API reference / Analysis and models

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metbit.lazy_opls_da

Analysis and models module in metbit 9.0.0.

import metbit.lazy_opls_da

Classes

lazy_opls_da

Parameters

dataDataFrame containing the dataset.

groupsList of class labels for each data sample.

working_dirDirectory path for storing output files.

feature_namesNames of features, defaults to None.

n_componentsNumber of components for OPLS-DA, defaults to 2.

scalingScaling method ('pareto'), defaults to 'pareto'.

estimatorModel estimator, defaults to 'opls'.

kfoldNumber of folds in cross-validation, defaults to 3.

random_stateRandom seed, defaults to 94.

auto_ncompAutomatically choose the optimal number of components, defaults to True.

permutationConduct permutation tests, defaults to True.

VIPCalculate VIP scores, defaults to True.

linear_regressionConduct linear regression analysis, defaults to True.

Returns

A printout of the model summary, including the project name, dataset information, configuration, and directory paths.

fit Method

Fits the OPLS-DA model to the dataset, generates plots, and saves them to the output directory.

Parameters

marker_colorDictionary mapping groups to colors.

custom_colorCustom color grouping.

custom_shapeCustom shape grouping.

symbol_dictDictionary mapping groups to marker symbols.

custom_legend_nameCustom for the legend, defaults to ['Group', 'Sub-group'].

marker_labelSpecifies marker labels ('class', 'group', or 'sub-group').

marker_sizeSize of markers in plots.

marker_opacityOpacity level of markers in plots.

individual_ellipseOption to display individual ellipses for each group.

Returns

A message indicating the model fitting was successful.

Directory and Project Setup =========================== Creates necessary folders in the working directory based on project needs (e.g., for VIP score plots, permutation scores, etc.). Paths are stored in a dictionary (self.path).

Directories Created

working_dir/project_name/element/plots/... for different plots.

working_dir/project_name/element/data/... for data outputs.

Plotting and Saving Data

1. Score Plot: Generates OPLS-DA score plots for each group. 2. Loading Plot: Generates and saves loading plots. 3. S Plot: Generates and saves S-score plots. 4. VIP Score Plot: Generates VIP score plots and saves VIP scores as CSV if VIP=True. 5. Permutation Test Plot: Conducts permutation tests and saves permutation scores as CSV if permutation=True. 6. Volcano Plot (Linear Regression): Generates volcano plot and saves data if linear_regression=True.

Methods

__init__(self, data: pd.DataFrame, groups: list, working_dir: str, feature_: list=None, n_components: int=2, scaling: str='pareto', estimator: str='opls', kfold: int=3, random_state: int=94, auto_ncomp: bool=True, permutation: bool=True, n_permutation: int=500, n_jobs: int=4, VIP: bool=True, VIP_threshold: float=1.5, linear_regression: bool=True, FC_threshold: float=1.5, p_val_threshold: float=2)

fit(self, marker_color: dict=None, custom_color: list=None, custom_shape: list=None, symbol_dict: dict=None, custom_legend_name=['Group', 'Sub-group'], marker_label=None, marker_size=None, marker_opacity=None, individual_ellipse=False)

Source

metbit/lazy_opls_da.py at v9.0.0

metbit.lazy_opls_da

Analysis and models module in metbit 9.0.0.

import metbit.lazy_opls_da

Classes

lazy_opls_da

Parameters

dataDataFrame containing the dataset.

groupsList of class labels for each data sample.

working_dirDirectory path for storing output files.

feature_namesNames of features, defaults to None.

n_componentsNumber of components for OPLS-DA, defaults to 2.

scalingScaling method ('pareto'), defaults to 'pareto'.

estimatorModel estimator, defaults to 'opls'.

kfoldNumber of folds in cross-validation, defaults to 3.

random_stateRandom seed, defaults to 94.

auto_ncompAutomatically choose the optimal number of components, defaults to True.

permutationConduct permutation tests, defaults to True.

VIPCalculate VIP scores, defaults to True.

linear_regressionConduct linear regression analysis, defaults to True.

Returns

A printout of the model summary, including the project name, dataset information, configuration, and directory paths.

fit Method

Fits the OPLS-DA model to the dataset, generates plots, and saves them to the output directory.

Parameters

marker_colorDictionary mapping groups to colors.

custom_colorCustom color grouping.

custom_shapeCustom shape grouping.

symbol_dictDictionary mapping groups to marker symbols.

custom_legend_nameCustom for the legend, defaults to ['Group', 'Sub-group'].

marker_labelSpecifies marker labels ('class', 'group', or 'sub-group').

marker_sizeSize of markers in plots.

marker_opacityOpacity level of markers in plots.

individual_ellipseOption to display individual ellipses for each group.

Returns

A message indicating the model fitting was successful.

Directories Created

working_dir/project_name/element/plots/... for different plots.

working_dir/project_name/element/data/... for data outputs.

Plotting and Saving Data

Methods

__init__(self, data: pd.DataFrame, groups: list, working_dir: str, feature_: list=None, n_components: int=2, scaling: str='pareto', estimator: str='opls', kfold: int=3, random_state: int=94, auto_ncomp: bool=True, permutation: bool=True, n_permutation: int=500, n_jobs: int=4, VIP: bool=True, VIP_threshold: float=1.5, linear_regression: bool=True, FC_threshold: float=1.5, p_val_threshold: float=2)

fit(self, marker_color: dict=None, custom_color: list=None, custom_shape: list=None, symbol_dict: dict=None, custom_legend_name=['Group', 'Sub-group'], marker_label=None, marker_size=None, marker_opacity=None, individual_ellipse=False)