ArticleCell systems2016
Low Dimensionality in Gene Expression Data Enables the Accurate Extraction of Transcriptional Programs from Shallow Sequencing.
Article in Cell systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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Who cites it
78 citing papers in PubMed.
- Latent causal diffusions for single-cell perturbation modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- SC-framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets.iScience · 2026Article
- Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Revealing global stoichiometry conservation architecture in cells from Raman spectral patterns.eLife · 2026Article
- Stage-Associated Cellular and Molecular Signatures in Diabetic Retinopathy Identified Through Integrated Bulk and Single-Cell Transcriptomic Analysis.International journal of molecular sciences · 2026Article
- Revealing a coherent cell-state landscape across single-cell datasets with CONCORD.Nature biotechnology · 2026Article
- Statistically principled feature selection for single cell transcriptomics.BMC bioinformatics · 2025Article
- Revealing a coherent cell state landscape across single cell datasets with CONCORD.bioRxiv : the preprint server for biology · 2025Article
- The intrinsic dimension of gene expression during cell differentiation.Nucleic acids research · 2025Article
- Single-nucleus profiling of the left ventricle of the mouse heart after chronic stress.Scientific data · 2025Article
- Live-cell omics with Raman spectroscopy.Microscopy (Oxford, England) · 2025Review
- Adaptive-like NK cell responses to influenza correlate with humoral immunity and are influenced by age and sex.bioRxiv : the preprint server for biology · 2025Article
- Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq.Nature communications · 2025Article
- Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales.Annual review of biophysics · 2025Review
- Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq.bioRxiv : the preprint server for biology · 2025Article
- Article
- Soft Modes as a Predictive Framework for Low Dimensional Biological Systems across Scales.ArXiv · 2024Article
- Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.Nature neuroscience · 2024Review
- Bacterial single-cell RNA sequencing captures biofilm transcriptional heterogeneity and differential responses to immune pressure.Nature communications · 2024Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A tradeoff between precision and throughput constrains all biological measurements, including sequencing-based technologies. Here, we develop a mathematical framework that defines this tradeoff between mRNA-sequencing depth and error in the extraction of biological information. We find that transcriptional programs can be reproducibly identified at 1% of conventional read depths. We demonstrate that this resilience to noise of "shallow" sequencing derives from a natural property, low dimensionality, which is a fundamental feature of gene expression data. Accordingly, our conclusions hold for ∼350 single-cell and bulk gene expression datasets across yeast, mouse, and human. In total, our approach provides quantitative guidelines for the choice of sequencing depth necessary to achieve a desired level of analytical resolution. We codify these guidelines in an open-source read depth calculator. This work demonstrates that the structure inherent in biological networks can be productively exploited to increase measurement throughput, an idea that is now common in many branches of science, such as image processing.
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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.