transcription_factor_analysis package

Submodules

transcription_factor_analysis.differential_signal_analysis module

class lbfextract.transcription_factor_analysis.differential_signal_analysis.DiffSignalAnalysis(df: DataFrame | DataFrame, output_path: Path, signal_col_index: list[str], outer_group_column: str, inner_group_column: str, value_column: str, correction_method: str, max_iter: int, alpha: float, overwrite: bool, rm_outliers: bool, save_individual_plots: bool, use_provided_gi: bool = False, user_background: list[str] = None, normalize: bool = False)

Bases: object

static get_enrichment(genes: list[str], background: list[str] = None) → DataFrame | None

get_output_folder()

plot_barplot_rejected_per_group()

plot_coverage(gi)

run()

save_results()

lbfextract.transcription_factor_analysis.differential_signal_analysis.create_path(ctx, param, value)

lbfextract.transcription_factor_analysis.differential_signal_analysis.get_state(mean_1, mean_2)

lbfextract.transcription_factor_analysis.differential_signal_analysis.parse_indices(ctx, param, value: str) → tuple[int, int] | None

lbfextract.transcription_factor_analysis.differential_signal_analysis.signed_log2(x)

transcription_factor_analysis.accessibility_extraction module

lbfextract.transcription_factor_analysis.accessibility_extraction.get_chromatin_accessibility_coverage(df_: DataFrame, metadata: AccessibilityConfig) → ndarray

Function to extract the chromatin accessibility at TFBSs for given TFs. Since the accessibility is inversely correlated with coverage. The sign of the amplitude is swapped and measured to be the min if there is a dip or the max, in case there is a peak in the central part of the signal. the central part is defined in the metadata

lbfextract.transcription_factor_analysis.accessibility_extraction.get_chromatin_accessibility_entropy(df_: DataFrame, metadata: AccessibilityConfig) → ndarray

Function to extract the chromatin accessibility at TFBSs for given TFs from entropy derived signals.

transcription_factor_analysis.loaders module

class lbfextract.transcription_factor_analysis.loaders.ResultsLoader(path_to_res_summary: Path, accessibility_extraction_config: dict, signal_length: int = 4000, flanking_signal_indices: tuple = (1000, 3000), normalize: bool = False, path_to_sample_sheet: Path = None, grouping_column: str = None, signal_type: str = 'coverage')

Bases: object

check_signal_type_compatibility(signal_type: str) → str

generate_sample_sheet()

get_accessibility(df_: DataFrame) → ndarray

get_res_df_polars() → DataFrame

load() → DataFrame

load_sample_sheet(path_to_sample_sheet: Path) → DataFrame

normalize_df(df: DataFrame) → DataFrame

classmethod register_signal_type(signal_name, fun: Callable, validator: Config)

signal_types = {'coverage': {'fun': <function get_chromatin_accessibility_coverage>, 'validator': <class 'lbfextract.transcription_factor_analysis.schemas.AccessibilityConfig'>}, 'entropy': {'fun': <function get_chromatin_accessibility_entropy>, 'validator': <class 'lbfextract.transcription_factor_analysis.schemas.AccessibilityConfig'>}}

transcription_factor_analysis.utils module

lbfextract.transcription_factor_analysis.utils.generate_time_stamp() → str

Generate a time stamp.

lbfextract.transcription_factor_analysis.utils.remove_outliers(data: Series, threshold: float = 1.5) → Series

Remove outliers from a Pandas Series using the IQR method.

Parameters:

• data – The data input.

• threshold – The threshold value to determine outliers (default: 1.5).

Returns:

The data with outliers removed.

transcription_factor_analysis.schemas module

class lbfextract.transcription_factor_analysis.schemas.AccessibilityConfig(config_dict: dict | None = None)

Bases: Config

end: int

schema = <Schema({'start': <class 'int'>, 'end': <class 'int'>}, extra=PREVENT_EXTRA, required=False) object>

start: int

Module contents