ArticleScientific reports2017
Batch effects and the effective design of single-cell gene expression studies.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 200 papers.
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Who cites it
200 citing papers in PubMed.
- Single-cell Transcriptomics in Autoimmune Diseases: Mechanistic Insights, Cross-disease Convergence, and Translational Challenges in Rheumatoid Arthritis, Systemic Lupus Erythematosus, Type 1 Diabetes, and Multiple Sclerosis.Clinical reviews in allergy & immunology · 2026Review
- CMAC: A deep learning framework for absolute cardiomyocyte transcriptional age estimation.bioRxiv : the preprint server for biology · 2026Article
- Interleukin-6 is critical in the development of pulmonary vascular disease inProceedings of the National Academy of Sciences of the United States of America · 2026Article
- scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.Nature communications · 2026Article
- Transcriptomic Meta-Analysis as a Framework for Robust Cross-Study Biological Inference.International journal of molecular sciences · 2026Review
- Semi-supervised disentangled representation learning for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- A Mouthful of Genomic Data: Single-Cell Insights into Salivary Gland Biology and Disease.Biology · 2026Review
- scZiva: imputation method for single-cell RNA-seq data with zero-inflated variational autoencoder.BMC bioinformatics · 2026Article
- A single-cell atlas and Shiny-based framework for murine lung injury and remodeling.Frontiers in bioinformatics · 2026Article
- Integrative biomarker and drug target discovery in osteosarcoma: traditional experimental approaches and AI-enabled insights.Frontiers in pharmacology · 2026Review
- From bulk RNA sequencing to spatial transcriptomics: a comparative review of differential gene expression analysis methods.Human genomics · 2025Review
- A transcriptomic atlas of astrocyte heterogeneity across space and time in mouse and marmoset.Neuron · 2025Article
- Mega-scale single-cell profiling reveals novel biomarkers associated with acute GvHD after allogeneic hematopoietic stem cell transplantation.Biomarker research · 2025Article
- Multimodal single cell analyses reveal gene networks of planarian stem cell differentiation.Nature communications · 2025Article
- Unique Transcriptomic Cell Types of the Granular Retrosplenial Cortex Are Preserved across Mice and Rats despite Dramatic Changes in Key Marker Genes.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Recent advancements in tissue dissociation techniques for cell manufacturing single-cell analysis and downstream processing.Stem cells translational medicine · 2025Review
- Recovery of biological signals lost in single-cell batch integration with CellANOVA.Nature biotechnology · 2025Article
- Commentary: a review of technical considerations for planning an RNA-Sequencing experiment.BMC genomics · 2025Review
- Genomic characterization of normal and aberrant human milk production.Science advances · 2025Article
140 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. However, the sources of experimental noise in scRNA-seq are not yet well understood. We investigated the technical variation associated with sample processing using the single-cell Fluidigm C1 platform. To do so, we processed three C1 replicates from three human induced pluripotent stem cell (iPSC) lines. We added unique molecular identifiers (UMIs) to all samples, to account for amplification bias. We found that the major source of variation in the gene expression data was driven by genotype, but we also observed substantial variation between the technical replicates. We observed that the conversion of reads to molecules using the UMIs was impacted by both biological and technical variation, indicating that UMI counts are not an unbiased estimator of gene expression levels. Based on our results, we suggest a framework for effective scRNA-seq studies.
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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.