ArticlePatterns (New York, N.Y.)2022
EMBEDR: Distinguishing signal from noise in single-cell omics data.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- Article
- Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective.Nature communications · 2025Article
- Review
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.Briefings in bioinformatics · 2024Article
- Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters.Nature communications · 2024Article
- A physically inspired approach to coarse-graining transcriptomes reveals the dynamics of aging.PloS one · 2024Article
- Spherical Rotation Dimension Reduction with Geometric Loss Functions.Journal of machine learning research : JMLR · 2024Article
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.bioRxiv : the preprint server for biology · 2023Article
- Improving reduced-order models through nonlinear decoding of projection-dependent outputs.Patterns (New York, N.Y.) · 2023Article
- How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data.Briefings in bioinformatics · 2022Article
- Data-driven assessment of dimension reduction quality for single-cell omics data.Patterns (New York, N.Y.) · 2022Article
- Quality control in scRNA-Seq can discriminate pacemaker cells: the mtRNA bias.Cellular and molecular life sciences : CMLS · 2021Article
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Authors and funding
3 authors.
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Abstract
Single-cell "omics"-based measurements are often high dimensional so that dimensionality reduction (DR) algorithms are necessary for data visualization and analysis. The lack of methods for separating signal from noise in DR outputs has limited their utility in generating data-driven discoveries in single-cell data. In this work we present EMBEDR, which assesses the output of any DR algorithm to distinguish evidence of structure from algorithm-induced noise in DR outputs. We apply EMBEDR to DR-generated representations of single-cell omics data of several modalities to show where they visually show real-not spurious-structure. EMBEDR generates a "p" value for each sample, allowing for direct comparisons of DR algorithms and facilitating optimization of algorithm hyperparameters. We show that the scale of a sample's neighborhood can thus be determined and used to generate a novel "cell-wise optimal" embedding. EMBEDR is available as a Python package for immediate use.
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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.