test_TER

Function

crisgi_obj.test_TER(
    target_group=None, 
    p_cutoff=0.05, 
    method="prod", 
    groups=None
)

Identifies Trend Expressed Interactions (TER) for each group in the dataset. This function evaluates interactions based on trend analysis and statistical significance, saving the results and statistics for downstream analysis.

Parameters

Name	Type	Description
target_group	str or None	Specific group to analyze. If None, all groups in groups are processed.
p_cutoff	float	P-value cutoff for statistical significance (default: 0.05).
method	str	Method used for interaction analysis (e.g., 'prod').
groups	list or None	List of groups to analyze. If None, uses self.groups.

Return type

None

Returns

This function does not return a value. It saves TER results and statistics to the edata.uns attribute and outputs CSV files with TER statistics for each group.

Attributes Set

• edata.uns[f'{method}_{self.groupby}_{target_group}_TER']: List of filtered interactions identified as TER.
• edata.uns[f'{method}_{self.groupby}_{target_group}_TER_df']: DataFrame containing detailed TER statistics for each interaction.

Example

# Assume `crisgi` is an instance of the CRISGI class, already initialized with data.

# Run TER analysis for all groups using the default method and p-value cutoff
crisgi.test_TER()

# Run TER analysis for a specific group with a custom p-value cutoff
crisgi.test_TER(target_group='GroupA', p_cutoff=0.01)

# Run TER analysis for a custom list of groups and a different method
custom_groups = ['GroupA', 'GroupB']
crisgi.test_TER(groups=custom_groups, method='sum')

After execution, TER results and statistics are saved in the edata.uns attribute and as CSV files in the specified output directory.