ArticleGenome biology2019
scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed.
- Facilitate integrated analysis of single cell multiomic data by binarizing gene expression values.Nature communications · 2025Article
- Gene function revealed at the moment of stochastic gene silencingCommunications biology · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- scQA: A dual-perspective cell type identification model for single cell transcriptome data.Computational and structural biotechnology journal · 2024Article
- Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.BMC bioinformatics · 2024Article
- Complete spatially resolved gene expression is not necessary for identifying spatial domains.Cell genomics · 2024Article
- Challenges and best practices in omics benchmarking.Nature reviews. Genetics · 2024Review
- scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data.Nature communications · 2024Article
- CellBiAge: Improved single-cell age classification using data binarization.Cell reports · 2023Article
- Consequences and opportunities arising due to sparser single-cell RNA-seq datasets.Genome biology · 2023Article
- Predictive and robust gene selection for spatial transcriptomics.Nature communications · 2023Article
- Statistics or biology: the zero-inflation controversy about scRNA-seq data.Genome biology · 2022Review
- Differential analysis of binarized single-cell RNA sequencing data captures biological variation.NAR genomics and bioinformatics · 2021Article
- Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen.Nature communications · 2021Article
- The epigenetic basis of cellular heterogeneity.Nature reviews. Genetics · 2021Review
- Correction to: scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data.Genome biology · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
Technical variation in feature measurements, such as gene expression and locus accessibility, is a key challenge of large-scale single-cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines.
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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.