ConDecon is a clustering-independent deconvolution method for estimating cell abundances in bulk tissues using single-cell RNA-seq data. The aim of ConDecon is to infer a probability distribution across a reference single-cell dataset that represents the likelihood for each cell in the reference data to be present in the query bulk tissue. ConDecon enables previously elusive analyses of dynamic cellular processes in bulk tissues and represents an increase in functionality and phenotypic resolution with respect to current methods for gene expression deconvolution. We anticipate that these features will improve our understanding of tissue cell composition by facilitating the inference of cell state abundances within complex bulk tissues, particularly in the context of evolving systems like development and disease progression. In the ConDecon GitHub repository, we demonstrate ConDecon's utility by applying it to transcriptomic data (ConDecon_B_cell_Tutorial), spatial transcriptomic data (ConDecon_Spatial_RNA_Tutorial), and chromatin accessibility data (ConDecon_ATAC_Tutorial). These datasets were downloaded from open-source data repositories (referenced below) and re-processed by the Camara lab. For convenience, the re-processed data associated with these tutorials has been uploaded below. Aubin, R. G., Montelongo, J., Hu, R., Camara, P. G. Clustering-independent estimation of cell abundances in bulk tissue using single-cell RNA-seq data. Biorxiv (2023).

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