The problem. Deconvolution leans on a single-cell reference, but the reference and the spatial assay are measured on different platforms, with different biases. Ignore that gap and you get confident, wrong cell-type maps. RCTD’s central claim is that handling this platform effect is what separates a robust method from a fragile one.

The idea. RCTD (Robust Cell Type Decomposition, shipped as spacexr) fits a statistical model that learns cell-type profiles from a reference and estimates each spot’s mixture while explicitly estimating and correcting the systematic platform difference between reference and spatial data. It runs in modes suited to the assay’s resolution, from near-single-cell (one or two cell types per pixel) to lower-resolution spots, so it degrades gracefully rather than assuming one cells-per-spot regime.

Why it matters. Read alongside cell2location, this is the instructive contrast: cell2location leans on full hierarchical Bayesian modeling of technical variation; RCTD foregrounds the reference-vs-spatial platform mismatch as the thing to correct. Same problem, two philosophies, and understanding both is what lets me pick, and justify, one for a given dataset rather than defaulting to whatever’s popular. That “know why, not just which” is the standard I want for every pipeline choice.

Verdict. Robust and widely used for good reasons, and unusually clear about what it’s correcting for, which I appreciate in a method. Same honest ceiling as all deconvolution, it needs a decent reference and is aimed at spot/near-spot data, with segmentation the better frame for true single-cell imaging. This closes the deconvolution cluster; segmentation is where the reading goes next.