neurostatx.cli.PartialLeastSquareRegression

Attributes

Name	Description
app	Cyclopts application for the PartialLeastSquareRegression

Functions

Name	Description
PartialLeastSquareRegression	Fit partial least squares regression of node attributes against

appattributemodule attribute#

View source

app = App(default_parameter=Parameter(negative=()))

Cyclopts application for the PartialLeastSquareRegression command-line tool.

PartialLeastSquareRegressionfunction#

View source

def PartialLeastSquareRegression(in_graph: Annotated[str, Parameter(show_default=False, group='Essential Files Options')], out_folder: Annotated[str, Parameter(show_default=False, group='Essential Files Options')], attributes: Annotated[List[str], Parameter(group='Model Parameters', show_default=True)] = None, weight: Annotated[str, Parameter(group='Model Parameters', show_default=True)] = 'membership', splits: Annotated[int, Parameter(group='Model Parameters', show_default=True)] = 10, permutations: Annotated[int, Parameter(group='Model Parameters', show_default=True)] = 1000, scoring: Annotated[ScoringMethod, Parameter(group='Model Parameters', show_default=True)] = ScoringMethod.r2, processes: Annotated[int, Parameter(group='Model Parameters', show_default=True)] = 1, plot_distributions: Annotated[bool, Parameter(show_default=True, group='Visualization Options')] = False, verbose: Annotated[bool, Parameter(-v, --verbose, group='Optional parameters')] = False, save_parameters: Annotated[bool, Parameter(-s, --save_parameters, group='Optional parameters')] = False, overwrite: Annotated[bool, Parameter(-f, --overwrite, group='Optional parameters')] = False)

Fit partial least squares regression of node attributes against graph edges.

PartialLeastSquareRegression uses node attributes as predictors and edge weights as the response. It cross-validates to choose the number of components, then runs permutation testing for statistical significance. It writes PLSR coefficients, statistics, and plots of attribute and edge-weight distributions and of the coefficients.

Notes

Preprocessing

Data are scaled to unit variance and zero mean. Edge weights are log-transformed; for now this assumes the weights are membership values from a fuzzy clustering analysis.

Node attributes

The script takes a single .gexf graph file, fetches node attributes from it, and runs PLSR on those attributes and the edge weights. If no attributes are provided, all attributes in the graph are used. To attach attributes to nodes, see AddNodesAttributes.

Scoring

Permutation testing assesses whether the model is statistically significant. The p-value uses the R2 score by default; other scores are listed in the scikit-learn scoring documentation [1]. The single-tailed p-value is:

p-value = ∑(score_perm >= score) / (nb_permutations)

Coefficient significance

Coefficient p-values are also computed from the permutation distribution by comparing PLSR coefficients to permuted coefficients. The two-tailed p-value is:

p-value = ∑(abs(coef_perm) >= abs(coef)) / (nb_permutations)

References

[1] scikit-learn scoring parameter

Parameters

Name	Type	Default	Description
in_graph	str	required	Graph file containing the data for the PLSR model.
out_folder	str	required	Output folder.
attributes	List[str]	None	Attribute names to include in the PLSR model. If None are provided, all attributes will be included. Defaults to None.
weight	str	'membership'	Edge weight to use for the PLSR model. Defaults to membership.
splits	int	10	Number of splits to use for the cross-validation. Defaults to 10.
permutations	int	1000	Number of permutations to use for the permutation testing. Defaults to 1000.
scoring	ScoringMethod	ScoringMethod.r2	Scoring method to use for the permutation testing. Defaults to r2.
processes	int	1	Number of processes to use for the cross-validation. Defaults to 1.
plot_distributions	bool	False	If true, will plot the distributions of the edges’ weights. Defaults to False.
verbose	bool	False	If true, produce verbose output. Defaults to False.
save_parameters	bool	False	If true, will save input parameters to .txt file. Defaults to False.
overwrite	bool	False	If true, force overwriting of existing output files. Defaults to False.

Examples

PartialLeastSquareRegression --in-graph graph.gexf
--out-folder output_folder -v -s