Figure 2

Simulation results from sparse global latent structure model. In every simulation, 2000 simulated genes were generated from a latent variable model with 20 latent factors. The latent factors were either independent Gaussian (two right columns), or randomly chosen from a mixture of four types (two left columns). Gaussian random noise was added to achieve different signal to noise ratios (columns), and different levels of sparsity were tested (rows). An additional 500 pure noise genes were generated from the standard Gaussian distribution. Each simulation setting was repeated 100 times. The success of latent factor recovery was evaluated by the R² values obtained by the regression of each latent factor against the identified factors that are most correlated with it. The relative frequencies (10 equal-sized bins between 0 and 1, equivalent to the histogram) of the R² values are plotted.