ArticleBioinformatics (Oxford, England)2018
Single cell network analysis with a mixture of Nested Effects Models.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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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
5 citing papers in PubMed, 16 citations in OpenAlex.
- scDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq.Briefings in bioinformatics · 2024Article
- Defining and identifying cell sub-crosstalk pairs for characterizing cell-cell communication patterns.Scientific reports · 2023Article
- Hierarchy of TGFβ/SMAD, Hippo/YAP/TAZ, and Wnt/β-catenin signaling in melanoma phenotype switching.Life science alliance · 2022Article
- NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.Frontiers in genetics · 2021Article
- Computational methods for single-cell omics across modalities.Nature methods · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: New technologies allow for the elaborate measurement of different traits of single cells under genetic perturbations. These interventional data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous. Results: We developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular subpopulations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq. Availability and implementation: The mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbg-ethz/mnem/. Supplementary information: Supplementary data are available at Bioinformatics online.
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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 OpenQuestion graph.