Allows creating a global `settings` variable used in DrDimont's
run_pipeline function and step-wise execution.
Default parameters can be changed within the function call.
drdimont_settings(
saving_path = tempdir(),
save_data = FALSE,
correlation_method = "spearman",
handling_missing_data = "all.obs",
reduction_method = "pickHardThreshold",
r_squared_cutoff = 0.85,
cut_vector = seq(0.2, 0.8, by = 0.01),
mean_number_edges = NULL,
edge_density = NULL,
p_value_adjustment_method = "BH",
reduction_alpha = 0.05,
n_threads = 1,
parallel_chunk_size = 10^6,
print_graph_info = TRUE,
conda = FALSE,
max_path_length = 3,
int_score_mode = "auto",
cluster_address = "auto",
median_drug_response = FALSE,
absolute_difference = FALSE,
...
)Named list of the settings for the pipeline
[string] Path to save intermediate output of DrDimont's functions. Default is temporary folder.
[bool] Save intermediate data such as correlation_matrices, individual_graphs, etc. during exectution of DrDimont. (default: FALSE)
["pearson"|"spearman"|"kendall"]
Correlation method used for graph generation. Argument is passed to cor.
(default: spearman)
["all.obs"|"pairwise.complete.obs"]
Method for handling of missing data during correlation matrix computation. Argument is passed
to cor. Can be a single character string if the same for all layers, else
a named list mapping layer names to methods, e.g,
handling_missing_data=list(mrna="all.obs", protein="pairwise.complete.obs").
Layers may be omitted if a method is mapped to `default`, e.g,
handling_missing_data=list(default="pairwise.complete.obs"). (default: all.obs)
["pickHardThreshold"|"p_value"]
Reduction method for reducing networks. `p_value` for hard thresholding based on the statistical
significance of the computed correlation. `pickHardThreshold` for a cutoff based on the scale-freeness
criterion (calls pickHardThreshold). Can be a single character string if the same
for all layers, else a named list mapping layer names to methods (see handling_missing_data setting).
Layers may be omitted if a method is mapped to `default`. (default: pickHardThreshold)
pickHardThreshold setting: [float|named list]
Minimum scale free topology fitting index R^2 for reduction using
pickHardThreshold.
Can be a single float number if the same for all layers, else a named list mapping layer names to a cutoff
(see handling_missing_data setting) or a named list in a named list mapping groupA or groupB and layer
names to a cutoff, e.g.,
r_squared_cutoff=list(groupA=list(mrna=0.85, protein=0.8), groupB=list(mrna=0.9, protein=0.85)).
Layers/groups may be omitted if a cutoff is mapped to `default`. (default: 0.85)
pickHardThreshold setting: [sequence of float|named list]
Vector of hard threshold cuts for which the scale free topology fit indices are calculated during
reduction with pickHardThreshold.
Can be a single regular sequence if the same for all layers, else a named list mapping layer names
to a cut vector or a named list in a named list mapping groupA or groupB and layer names to a cut
vector (see r_squared_cutoff setting). Layers/groups may be omitted if a vector is mapped
to `default`. (default: seq(0.2, 0.8, by = 0.01))
pickHardThreshold setting: [int|named list]
Maximal mean number edges threshold to find a suitable edge weight cutoff employing
pickHardThreshold to reduce the network to at most the specified mean number of edges.
Can be a single int number if the same for all layers, else a named list mapping layer names to a mean number of edges or
a named list in a named list mapping groupA or groupB and layer names to a cutoff (see r_squared_cutoff setting).
Attention: This parameter overwrites the 'r_squared_cutoff' and 'edge_density' parameters if not set to NULL. (default: NULL)
pickHardThreshold setting: [float|named list]
Maximal network edge density to find a suitable edge weight cutoff employing
pickHardThreshold to reduce the network to at most the specified edge density.
Can be a single float number if the same for all layers, else a named list mapping layer names to a mean number of edges or
a named list in a named list mapping groupA or groupB and layer names to a cutoff (see r_squared_cutoff setting).
Attention: This parameter overwrites the 'r_squared_cutoff' parameter if not set to NULL. (default: NULL)
p_value setting: ["holm"|"hochberg"|"hommel"|"bonferroni"|"BH"|"BY"|"fdr"|"none"] Correction method applied to p-values. Passed to p.adjust. (default: "BH")
p_value setting: [float] Significance value for correlation p-values during reduction. Not-significant edges are dropped. (default: 0.05)
p_value setting: [int] Number of threads for parallel computation of p-values during p-value reduction. (default: 1)
p_value setting: [int] Number of p-values in smallest work unit when computing in parallel during network reduction with method `p_value`. (default: 10^6)
[bool] Print summary of the reduced graph to the console after network generation. (default: TRUE)
[bool] Python installation in conda environment. Set TRUE if Python is installed with conda. (default: FALSE)
[int]
Integer of maximum length of simple paths to include in the
generate_interaction_score_graphs computation. (default: 3)
["auto"|"sequential"|"ray"] Interaction score sequential or parallel ("ray") computation. For parallel computation the Python library Ray ist used. When set to `auto` computation depends on the graph sizes. (default: "auto")
[string] Local node IP-address of Ray if executed on a cluster.
On a cluster: Start ray with ray start --head --num-cpus 32 on the console before DrDimont execution.
It should work with "auto", if it does not specify IP-address given by the ray start command. (default: "auto")
[bool] Computation of median (instead of mean) of a drug's targets differential scores (default: FALSE)
[bool] Computation of drug response scores based on absolute differential scores (instead of the actual differential scores) (default: FALSE)
Supply additional settings.
settings <- drdimont_settings(
correlation_method="spearman",
handling_missing_data=list(
default="pairwise.complete.obs",
mrna="all.obs"),
reduction_method="pickHardThreshold",
max_path_length=3)
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