metaloci package

Subpackages

• metaloci.graph_layout package
  • Submodules
  • metaloci.graph_layout.kk module
    • estimate_cutoff()
    • estimate_persistence_length()
    • get_restraints_matrix()
    • get_subset_matrix()
• metaloci.misc package
  • Submodules
  • metaloci.misc.misc module
    • bedparser()
    • binsearcher()
    • check_names()
    • clean_matrix()
    • create_version_log()
    • get_poi_data()
    • gtfparser()
    • has_exactly_one_line()
    • signal_binnarize()
    • signal_normalization()
    • ucscparser()
    • write_bad_region()
    • write_bed()
    • write_moran_data()
• metaloci.plot package
  • Submodules
  • metaloci.plot.plot module
    • create_composite_figure()
    • create_number_image()
    • get_gaudi_signal_plot()
    • get_gaudi_type_plot()
    • get_hic_plot()
    • get_highlight()
    • get_kk_plot()
    • get_lmi_change_scan_plot()
    • get_lmi_scatterplot()
    • kk_plot_to_subplot()
    • mixed_matrices_plot()
    • place_composite()
    • place_pdf()
    • save_mm_kk()
    • signal_plot()
• metaloci.spatial_stats package
  • Submodules
  • metaloci.spatial_stats.lmi module
    • aggregate_signals()
    • compute_lmi()
    • construct_voronoi()
    • get_bed()
    • load_region_signals()
• metaloci.tests package
  • Submodules
  • metaloci.tests.test_tools module
• metaloci.tools package
  • Submodules
  • metaloci.tools.figure module
    • get_figures()
    • run()
  • metaloci.tools.layout module
    • get_region_layout()
    • run()
  • metaloci.tools.ml module
    • get_lmi()
    • run()
  • metaloci.tools.prep module
    • run()
• metaloci.utility_scripts package
  • Submodules
  • metaloci.utility_scripts.bts module
    • bts_ratio()
    • param_search()
    • pl_estimation()
    • run()
    • sum_hic_columns()
  • metaloci.utility_scripts.gene_selector module
    • run()
  • metaloci.utility_scripts.sniffer module
    • run()

Submodules

metaloci.mlo module

Class for the METALoci object

class metaloci.mlo.MetalociObject(region: str, resolution: int, save_path: str, chrom: str = None, start: int = None, end: int = None, poi: int = None)

Bases: object

This object is base class of METALoci. It contains all the information for a given region.

Parameters:

• region (str) – Region of interest

• resolution (int) – Resolution of the HiC experiment

• persistence_length (float) – Persistence length (parameter needed for the Kamada-Kawai algorithm)

• save_path (str) – Path where to save the mlo

• chrom (str, optional) – Chromosome of the region

• start (int, optional) – Start of the region

• end (int, optional) – End of the region

• poi (int, optional) – Bin position of the point of interest (usually the TSS of the gene)

• resolution – Resolution of the Hi-C to use.

• persistence_length – Persistence length to use.

• save_path – Path to save the object.

• chrom – Chromosome of the region.

• start – Start of the region.

• end – End of the region.

• poi – Point of interest of the region.

• matrix (numpy.ndarray, optional) – Hi-C matrix for the region.

• subset_matrix (numpy.ndarray, optional) – Subset of the matrix.

• bad_region (bool, optional) – Bool to indicate if the region does not meet quelity criteria to be considered significant.

• mixed_matrices (dict, optional) – Dictionary with the mixed matrices (top half if the full matrix, bottom part is the subset matrix).

• signals_dict (dict, optional) – Dictionary with the signals for the region.

• flat_matrix (numpy.ndarray, optional) – Flattened matrix.

• kk_cutoff (dict, optional) – Dictionary with the cutoffs for the Kamada-Kawai algorithm.

• kk_restraints_matrix (numpy.ndarray, optional) – Matrix with the restraints to be used by the Kamada-Kawai algorithm.

• kk_top_indexes (numpy.ndarray, optional) – Top indexes of the Kamada-Kawai algorithm, used to build the subset matrix.

• kk_graph (networkx.Graph, optional) – Graph of the Kamada-Kawai algorithm.

• kk_nodes (numpy.ndarray, optional) – Nodes of the Kamada-Kawai algorithm.

• kk_coords (numpy.ndarray, optional) – Coordinates of the Kamada-Kawai algorithm.

• kk_distances (numpy.ndarray, optional) – Distances of the Kamada-Kawai algorithm.

• agg (numpy.ndarray, optional) – Aggregated signals.

• lmi_geometry (numpy.ndarray, optional) – Geometry of the Gaudí plot.

• lmi_info (dict, optional) – Information of the the Local Moran’s I output for this region.

save(file_handler: str)

Function to save the mlobject. :param file_handler: Path to the file name where to save the mlo :type file_handler: str

metaloci.mlo.reconstruct(state)

Function to reconstruct the object from the state dictionary.