ArticleBioinformatics (Oxford, England)2020
A Bayesian approach to accurate and robust signature detection on LINCS L1000 data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases.Briefings in bioinformatics · 2024Article
- Data Valuation with Gradient Similarity.ArXiv · 2024Article
- Graph Structured Neural Networks for Perturbation Biology.bioRxiv : the preprint server for biology · 2024Article
- Regulating the cell shift of endothelial cell-like myofibroblasts in pulmonary fibrosis.The European respiratory journal · 2023Article
- Hierarchical multi-omics data integration and modeling predict cell-specific chemical proteomics and drug responses.Cell reports methods · 2023Article
- Repurposing ibudilast to mitigate Alzheimer's disease by targeting inflammation.Brain : a journal of neurology · 2023Article
- DrSim: Similarity Learning for Transcriptional Phenotypic Drug Discovery.Genomics, proteomics & bioinformatics · 2022Article
- Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer's disease drug repurposing.PLoS computational biology · 2022Article
- Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing.Patterns (New York, N.Y.) · 2022Article
- Cell-specific gene association network construction from single-cell RNA sequence.Cell cycle (Georgetown, Tex.) · 2021Article
- A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing.Nature machine intelligence · 2021Article
- Predicting mechanism of action of cellular perturbations with pathway activity signatures.Bioinformatics (Oxford, England) · 2020Article
- A deep learning framework for high-throughput mechanism-driven phenotype compound screening.bioRxiv : the preprint server for biology · 2020Article
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4 authors.
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Abstract
motivationLINCS L1000 dataset contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, the existing peak deconvolution algorithms cannot recover the accurate expression level of genes in many cases, inducing severe noise in the dataset and limiting its applications in biomedical studies.
resultsHere, we present a novel Bayesian-based peak deconvolution algorithm that gives unbiased likelihood estimations for peak locations and characterize the peaks with probability based z-scores. Based on the above algorithm, we build a pipeline to process raw data from L1000 assay into signatures that represent the features of perturbagen. The performance of the proposed pipeline is evaluated using similarity between the signatures of bio-replicates and the drugs with shared targets, and the results show that signatures derived from our pipeline gives a substantially more reliable and informative representation for perturbagens than existing methods. Thus, the new pipeline may significantly boost the performance of L1000 data in the downstream applications such as drug repurposing, disease modeling and gene function prediction. AVAILABILITY AND IMPLEMENTATION: The code and the precomputed data for LINCS L1000 Phase II (GSE 70138) are available at https://github.com/njpipeorgan/L1000-bayesian. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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