Discovering reference-missing cell types in bulk transcriptomics.

Bulk RNA-seq deconvolution methods rely on single-cell reference data to estimate cell-type proportions in heterogeneous tissues, but certain cell types may be systematically absent from single-cell references due to technical limitations such as poor dissociation efficiency, low capture rates, or cell fragility. While recent studies have shown that signatures of missing cell types persist in deconvolution residuals, existing approaches cannot automatically determine how many cell types are missing, estimate their specific proportions, or reconstruct their expression signatures. Here, we present DeconX, a computational framework that addresses these limitations by generating "pseudo-cells" from deconvolution residuals, enabling estimation of the number, proportions, and expression signatures of missing cell types. Through comprehensive evaluation on simulated datasets, we demonstrate that DeconX accurately recovers missing cell-type proportions and expression profiles across varying conditions, and systematically identifies key factors affecting performance, including expression similarity between missing and reference cell types and missing cell-type proportions. Application to high-grade serous ovarian cancer (HGSOC) samples reasonably identifies and quantifies adipocyte populations that are absent from matched single-cell references, recovering biologically interpretable expression signatures consistent with known adipocyte markers. We further demonstrate that DeconX can reasonably determine the number of missing cell types, supporting automated analysis of unknown tissue compositions. DeconX transforms residual-based missing cell-type inference from exploratory analysis into a complete computational framework, enabling accurate characterization of tissue heterogeneity from archival bulk RNA-seq data even when single-cell references are incomplete. The source code is available at https://github.com/Seniorious123/DeconX/, and the documentation and tutorials are available at https://deconx.readthedocs.io.