ArticleGenome research2021
Applications of single-cell genomics and computational strategies to study common disease and population-level variation.
Article in Genome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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
17 citing papers in PubMed.
- AI-based characterization of Alzheimer's disease phenotypes from population-scale single-cell data.Nature medicine · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Uncovering the Molecular Signatures of Rare Genetic Diseases in the Punjabi Population.International journal of molecular sciences · 2025Observational
- ResNet50 and Single-Cell Multi-Omics analysis identify key cellular and molecular features in pediatric acute lymphoblastic leukemia.Annals of hematology · 2025Article
- scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data.Biology · 2025Article
- Mitochondrial Dysfunction in Diabetes: Shedding Light on a Widespread Oversight.Pathophysiology : the official journal of the International Society for Pathophysiology · 2025Review
- Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach.Genome research · 2025Article
- Differential expression and co-expression reveal cell types relevant to genetic disorder phenotypes.Bioinformatics (Oxford, England) · 2024Article
- Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells.bioRxiv : the preprint server for biology · 2024Article
- eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.BMC bioinformatics · 2024Article
- eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.bioRxiv : the preprint server for biology · 2024Article
- Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression.Bioinformatics advances · 2024Article
- Exploring Single-Cell Exposomics by Mass Spectrometry.Environmental science & technology · 2023Review
- Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases.Arteriosclerosis, thrombosis, and vascular biology · 2023Review
- Multi-omic integration reveals cell-type-specific regulatory networks of insulin resistance in distinct ancestry populations.Cell systems · 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
- The role of single-cell genomics in human genetics.Journal of medical genetics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.
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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.