Single-cell RNA sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. In this talk, I will discuss ConDecon, an approach for inferring the likelihood for each cell in a reference single-cell dataset to be present in a tissue that has been profiled at the bulk level, without relying on cluster labels or cell-type specific gene expression signatures. ConDecon makes use of the space of gene rank correlations to approximate the space of cell abundances. We will demonstrate the utility of ConDecon using gene expression data of pediatric ependymal tumors, where we uncover the implication of neurodegenerative microglial inflammatory pathways in the mesenchymal transformation of these tumors.