{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Magness A"],"funding":["Cancer Research UK","European Research Council","The Francis Crick Institute","Royal Society","Wellcome Trust","EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)"],"pagination":["5135"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11180132"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (TYPEx) and interpretable spatial analysis (Spatial-PHLEX) as three independent but interoperable modules. PHLEX generates single-cell identities, cell densities within tissue compartments, marker positivity calls and spatial metrics such as cellular barrier scores, along with summary graphs and spatial visualisations. PHLEX was developed using imaging mass cytometry (IMC) in the TRACERx study, validated using published Co-detection by indexing (CODEX), IMC and orthogonal data and benchmarked against state-of-the-art approaches. We evaluated its"],"journal":["Nature communications"],"pubmed_title":["Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX."],"pmcid":["PMC11180132"],"funding_grant_id":["C416/A21999","101019366","CC2040","CC2041","RF\\ERE\\231118","838540","RF\\ERE\\210216","101079113"],"pubmed_authors":["Swanton C","Levi D","Hiley CT","Reading JL","Magness A","van Maldegem F","Downward J","Quezada SA","Lee C","Moore DA","Sivakumar M","Colliver E","Sahai E","Shimato M","Valand K","Enfield KSS","Daly E","Hobson PS","Angelova M"],"additional_accession":[]},"is_claimable":false,"name":"Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX.","description":"The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (TYPEx) and interpretable spatial analysis (Spatial-PHLEX) as three independent but interoperable modules. PHLEX generates single-cell identities, cell densities within tissue compartments, marker positivity calls and spatial metrics such as cellular barrier scores, along with summary graphs and spatial visualisations. PHLEX was developed using imaging mass cytometry (IMC) in the TRACERx study, validated using published Co-detection by indexing (CODEX), IMC and orthogonal data and benchmarked against state-of-the-art approaches. We evaluated its","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jun","modification":"2026-06-02T21:22:19.538Z","creation":"2026-04-20T03:14:16.759Z"},"accession":"S-EPMC11180132","cross_references":{"pubmed":["38879602"],"doi":["10.1038/s41467-024-48870-5"]}}