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analysis_config.yaml
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analysis_config.yaml
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# This directory (*ABSOLUTE* path) will contain the intermediate
# results for all of the analyses.
# The default empty string ("") creates a "temp" subdirectory in the current working directory.
# You may need to change it to fit your circumstances,
# e.g., "/scratch" or wherever you have plenty of disk available.
temp_dir: ""
# Define the simulation study
simulation_study:
# the grid of simulated datasets
simulation_grid:
V: [40, 100, 200] # number of variables
R: [0.1, 0.5, 0.75, 1.0] # fraction of edges to remove
A: [0.1, 0.5, 0.75, 1.0] # fraction of new edges to add
T: [8] # length of each time series
M: 4 # number of time series
# polynomial degree used by autoregression *data generator*
polynomial_degree: 3
# Number of simulations at each grid point
convergence:
timeout: 43200
stop_points: [100, 1000, 5000, 10000, 20000, 50000, 100000]
psrf_ub: 1.1
neff_per_chain: 10.0
n_chains: 4
N: 5
prediction:
timeout: 43200
n_chains: 4
N: 5
# the grid of MCMC hyperparameters
mcmc_hyperparams:
burnin: 0.5
regression_deg: [1]
max_samples: 100000
lambda_prop_std: [3.0]
large_indeg: 15
baseline_methods: ["funchisq", "lasso", "hill", "prior_baseline", "uniform"]
dream_challenge:
cell_lines: ["BT20", "BT549", "MCF7", "UACC812"]
stimuli: ["EGF", "FGF1", "HGF", "IGF1", "Insulin", "NRG1", "PBS", "Serum"]
N: 5
convergence:
stop_points: [100, 1000, 5000, 10000, 20000, 50000, 100000]
psrf_ub: 1.1
neff_per_chain: 10.0
mcmc_hyperparams:
timeout: 43200
burnin: 0.5
regression_deg: [1]
max_samples: 100000
lambda_prop_std: [3.0]
large_indeg: 15
n_chains: 4
baseline_methods: ["hill", "funchisq", "lasso", "prior_baseline"]
hill_timetest:
modes: ["full"] # ["linear", "quadratic", "full"]
deg_v_combs: [[1,40], [1,100], [1,200],
[2,40], [2,100], [2,200],
[3,40], [3,100], [3,200],
[4,40], #[4,100], [4,200],
[5,40], #[5,100], [5,200],
[6,40]] #,
#[7,40]]
timeout: 43200
mem_mb: 32000