MACS3.Signal.CallPeakUnit module

Compute MACS3 peak-calling scores and helper statistics.

This code is free software; you can redistribute it and/or modify it under the terms of the BSD License (see the file LICENSE included with the distribution).

class MACS3.Signal.CallPeakUnit.CallerFromAlignments(treat, ctrl, d=200, ctrl_d_s=[200, 1000, 10000], treat_scaling_factor=1.0, ctrl_scaling_factor_s=[1.0, 0.2, 0.02], stderr_on=False, pseudocount=1, end_shift=0, lambda_bg=0, save_bedGraph=False, bedGraph_filename_prefix='PREFIX', bedGraph_treat_filename='TREAT.bdg', bedGraph_control_filename='CTRL.bdg', cutoff_analysis_filename='TMP.txt', save_SPMR=False)

Bases: object

Compute pileups, scores, and peaks from FWTrack/PETrack alignments.

treat: Treatment track (FWTrack or PETrackI/PETrackII).

ctrl: Control track (FWTrack or PETrackI/PETrackII).

d: Fragment extension size for treatment (unused in PE mode).

ctrl_d_s: Fragment extension size for control.

treat_scaling_factor: Scaling factor for treatment.

ctrl_scaling_factor_s: Scaling factors for control.

lambda_bg: Minimum local bias to fill missing values.

chromosomes: Common chromosome names in treat/control.

pseudocount: Pseudocount used for logLR/FE/logFE calculations.

bedGraph_filename_prefix: Prefix for pileup/lambda bedGraph outputs.

end_shift: Shift applied to read ends before extension.

trackline: Whether to emit UCSC track lines in bedGraph outputs.

save_bedGraph: Whether to save pileup/lambda bedGraph files.

save_SPMR: Whether to save pileup normalized per million reads.

no_lambda_flag: Whether to ignore local lambda (use global only).

PE_mode: Whether treatment is paired-end.

PE_mode: _fake_callable

bedGraph_control_filename: bytes

bedGraph_ctrl_f: PointerInstance

bedGraph_filename_prefix: bytes

bedGraph_treat_f: PointerInstance

bedGraph_treat_filename: bytes

call_broadpeaks(scoring_function_symbols, lvl1_cutoff_s, lvl2_cutoff_s, min_length=200, lvl1_max_gap=50, lvl2_max_gap=400, cutoff_analysis=False)

This function try to find enriched regions within which, scores are continuously higher than a given cutoff for level 1, and link them using the gap above level 2 cutoff with a maximum length of lvl2_max_gap.

Parameters:

scoring_function_symbols (list) – Symbols for score functions. Use 'p' (pscore), 'q' (qscore), 'f' (fold change), or 's' (subtraction). Example: ['p', 'q'].
lvl1_cutoff_s (list) – Cutoffs for highly enriched regions.
lvl2_cutoff_s (list) – Cutoffs for linkage regions.
min_length (typedef) – Minimum peak length.
lvl1_max_gap (typedef) – Maximum gap to merge nearby peaks.
lvl2_max_gap (typedef) – Maximum length of linkage regions.
cutoff_analysis (_fake_callable) – Whether to compute cutoff-vs-peak metrics.

Returns:

(PeakIO, BroadPeakIO) for level-1 peaks and broad regions.

Return type:

tuple

Examples

lvl1, broad = caller.call_broadpeaks(['p'], [5.0], [2.0])

call_peaks(scoring_function_symbols, score_cutoff_s, min_length=200, max_gap=50, call_summits=False, cutoff_analysis=False)

Call narrow peaks for all chromosomes.

Parameters:

scoring_function_symbols (list) – Symbols for score functions. Use 'p' (pscore), 'q' (qscore), 'f' (fold change), or 's' (subtraction). Example: ['p', 'q'].
score_cutoff_s (list) – Cutoff values corresponding to scoring_function_symbols.
min_length (typedef) – Minimum peak length.
max_gap (typedef) – Maximum gap of insignificant regions within a peak.
call_summits (_fake_callable) – Whether to call sub-peaks (summits).
cutoff_analysis (_fake_callable) – Whether to compute cutoff-vs-peak metrics.

Returns:

Collection of called peaks.

Return type:

PeakIO

Examples

peaks = caller.call_peaks(['p'], [5.0], min_length=200)

chr_pos_treat_ctrl: list

chromosomes: list

ctrl: object

ctrl_d_s: list

ctrl_scaling_factor_s: list

cutoff_analysis_filename: bytes

d: int

destroy(): Remove temporary pileup files created during peak calling.

enable_trackline(): Enable UCSC track line output when writing bedGraphs.

end_shift: int

lambda_bg: float

no_lambda_flag: _fake_callable

optimal_p_cutoff: float

pileup_data_files: dict

pileup_treat_ctrl_a_chromosome(chrom): After this function is called, self.chr_pos_treat_ctrl will be reand: set assigned to the pileup values of the given chromosome.

pqtable: cykhash.Float32to32Map

pseudocount: double

pvalue_all_done: _fake_callable

pvalue_length: dict

pvalue_npeaks: dict

save_SPMR: _fake_callable

save_bedGraph: _fake_callable

set_pseudocount(pseudocount): Update the pseudocount used in scoring.

trackline: _fake_callable

treat: object

treat_scaling_factor: float

MACS3.Signal.CallPeakUnit.clean_up_ndarray(x): Resize x to zero length, releasing its underlying buffer.

MACS3.Signal.CallPeakUnit.fclose(*args, **kwargs)

MACS3.Signal.CallPeakUnit.find_optimal_cutoff(x, y)

Return the best cutoff x and y.

We assume that total peak length increase exponentially while decreasing cutoff value. But while cutoff decreases to a point that background noises are captured, total length increases much faster. So we fit a linear model by taking the first 10 points, then look for the largest cutoff that

*Currently, it is coded as a useless function.

Return type:: tuple

MACS3.Signal.CallPeakUnit.get_from_multiple_scores(multiple_score_arrays, index)

Return scores at index from each array in multiple_score_arrays.

Return type:: list

MACS3.Signal.CallPeakUnit.getitem_then_subtract(peakset, start)

Return peak starts relative to start for axillary metrics.

Return type:: list