CONCLAVE: CONsensus CLustering with Annotation-Validation Extrapolation for cyclic multiplexed immunofluorescence data

Abstract

High-dimensional cyclic multiplexed immunofluorescence (cMIF) enables single-cell phenotyping within intact tissues. Cell annotations rely on a multi-step pipeline involving normalization, sampling, dimensionality reduction, and clustering, but the absence of standardized benchmarks for method selection--especially at the clustering stage--leads to inconsistent and less reproducible phenotyping. To address this, we developed CONCLAVE, a consensus-clustering-based workflow that optimizes upstream steps and integrates results from multiple clustering algorithms retaining only those cell labels supported by at least two independent methods. Through in-silico simulations and real-world cMIF datasets, CONCLAVE consistently outperformed single-clustering-method approaches in accuracy, reproducibility, and robustness, with improvements becoming more evident when mapped within spatial tissue contexts. Additionally, CONCLAVE includes a scoring module that flags regions likely to contain unreliable or inconsistent data, facilitating targeted quality control. In summary, CONCLAVE offers a robust framework for cell annotation in cMIF datasets, enhancing the reliability of downstream spatial proteomics analyses. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/688190v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@14ddfecorg.highwire.dtl.DTLVardef@1a83a17org.highwire.dtl.DTLVardef@17dfc74org.highwire.dtl.DTLVardef@493c07_HPS_FORMAT_FIGEXP M_FIG C_FIG