Predicting Immune Cell Composition Using Linear Programming

A large data set is used to infer the fraction of immune cells using gene expression data. A model should be developed to infer the fraction of immune cells from a small set of genes used in cell surface marker experiments. We present a model created by linear programming (LP) by simplex method. To evaluate the accuracy of the algorithm, we created a simulated data set and evaluated its performance against the digital cell quantization (DCQ) algorithm. Finally, we applied our LP method to the systemic lupus erythematosus (SLE) patient dataset and examined the differences from healthy controls.