Figure 2

A comparison of parameters estimated by the MATLAB and R implementations of ISOpure for the Beer dataset. Each plot shows the entries of a parameter estimated using ISOpureR plotted against the corresponding entries estimated using the MATLAB code. The parameter is an average over 49 models run with different initial conditions (one MATLAB model for the Beer dataset resulted in a zero θ value, and was dropped). The line y=x is indicated in black, and the linear regression line, or robust regression line for θ, is dashed orange. (A) Parameters from the Cancer Profile Estimation step of ISOpure: (i) ν, the hyper-parameter for the Dirichlet distribution over θ, (ii) θ, the proportion of a patient sample from a known healthy-tissue profile, (iii) m, the average mRNA abundance cancer profile, (iv) α, the fraction of cancer cells for every patient sample, (v) ω a hyper-parameter for the Dirichlet distribution over m. (B) Parameters from the Patient Profile Estimation step of ISOpure: (i) ν, the hyper-parameter for the Dirichlet distribution over θ, (ii) θ, the proportion of a patient sample from a known healthy-tissue profile, (iii) c _n , the purified mRNA abundance cancer profile for each patient.