ArticlePLoS computational biology2012
PERT: a method for expression deconvolution of human blood samples from varied microenvironmental and developmental conditions.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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Who cites it
70 citing papers in PubMed.
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Cell type inference in cell-free nucleic acid liquid biopsy.Nature biotechnology · 2025Review
- Computationally resolved neuroprogenitor cell biomarkers associate with human disorders.Stem cell reports · 2025Article
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- imply: improving cell-type deconvolution accuracy using personalized reference profiles.Genome medicine · 2024Article
- Article
- Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures.Nature communications · 2023Article
- Deconvolution of bulk gene expression profiles reveals the association between immune cell polarization and the prognosis of hepatocellular carcinoma patients.Cancer medicine · 2023Article
- Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells.International journal of molecular sciences · 2023Article
- Bioinformatics Tools for Bulk Gene Expression Deconvolution in Diabetic Retinopathy.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Tumor microenvironment: barrier or opportunity towards effective cancer therapy.Journal of biomedical science · 2022Review
- FastMix: a versatile data integration pipeline for cell type-specific biomarker inference.Bioinformatics (Oxford, England) · 2022Article
- GEOMETRIC STRUCTURE GUIDED MODEL AND ALGORITHMS FOR COMPLETE DECONVOLUTION OF GENE EXPRESSION DATA.Foundations of data science (Springfield, Mo.) · 2022Article
- Cell types of origin of the cell-free transcriptome.Nature biotechnology · 2022Article
- Comprehensive evaluation of deconvolution methods for human brain gene expression.Nature communications · 2022Article
- Feasibility of a novel non-invasive swab technique for serial whole-exome sequencing of cervical tumors during chemoradiation therapy.PloS one · 2022Article
- An interferon-related signature characterizes the whole blood transcriptome profile of insulin-resistant individuals-the CODAM study.Genes & nutrition · 2021Article
- AdRoit is an accurate and robust method to infer complex transcriptome composition.Communications biology · 2021Article
- Article
- Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence.Signal transduction and targeted therapy · 2021Review
10 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity.
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