multideconv - an integrative pipeline for combining first and second generation cell type deconvolution results
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Summary
The number of computational methods for cell type deconvolution from bulk RNA-seq data has been increasing in the last years, but their high feature complexity and variability of results across methods and signatures limit their utility and effectiveness for patient stratification. Applying multiple combinations of deconvolution methods and signatures often results in hundreds of redundant or contradictory cell type features describing the composition of complex tumour samples. Benchmarking efforts are inherently limited by the lack of bias-free ground truth, often yielding inconsistent results or no consensus. To address these limitations, we present multideconv , an R package that reduces dimensionality and eliminates redundancy in deconvolution results, through unsupervised filtering and iterative correlation analyses. Built on top of existing frameworks, multideconv harmonizes outputs across methods to identify robust cell type proportion estimates and mitigate signature-driven heterogeneity.
Availability and implementation
The multideconv R package and tutorials are available at https://github.com/VeraPancaldiLab/multideconv . The code to reproduce the analysis and figures is available on github at https://github.com/VeraPancaldiLab/multideconv_paper . The Mariathasan et al. datasets used to showcase the method can be found in the IMVigor210Biologies R Package. The Gide et al. bulk RNA dataset is available in the European Nucleotide Archive (ENA) under accession number PRJEB23709. The scRNAseq LUAD cohort (Vanderbilt) data is available on Zenodo under accession number 7878082.
Contact
marcelo.hurtado@inserm.fr or vera.pancaldi@inserm.fr
Supplementary information
Supplementary materials are available at Supplementary materials - multideconv
