Fig. 1

MATCHER method overview. a We infer manifold representations of each dataset using a Gaussian process latent variable model (GPLVM). However, the resulting “pseudotime” values from different genomic data types are not directly comparable due to differences in orientation, scale, and “time warping.” Both the color of the curve (black to yellow) and cell morphology (blob to oblong) indicate position within this hypothetical process. b, c To account for these effects, pseudotime for each kind of data is modeled as a non-linear function (warping function) of master time using a Gaussian process. d MATCHER infers “master time” in which the steps of a biological process correspond to values uniformly distributed between 0 and 1 and are comparable among different data types. However, different datasets are measured from different physical cells and thus may sample different points in the biological process and even different numbers of cells. e Diagram showing how MATCHER’s generative model can infer corresponding cell measurements. The generated cell is drawn with transparency to indicate that this is an inferred rather than observed quantity. f Applying MATCHER to multiple types of data provides exactly corresponding measurements from observed cells and unobserved cells (indicated with transparency) generated by MATCHER