ArticleBioinformatics (Oxford, England)2023
OutSingle: a novel method of detecting and injecting outliers in RNA-Seq count data using the optimal hard threshold for singular values.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- AXOLOTL: an accurate method for detecting aberrant gene expression in rare diseases using coexpression constraints.Bioinformatics (Oxford, England) · 2026Article
- Reduced ULK1 links impaired autophagy and mitophagy to Alzheimer's disease pathology.Nature aging · 2026Article
- saseR: juggling offsets unlocks RNA-seq tools for fast and scalable differential usage, aberrant splicing and expression retrieval.Genome biology · 2026Article
- PROTRIDER: protein abundance outlier detection from mass spectrometry-based proteomics data with a conditional autoencoder.Bioinformatics (Oxford, England) · 2025Article
- BOGO: A Proteome-Wide Gene Overexpression Platform for Discovering Rational Cancer Combination Therapies.bioRxiv : the preprint server for biology · 2025Article
- Allele Specific Expression Quality Control Fills Critical Gap in Transcriptome Assisted Rare Variant Interpretation.bioRxiv : the preprint server for biology · 2025Article
- Aberrant gene expression prediction across human tissues.Nature communications · 2025Article
- Genome and transcriptome sequencing for inborn errors of immunity: a feasible multi-omics diagnostic approach.Frontiers in immunology · 2025Article
- Denoiseit: denoising gene expression data using rank based isolation trees.BMC bioinformatics · 2024Article
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5 authors.
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Abstract
motivationFinding outliers in RNA-sequencing (RNA-Seq) gene expression (GE) can help in identifying genes that are aberrant and cause Mendelian disorders. Recently developed models for this task rely on modeling RNA-Seq GE data using the negative binomial distribution (NBD). However, some of those models either rely on procedures for inferring NBD's parameters in a nonbiased way that are computationally demanding and thus make confounder control challenging, while others rely on less computationally demanding but biased procedures and convoluted confounder control approaches that hinder interpretability.
resultsIn this article, we present OutSingle (Outlier detection using Singular Value Decomposition), an almost instantaneous way of detecting outliers in RNA-Seq GE data. It uses a simple log-normal approach for count modeling. For confounder control, it uses the recently discovered optimal hard threshold (OHT) method for noise detection, which itself is based on singular value decomposition (SVD). Due to its SVD/OHT utilization, OutSingle's model is straightforward to understand and interpret. We then show that our novel method, when used on RNA-Seq GE data with real biological outliers masked by confounders, outcompetes the previous state-of-the-art model based on an ad hoc denoising autoencoder. Additionally, OutSingle can be used to inject artificial outliers masked by confounders, which is difficult to achieve with previous approaches. We describe a way of using OutSingle for outlier injection and proceed to show how OutSingle outperforms its competition on 16 out of 18 datasets that were generated from three real datasets using OutSingle's injection procedure with different outlier types and magnitudes. Our methods are applicable to other types of similar problems involving finding outliers in matrices under the presence of confounders. AVAILABILITY AND IMPLEMENTATION: The code for OutSingle is available at https://github.com/esalkovic/outsingle.
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