Source code for cnvlib.fix

"""Supporting functions for the 'fix' command."""
from __future__ import absolute_import, division
import bisect

import numpy
from Bio._py3k import zip

from . import params, reference, smoothing
from .ngfrills import echo

[docs]def load_adjust_coverages(pset, ref_pset, fix_gc, fix_edge, fix_rmask): """Load and filter probe coverages; correct using reference and GC.""" if 'gc' in pset: # Don't choke on Picard-derived files that have the GC column pset = pset.drop_extra_columns() # No corrections needed if there are no data rows (e.g. no antitargets) if not len(pset): return pset # Check for signs that the wrong reference was used missing_keys = set(pset.labels()).difference(ref_pset.labels()) if missing_keys: raise ValueError("Reference is missing %d bins found in %s" % (len(missing_keys), pset.sample_id)) # ENH: Wouldn't this be easier as [gene != 'Background']? ref_matched = match_ref_to_probes(ref_pset, pset) # Normalize coverages according to the reference # (Subtract the reference log2 copy number to get the log2 ratio)['coverage'] -= ref_matched['coverage'] # Drop probes that had poor coverage in the pooled reference ok_cvg_indices = (True - reference.mask_bad_probes(ref_matched)) =[ok_cvg_indices] =[ok_cvg_indices] # Apply corrections for known systematic biases in coverage pset.center_all() if fix_gc: if 'gc' in ref_matched: echo("Correcting for GC bias...") pset = center_by_window(pset, .1, ref_matched['gc']) else: echo("WARNING: Skipping correction for RepeatMasker bias") if fix_edge: echo("Correcting for edge effects...") pset = center_by_window(pset, .1, make_edge_sorter(pset, params.INSERT_SIZE)) if fix_rmask: if 'rmask' in ref_matched: echo("Correcting for RepeatMasker bias...") pset = center_by_window(pset, .1, ref_matched['rmask']) else: echo("WARNING: Skipping correction for RepeatMasker bias") pset.center_all() return pset
[docs]def match_ref_to_probes(ref_pset, probes): """Filter the reference probes to match the target or antitarget probe set. """ ref_lookup = dict(zip(ref_pset.labels(), ref_pset)) ref_matched_rows = [tuple(ref_lookup[label]) for label in probes.labels()] ref_matched = ref_pset.to_rows(ref_matched_rows) return ref_matched
[docs]def center_by_window(pset, fraction, sort_key): """Smooth out biases according to the trait specified by sort_key. E.g. correct GC-biased probes by windowed averaging across similar-GC probes; or for similar interval sizes. """ adj_pset = pset.copy() # Separate neighboring probes that could have the same key # (to avoid re-centering actual CNV regions -- only want an independently # sampled subset of presumably overall-CN-neutral probes) shuffle_order = adj_pset.shuffle() if isinstance(sort_key, numpy.ndarray): # Apply the same shuffling to the key array as to the target probe set sort_key = sort_key[shuffle_order] # Sort the data according to the specified parameter adj_pset.sort(key=sort_key) biases = smoothing.rolling_median(adj_pset.coverage, fraction) # biases = smoothing.smoothed(adj_pset.coverage, fraction) adj_pset['coverage'] -= biases adj_pset.sort() return adj_pset
[docs]def make_edge_sorter(target_probes, margin): """Create a sort-key function for tiling edge effects.""" # Index the target interval positions chrom_tile_starts = {} chrom_tile_ends = {} for chrom, rows in target_probes.by_chromosome(): chrom_tile_starts[chrom] = rows['start'] chrom_tile_ends[chrom] = rows['end'] def get_edge(chrom, tgt_start, tgt_end, insert_size): """Quantify the "edge effect" of the target tile and its neighbors. The result is proportional to the change in the target's coverage due to these edge effects, i.e. the expected loss of coverage near the target edges and, if there are close neighboring tiles, gain of coverage due to "spill over" reads from the neighbor tiles. (This is not the actual change in coverage. This is just a tribute.) """ margin_start = tgt_start - insert_size margin_end = tgt_end + insert_size tile_starts = chrom_tile_starts[chrom] tile_ends = chrom_tile_ends[chrom] target_size = (tgt_end - tgt_start) # Calculate coverage loss at (both) tile edges loss = edge_loss(target_size, insert_size) # For each neighbor tile, calculate coverage gain to the target gaps_left = [] gaps_right = [] # Find the leftmost tile in the margin left_idx = max(0, bisect.bisect_left(tile_ends, margin_start) - 1) for (tile_start, tile_end) in zip(tile_starts[left_idx:], tile_ends[left_idx:]): if tile_end <= margin_start: # No overlap on the 5' end -- keep moving forward continue if tile_start >= margin_end: # No overlap on the 3' end -- we're done break if tile_start == tgt_start and tile_end == tgt_end: # The target itself continue # Tile is within margins if margin_start <= tile_end <= tgt_start: # Left neighbor gaps_left.append(tgt_start - tile_end) elif tgt_end <= tile_start <= margin_end: # Right neighbor gaps_right.append(tile_start - tgt_end) elif tile_start < tgt_start and tile_end >= tgt_start: # Overlap on left side -- treat as adjacent gaps_left.append(0) elif tile_start <= tgt_end and tile_end > tgt_end: # Overlap on right side -- treat as adjacent gaps_right.append(0) else: # DBG: This should probably never happen echo("Oddly positioned tile (%s:%d-%d) vs. target (%d-%d)" % (chrom, tile_start, tile_end, tgt_start, tgt_end)) continue gain = 0 if gaps_left: gain += edge_gain(target_size, insert_size, min(gaps_left)) if gaps_right: gain += edge_gain(target_size, insert_size, min(gaps_right)) return gain - loss def sorter_edge(row): """Calculate the edge effects on this bin. Find tiled intervals within a margin (+/- bp) of the given probe (including the probe itself, so the edge is never zero). Return the proportion of the windowed range that is covered by tiled regions. """ return get_edge(row['chromosome'], row['start'], row['end'], margin) return sorter_edge
[docs]def edge_loss(target_size, insert_size): """Calculate coverage loss at the edges of a baited region. Letting i = insert size and t = target size, the proportional loss of coverage near the two edges of the baited region (combined) is:: i/2t If the "shoulders" extend outside the bait $(t < i), reduce by:: (i-t)^2 / 4it on each side, or (i-t)^2 / 2it total. """ loss = insert_size / (2 * target_size) if target_size < insert_size: # Drop the shoulder part that would extend past the bait loss -= ((insert_size - target_size)**2 / (2 * insert_size * target_size)) return loss
[docs]def edge_gain(target_size, insert_size, gap_size): """Calculate coverage gain from a neighboring bait's flanking reads. Letting i = insert size, t = target size, g = gap to neighboring bait, the gain of coverage due to a nearby bait, if g < i, is:: (i-g)^2 / 4it If the neighbor flank extends beyond the target (t+g < i), reduce by:: (i-t-g)^2 / 4it """ assert gap_size <= insert_size gain = ((insert_size - gap_size)**2 / (4 * insert_size * target_size)) if target_size + gap_size < insert_size: # Drop the flank part that extends past this baited region gain -= ((insert_size - target_size - gap_size)**2 / (4 * insert_size * target_size)) return gain
[docs]def apply_weights(cnarr, ref_arr, min_weight=1e-5): """Calculate weights for each bin. Weights are derived from the "spread" column of the reference. In future, deviations within a rolling in the sample array may also be considered. """ ref_matched = match_ref_to_probes(ref_arr, cnarr) # Weight is proportional to variance, 0--1 variances = ref_matched['spread'] ** 2 max_variance = variances.max() if max_variance == 0: weights = numpy.ones_like(variances) else: weights = 1.0 - (variances / max_variance) # Avoid 0-value bins -- CBS doesn't like these weights[weights <= min_weight] = min_weight return cnarr.add_columns(weight=weights)