ArticleNucleic acids research2019
Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 77 citations in OpenAlex.
- DR.DEGMON: self-explainable deep neural network for drug-induced cell viability prediction incorporating differentially expressed genes and gene ontology.BMC medical genomics · 2026Article
- Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis.NPJ digital medicine · 2026Article
- IL1RAP Is Associated With an Inflammation-Immunity-Related State in Skin Cutaneous Melanoma: Integrative Evidence From Pan-Cancer Data and Melanoma Immunotherapy Cohorts.International journal of genomics · 2026Article
- CycleMix: Gaussian mixture modeling of the cell cycle.Bioinformatics advances · 2026Article
- One size does not fit all: A comparative framework for optimizing ribonucleic acid transfection across acute myeloid leukemia cell lines.Biology methods & protocols · 2026Article
- VIP-OT: Dissecting Single-Cell Biochemical State Dynamics under Perturbation via Vibrational Painting and Optimal Transport.bioRxiv : the preprint server for biology · 2025Article
- Mapping disease-specific vascular cell populations responsible for obliterative arterial remodelling during the development of pulmonary arterial hypertension.Cardiovascular research · 2025Article
- RIDDEN: Data-driven inference of receptor activity from transcriptomic data.PLoS computational biology · 2025Article
- Article
- Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features.Acta pharmaceutica Sinica. B · 2025Article
- Comparative evaluation of feature reduction methods for drug response prediction.Scientific reports · 2024Article
- Toxicogenomic assessment of in vitro macrophages exposed to profibrotic challenge reveals a sustained transcriptomic immune signature.Computational and structural biotechnology journal · 2024Article
- Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.Nature communications · 2024Article
- From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability.Molecular biology of the cell · 2024Article
- Graph Structured Neural Networks for Perturbation Biology.bioRxiv : the preprint server for biology · 2024Article
- MDTR: a knowledge-guided interpretable representation for quantifying liver toxicity at transcriptomic level.Frontiers in pharmacology · 2024Article
- Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing.BMC bioinformatics · 2023Article
- Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.BMC bioinformatics · 2023Article
- Validation of transcriptome signature reversion for drug repurposing in oncology.Briefings in bioinformatics · 2023Article
- Application of perturbation gene expression profiles in drug discovery-From mechanism of action to quantitative modelling.Frontiers in systems biology · 2023Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcriptional perturbation signatures are valuable data sources for functional genomics. Linking perturbation signatures to screenings opens the possibility to model cellular phenotypes from expression data and to identify efficacious drugs. We linked perturbation transcriptomics data from the LINCS-L1000 project with cell viability information upon genetic (Achilles project) and chemical (CTRP screen) perturbations yielding more than 90 000 signature-viability pairs. An integrated analysis showed that the cell viability signature is a major factor underlying perturbation signatures. The signature is linked to transcription factors regulating cell death, proliferation and division time. We used the cell viability-signature relationship to predict viability from transcriptomics signatures, and identified and validated compounds that induce cell death in tumor cell lines. We showed that cellular toxicity can lead to unexpected similarity of signatures, confounding mechanism of action discovery. Consensus compound signatures predicted cell-specific drug sensitivity, even if the signature is not measured in the same cell line, and outperformed conventional drug-specific features. Our results can help in understanding mechanisms behind cell death and removing confounding factors of transcriptomic perturbation screens. To interactively browse our results and predict cell viability in new gene expression samples, we developed CEVIChE (CEll VIability Calculator from gene Expression; https://saezlab.shinyapps.io/ceviche/).
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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.