ArticleBMC bioinformatics2019
M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data.GigaScience · 2022Article
- Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability.NAR genomics and bioinformatics · 2022Article
- Comparison and evaluation of statistical error models for scRNA-seq.Genome biology · 2022Article
- Ref-1 redox activity alters cancer cell metabolism in pancreatic cancer: exploiting this novel finding as a potential target.Journal of experimental & clinical cancer research : CR · 2021Article
- SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data.Briefings in bioinformatics · 2021Article
- Review
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2019: bioinformatics methods and applications for human diseases.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundVarious statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model.
resultsWe developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model.
conclusionA comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.
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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.