ArticleNAR genomics and bioinformatics2021
Differential analysis of binarized single-cell RNA sequencing data captures biological variation.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- Article
- Differential expression analysis in single cell and spatial RNASeq without model assumptions.bioRxiv : the preprint server for biology · 2025Article
- Differential detection workflows for multi-sample single-cell RNA-seq data.BMC genomics · 2025Article
- Notch-activated basophils support intestinal CD4Mucosal immunology · 2025Article
- Facilitate integrated analysis of single cell multiomic data by binarizing gene expression values.Nature communications · 2025Article
- Clinical cell-surface targets in metastatic and primary solid cancers.JCI insight · 2024Article
- Complete spatially resolved gene expression is not necessary for identifying spatial domains.Cell genomics · 2024Article
- CellBiAge: Improved single-cell age classification using data binarization.Cell reports · 2023Article
- Differential detection workflows for multi-sample single-cell RNA-seq data.bioRxiv : the preprint server for biology · 2023Article
- DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.Genome biology · 2023Article
- Consequences and opportunities arising due to sparser single-cell RNA-seq datasets.Genome biology · 2023Article
- Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking.Life (Basel, Switzerland) · 2022Article
- Cell types as species: Exploring a metaphor.Frontiers in plant science · 2022Review
- Transcriptomics of the depressed and PTSD brain.Neurobiology of stress · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose to use binarized expression profiles to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that a binarized representation of single-cell expression data accurately represents biological variation and reveals the relative abundance of transcripts more robustly than counts.
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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.