-
Notifications
You must be signed in to change notification settings - Fork 15
Home
--data: data matrix columns = Samples, Rows = count categories, can include headers and index labels.
--K0: Initial number of active clusters/signatures defaults to the number of rows.
--max_iter: maximum iterations to run ard-NMF default is 10,000.
--tolerance: Early stop condition based on max lambda entry default is 1e-5.
--a: Hyperparamter. We recommend trying various values of a. Smaller values will result in sparser results a good starting point might be a = log(F+N).
--prior_on_W: Prior on W matrix "L1" (exponential) or "L2" (half-normal). Defaults to L1.
--prior_on_H: Prior on H matrix "L1" (exponential) or "L2" (half-normal). Defaults to L1.
--objective: Defines the data objective. Choose between "poisson" or "gaussian". Defaults to Poisson.
--phi: Dispersion parameter see paper for discussion of choosing phi. We default to recommended settings in Tan and Fevotte 2012.
--b: Default used is as recommended in Tan and Fevotte 2012.
--output_file: output_file_name if run in array mode this correspond to the output directory.
--labeled: Pass this argument if the data matrix has has row and column labels/headers
--report_frequency: Number of iterations between progress reports.
Each of these is also summarized in the output parameters table if run in job array mode.
W matrix: [output_file name or label column from parameters file]_W.tx. Contains signature or cluster activations.
H matrix: [output_file name or label column from parameters file]_H.txt Contains patient/sample signature or cluster activities.
Number of active clusters / signatures: [output_file name or label column from parameters file]_n_signatures.txt
Objective function value: [output_file name or label column from parameters file]_objective_function.txt
Tan, V. Y. F., Edric, C. & Evotte, F.. Automatic Relevance Determination in Nonnegative Matrix Factorization with the β-Divergence. (2012).