComplex diseases involve perturbation in multiple pathways and a major challenge in clinical genomics is characterizing pathway perturbations in individual samples. This can lead to patient-specific identification of the underlying mechanism of disease thereby improving diagnosis and personalizing treatment. Existing methods rely on external databases to quantify pathway activity scores. This ignores the data dependencies and that pathways are incomplete or condition-specific.
resultsssNPA is a new approach for subtyping samples based on deregulation of their gene networks. ssNPA learns a causal graph directly from control data. Sample-specific network neighborhood deregulation is quantified via the error incurred in predicting the expression of each gene from its Markov blanket. We evaluate the performance of ssNPA on liver development single-cell RNA-seq data, where the correct cell timing is recovered; and two TCGA datasets, where ssNPA patient clusters have significant survival differences. In all analyses ssNPA consistently outperforms alternative methods, highlighting the advantage of network-based approaches. AVAILABILITY AND IMPLEMENTATION: http://www.benoslab.pitt.edu/Software/ssnpa/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.