Package 'NetCoupler'

Convert network graphs to edge tables as tibbles/data.frames.

Description

Usage

as_edge_tbl(network_object)

Arguments

network_object

Network graph from nc_estimate_network().

Value

A tibble, with at least two columns:

• source_node: The starting node (variable).

• target_node: The ending node (variable) that links with the source node.

• adjacency_weight: (Optional) The "weight" given to the edge, which represents the strength of the link between two nodes.

See Also

See nc_estimate_links for examples on using NetCoupler.

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Classification options for direct, ambigious, and no effect.

Description

Classification options for direct, ambigious, and no effect.

Usage

classify_options(
  single_metabolite_threshold = 0.05,
  network_threshold = 0.1,
  direct_effect_adjustment = NA
)

Arguments

single_metabolite_threshold, network_threshold, direct_effect_adjustment

See the classify_option_list argument in nc_estimate_links for details.

Value

List with options for the classification.

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Compute model estimates between an external (exposure or outcome) variable and a network.

Description

This is the main function that identifies potential links between external factors and the network. There are two functions to estimate and classify links:

• nc_estimate_exposure_links(): Computes the model estimates for the exposure side.

• nc_estimate_outcome_links(): Computes the model estimates for the exposure side.

Usage

nc_estimate_exposure_links(
  data,
  edge_tbl,
  exposure,
  adjustment_vars = NA,
  model_function,
  model_arg_list = NULL,
  exponentiate = FALSE,
  classify_option_list = classify_options()
)

nc_estimate_outcome_links(
  data,
  edge_tbl,
  outcome,
  adjustment_vars = NA,
  model_function,
  model_arg_list = NULL,
  exponentiate = FALSE,
  classify_option_list = classify_options()
)

Arguments

data	The data.frame or tibble that contains the variables of interest, including the variables used to make the network.
edge_tbl	Output graph object from nc_estimate_network(), converted to an edge table using as_edge_tbl().
exposure, outcome	Character. The exposure or outcome variable of interest.
adjustment_vars	Optional. Variables to adjust for in the models.
model_function	A function for the model to use (e.g. stats::lm(), stats::glm(), survival::coxph()). Can be any model as long as the function has the arguments formula and data. Type in the model function as a bare object (without ⁠()⁠, for instance as lm).
model_arg_list	Optional. A list containing the named arguments that will be passed to the model function. A simple example would be list(family = binomial(link = "logit")) to specify that the glm model is a logistic model and not a linear one. See the examples for more on the usage.
exponentiate	Logical. Whether to exponentiate the log estimates, as computed with e.g. logistic regression models.
classify_option_list	A list with classification options for direct, ambigious, or no effects. Used with the classify_options() function with the arguments: single_metabolite_threshold: Default of 0.05. P-values from models with only the index metabolite (no neighbour adjustment) are classified as effects if below this threshold. For larger sample sizes and networks, we recommend lowering the threshold to reduce risk of false positives. network_threshold: Default of 0.1. P-values from any models that have direct neighbour adjustments are classified as effects if below this threshold. This is assumed as a one-sided p-value threshold. Like the threshold above, a lower value should be used for larger sample sizes and networks. direct_effect_adjustment: Default is NA. After running the algorithm once, sometimes it's useful to adjust for the direct effects identified to confirm whether other links exist.

Value

Outputs a tibble that contains the model estimates from either the exposure or outcome side of the network as well as the effect classification. Each row represents the "no neighbour node adjusted" model and has the results for the outcome/exposure to index node pathway. Columns for the outcome are:

• outcome or exposure: The name of the variable used as the external variable.

• index_node: The name of the metabolite used as the index node from the network. In combination with the outcome/exposure variable, they represent the individual model used for the classification.

• estimate: The estimate from the outcome/exposure and index node model.

• std_error: The standard error from the outcome/exposure and index node model.

• fdr_p_value: The False Discovery Rate-adjusted p-value from the outcome/exposure and index node model.

• effect: The NetCoupler classified effect between the index node and the outcome/exposure. Effects are classified as "direct" (there is a probable link based on the given thresholds), "ambigious" (there is a potential link but not all thresholds were passed), and "none" (no potential link seen).

The tibble output also has an attribute that contains all the models generated before classification. Access it with attr(output, "all_models_df").

See Also

vignette("examples") article has more details on how to use NetCoupler with different models.

Examples

standardized_data <- simulated_data %>%
    nc_standardize(starts_with("metabolite"))

metabolite_network <- simulated_data %>%
    nc_standardize(starts_with("metabolite"),
                   regressed_on = "age") %>%
    nc_estimate_network(starts_with("metabolite"))
edge_table <- as_edge_tbl(metabolite_network)

results <- standardized_data %>%
  nc_estimate_exposure_links(
    edge_tbl = edge_table,
    exposure = "exposure",
    model_function = lm
   )
results

# Get results of all models used prior to classification

---

Create an estimate of the metabolic network as an undirected graph.

Description

The main NetCoupler network creator. Uses the input data to estimate the underlying undirected graph. The default uses the PC algorithm, implemented within NetCoupler with pc_estimate_undirected_graph() Defaults to using the PC algorithm to calculate possible edges. Any missing values in the input data are removed by this function, since some computations can't handle missingness.

Usage

nc_estimate_network(data, cols = everything(), alpha = 0.01)

Arguments

data	Data that would form the underlying network.
cols	<tidy-select> Variables to include by using dplyr::select() style selection.
alpha	The alpha level to use to test whether an edge exists or not. Default is 0.01.

Value

Outputs a tidygraph::tbl_graph() with the start and end nodes, as well as the edge weights.

See Also

See nc_estimate_links for examples on using NetCoupler and pc_estimate_undirected_graph for more details on the PC-algorithm network estimation method.

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Standardize the metabolic variables.

Description

Can standardize by either 1) log()-transforming and then applying scale() (mean-center and scaled by standard deviation), or 2) if regressed_on variables are given, then log-transforming, running a linear regression to obtain the stats::residuals(), and finally scaled. Use regressed_on to try to remove influence of potential confounding.

Usage

nc_standardize(data, cols = everything(), regressed_on = NULL)

Arguments

data	Data frame.
cols	Metabolic variables that will make up the network.
regressed_on	Optional. A character vector of variables to regress the metabolic variables on. Use if you want to standardize the metabolic variables on variables that are known to influence them, e.g. sex or age. Calculates the residuals from a linear regression model.

Value

Outputs a tibble object, with the original metabolic variables now standardized.

See Also

nc_estimate_links for more detailed examples or the vignette("NetCoupler").

Examples

# Don't regress on any variable
simulated_data %>%
  nc_standardize(starts_with("metabolite_"))

# Extract residuals by regressing on a variable
simulated_data %>%
  nc_standardize(starts_with("metabolite_"), "age")

# Works with factors too
simulated_data %>%
  dplyr::mutate(Sex = as.factor(sample(rep(c("F", "M"), times = nrow(.) / 2)))) %>%
  nc_standardize(starts_with("metabolite_"), c("age", "Sex"))

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Simulated dataset with an underlying Directed Graph structure for the metabolites.

Description

Simulated dataset with an underlying Directed Graph structure for the metabolites.

Usage

simulated_data

Format

The simulated dataset is a tibble with the following variables:

• Two outcome variables (outcome_continuous and outcome_binary) along with survival time (outcome_event_time) that is used for the outcome_binary variable

• A generic exposure variable as continuous

• 12 ⁠metabolite_*⁠ variables

• An age variable used as a confounder

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