ArticleBioinformatics (Oxford, England)2019
Dissecting differential signals in high-throughput data from complex tissues.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues.bioRxiv : the preprint server for biology · 2026Article
- Cell-type-specific and inflammatory DNA methylation patterns associated with PTSD.Brain, behavior, and immunity · 2025Article
- Early establishment and life course stability of sex biases in the human brain transcriptome.Cell genomics · 2025Article
- Detection of cell-type-specific differentially methylated regions in epigenome-wide association studies.Bioinformatics (Oxford, England) · 2025Article
- Unified high-resolution immune cell fraction estimation in blood tissue from birth to old age.Genome medicine · 2025Article
- A Multicellular In Vitro Model of the Human Intestine with Immunocompetent Features Highlights Host-Pathogen Interactions During Early Salmonella Typhimurium Infection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Reference-free deconvolution of complex samples based on cross-cell-type differential analysis: Systematic evaluations with various feature selection options.Frontiers in genetics · 2025Article
- cypress: an R/Bioconductor package for cell-type-specific differential expression analysis power assessment.Bioinformatics (Oxford, England) · 2024Article
- ACCURATE ESTIMATION OF RARE CELL-TYPE FRACTIONS FROM TISSUE OMICS DATA VIA HIERARCHICAL DECONVOLUTION.The annals of applied statistics · 2024Article
- imply: improving cell-type deconvolution accuracy using personalized reference profiles.Genome medicine · 2024Article
- Multimodal joint deconvolution and integrative signature selection in proteomics.Communications biology · 2024Article
- Regional analysis to delineate intrasample heterogeneity with RegionalST.Bioinformatics (Oxford, England) · 2024Article
- Article
- ISLET: individual-specific reference panel recovery improves cell-type-specific inference.Genome biology · 2023Observational
- Genes critical for development and differentiation of dopaminergic neurons are downregulated in Parkinson's disease.Journal of neural transmission (Vienna, Austria : 1996) · 2023Article
- CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data.Genome biology · 2023Article
- Nucleated red blood cells explain most of the association between DNA methylation and gestational age.Communications biology · 2023Article
- A comprehensive assessment of cell type-specific differential expression methods in bulk data.Briefings in bioinformatics · 2023Article
- Cell-Type Deconvolution of Bulk DNA Methylation Data with EpiSCORE.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Deep learning in bioinformatics.Turkish journal of biology = Turk biyoloji dergisi · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationSamples from clinical practices are often mixtures of different cell types. The high-throughput data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis, and will lead to biased results if not properly accounted for.
resultsWe develop a method to model the high-throughput data from mixed, heterogeneous samples, and to detect differential signals. Our method allows flexible statistical inference for detecting a variety of cell-type specific changes. Extensive simulation studies and analyses of two real datasets demonstrate the favorable performance of our proposed method compared with existing ones serving similar purpose. AVAILABILITY AND IMPLEMENTATION: The proposed method is implemented as an R package and is freely available on GitHub (https://github.com/ziyili20/TOAST). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.