Fig. 1
From: Universal prediction of cell-cycle position using transfer learning
Principal component analysis recovers time ordering in simulations. Simulations are based on cosine functions with Gaussian noise (see the “Methods” section). (a) Expression vs. time for 2 genes with different peak locations and amplitudes. Each of the two gene peaks are replicated 50 times for a total of 500 genes and 1000 time points (cells). (b) Expression vs. permuted time, representing the unknown time order of observed data which obscures the periodicity of the functions. (c) Principal component analysis of the data from (b) and (a); the two datasets have equivalent principal components. We infer cell-cycle position (\(\hat {\theta }\)) by the angle of the ellipsoid. The red dot indicates θ=0. (d) Expression vs. inferred cell-cycle position