ArticleGenome medicine2023
Not every estimate counts - evaluation of cell composition estimation approaches in brain bulk tissue data.
Article in Genome medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Single-nucleus transcriptomics-based drug screening platform for focal cortical dysplasia.Molecular psychiatry · 2026Article
- Cardiac epigenome in heart development and disease.Nature reviews. Cardiology · 2026Review
- CHAS infers cell type-specific signatures in bulk brain histone acetylation studies of neurological and psychiatric disorders.Cell reports methods · 2025Article
- Single-nucleus transcriptomics reveals disease- and pathology-specific signatures in α-synucleinopathies.Brain : a journal of neurology · 2025Article
- lute: estimating the cell composition of heterogeneous tissue with varying cell sizes using gene expression.BMC genomics · 2025Article
- Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics.Science advances · 2024Article
- Mitochondrial complex I deficiency stratifies idiopathic Parkinson's disease.Nature communications · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVariation in cell composition can dramatically impact analyses in bulk tissue samples. A commonly employed approach to mitigate this issue is to adjust statistical models using estimates of cell abundance derived directly from omics data. While an arsenal of estimation methods exists, the applicability of these methods to brain tissue data and whether or not cell estimates can sufficiently account for confounding cellular composition has not been adequately assessed.
methodsWe assessed the correspondence between different estimation methods based on transcriptomic (RNA sequencing, RNA-seq) and epigenomic (DNA methylation and histone acetylation) data from brain tissue samples of 49 individuals. We further evaluated the impact of different estimation approaches on the analysis of H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-seq) data from entorhinal cortex of individuals with Alzheimer's disease and controls.
resultsWe show that even closely adjacent tissue samples from the same Brodmann area vary greatly in their cell composition. Comparison across different estimation methods indicates that while different estimation methods applied to the same data produce highly similar outcomes, there is a surprisingly low concordance between estimates based on different omics data modalities. Alarmingly, we show that cell type estimates may not always sufficiently account for confounding variation in cell composition.
conclusionsOur work indicates that cell composition estimation or direct quantification in one tissue sample should not be used as a proxy to the cellular composition of another tissue sample from the same brain region of an individual-even if the samples are directly adjacent. The highly similar outcomes observed among vastly different estimation methods, highlight the need for brain benchmark datasets and better validation approaches. Finally, unless validated through complementary experiments, the interpretation of analyses outcomes based on data confounded by cell composition should be done with great caution, and ideally avoided all together.
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