control_adjust_batch <- list(
# logical. Whether or not a zero-inflated model should be
# run. Default to TRUE (zero-inflated model). If set to FALSE then ComBat
# (with parametric adjustment) will be performed.
zero_inflation = TRUE,
# numeric. Pseudo count to add feature_abd before
# log-transformation. Automatically set to half of minimal non-zero value if
# not specified.
pseudo_count = NULL,
# character (or null). Name of diagnostic plot file.
diagnostic_plot = "adjust_batch_diagnostic.pdf",
conv = 1e-4,
maxit = 1000,
# logical. Whether or not verbose modeling information is printed.
verbose = TRUE
)
control_lm_meta <- list(
# character. Normalization parameter for Maaslin2.
normalization = "TSS",
# character. Transformation parameter for Maaslin2.
transform = "AST",
# character. Analysis method parameter for Maaslin2.
analysis_method = "LM",
# character. Method parameter for rma.
rma_method = "REML",
rma_conv = 1e-4,
rma_maxit = 1000,
# character. Output directory for Maaslin2 output and forest plots.
output = "MMUPHin_lm_meta/",
# character (or null). Suffix of forest plot file.
forest_plot = "forest.pdf",
# logical. Whether or not verbose modeling information is printed.
verbose = TRUE
)
control_discrete_discover <- list(
# integer. Maximum number of clusters to perform/evaluate on.
k_max = 10,
# function. Clustering function.
cluster_function = fpc::claraCBI,
# character. Needs to be either "centroid" or "knn"
classify_method = "centroid",
# integer. Number of randomized iterations to run for prediction strength.
M = 30,
# intger. Number of nearest neighbors when using method knn
nnk = 1,
diagnostic_plot = "discrete_diagnostic.pdf",
verbose = TRUE
)
control_continuous_discover <- list(
normalization = "TSS",
# transformation parameter.
transform = "AST",
# pseudo count to add to the count table before log-transformation.
pseudo_count = NULL,
# percentage variance explained cutoff to choose the top PCs.
var_perc_cutoff = 0.8,
# correlation cutoff to construct edges for the PC network.
cos_cutoff = 0.5,
# function used to perform network community structure discovery.
cluster_function = igraph::cluster_optimal,
# output file for clustered PC network.
network_plot = "clustered_network.pdf",
# cluster size cutoff (for cluster to be included in the visualized network
plot_size_cutoff = 2,
# output file for diagnostic plot.
diagnostic_plot = "continuous_diagnostic.pdf",
verbose = TRUE
)
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