Source code for cnvlib.scatter

"""Command-line interface and corresponding API for CNVkit."""
# NB: argparse CLI definitions and API functions are interwoven:
#   "_cmd_*" handles I/O and arguments processing for the command
#   "do_*" runs the command's functionality as an API
from __future__ import absolute_import, division, print_function

import collections
import logging

import numpy as np
from matplotlib import pyplot
from skgenome.rangelabel import unpack_range

from . import core, params, plots
from .plots import MB
from .cnary import CopyNumArray as CNA

POINT_COLOR = '#606060'
SEG_COLOR = 'darkorange'

[docs]def do_scatter(cnarr, segments=None, variants=None, show_range=None, show_gene=None, do_trend=False, by_bin=False, window_width=1e6, y_min=None, y_max=None, antitarget_marker=None, segment_color=SEG_COLOR, title=None, ): """Plot probe log2 coverages and segmentation calls together.""" if by_bin: bp_per_bin = (sum(c.end.iat[-1] for _, c in cnarr.by_chromosome()) / len(cnarr)) window_width /= bp_per_bin show_range_bins = plots.translate_region_to_bins(show_range, cnarr) cnarr, segments, variants = plots.update_binwise_positions( cnarr, segments, variants) global MB orig_mb = MB MB = 1 if not show_gene and not show_range: genome_scatter(cnarr, segments, variants, do_trend, y_min, y_max, title, segment_color) else: if by_bin: show_range = show_range_bins chromosome_scatter(cnarr, segments, variants, show_range, show_gene, antitarget_marker, do_trend, by_bin, window_width, y_min, y_max, title, segment_color) if by_bin: # Reset to avoid permanently altering the value of cnvlib.scatter.MB MB = orig_mb
# === Genome-level scatter plots ===
[docs]def genome_scatter(cnarr, segments=None, variants=None, do_trend=False, y_min=None, y_max=None, title=None, segment_color=SEG_COLOR): """Plot all chromosomes, concatenated on one plot.""" if (cnarr or segments) and variants: # Lay out top 3/5 for the CN scatter, bottom 2/5 for SNP plot axgrid = pyplot.GridSpec(5, 1, hspace=.85) axis = pyplot.subplot(axgrid[:3]) axis2 = pyplot.subplot(axgrid[3:], sharex=axis) # Place chromosome labels between the CNR and SNP plots axis2.tick_params(labelbottom=False) chrom_sizes = plots.chromosome_sizes(cnarr or segments) snv_on_genome(axis2, variants, chrom_sizes, segments, do_trend, segment_color) else: _fig, axis = pyplot.subplots() if title is None: title = (cnarr or segments or variants).sample_id if cnarr or segments: axis.set_title(title) cnv_on_genome(axis, cnarr, segments, do_trend, y_min, y_max, segment_color) else: axis.set_title("Variant allele frequencies: %s" % title) chrom_sizes = collections.OrderedDict( (chrom, subarr["end"].max()) for chrom, subarr in variants.by_chromosome()) snv_on_genome(axis, variants, chrom_sizes, segments, do_trend, segment_color)
[docs]def cnv_on_genome(axis, probes, segments, do_trend=False, y_min=None, y_max=None, segment_color=SEG_COLOR): """Plot bin ratios and/or segments for all chromosomes on one plot.""" # Configure axes etc. axis.axhline(color='k') axis.set_ylabel("Copy ratio (log2)") if not (y_min and y_max): if segments: # Auto-scale y-axis according to segment mean-coverage values # (Avoid spuriously low log2 values in HLA and chrY) low_chroms = segments.chromosome.isin(('6', 'chr6', 'Y', 'chrY')) seg_auto_vals = segments[~low_chroms]['log2'].dropna() if not y_min: y_min = (np.nanmin([seg_auto_vals.min() - .2, -1.5]) if len(seg_auto_vals) else -2.5) if not y_max: y_max = (np.nanmax([seg_auto_vals.max() + .2, 1.5]) if len(seg_auto_vals) else 2.5) else: if not y_min: y_min = -2.5 if not y_max: y_max = 2.5 axis.set_ylim(y_min, y_max) # Group probes by chromosome (to calculate plotting coordinates) if probes: chrom_sizes = plots.chromosome_sizes(probes) chrom_probes = dict(probes.by_chromosome()) # Precalculate smoothing window size so all chromosomes have similar # degree of smoothness # NB: Target panel has ~1k bins/chrom. -> 250-bin window # Exome: ~10k bins/chrom. -> 2500-bin window window_size = int(round(.15 * len(probes) / probes.chromosome.nunique())) else: chrom_sizes = plots.chromosome_sizes(segments) # Same for segment calls chrom_segs = dict(segments.by_chromosome()) if segments else {} # Plot points & segments x_starts = plots.plot_x_dividers(axis, chrom_sizes) for chrom, x_offset in x_starts.items(): if probes and chrom in chrom_probes: subprobes = chrom_probes[chrom] x = 0.5 * (subprobes['start'] + subprobes['end']) + x_offset axis.scatter(x, subprobes['log2'], marker='.', color=POINT_COLOR, edgecolor='none', alpha=0.2) if do_trend: # ENH break trendline by chromosome arm boundaries? axis.plot(x, subprobes.smoothed(window_size), color=POINT_COLOR, linewidth=2, zorder=-1) if chrom in chrom_segs: for seg in chrom_segs[chrom]: color = choose_segment_color(seg, segment_color) axis.plot((seg.start + x_offset, seg.end + x_offset), (seg.log2, seg.log2), color=color, linewidth=3, solid_capstyle='round')
[docs]def snv_on_genome(axis, variants, chrom_sizes, segments, do_trend, segment_color): """Plot a scatter-plot of SNP chromosomal positions and shifts.""" axis.set_ylim(0.0, 1.0) axis.set_ylabel("VAF") x_starts = plots.plot_x_dividers(axis, chrom_sizes) # Calculate the coordinates of plot components chrom_snvs = dict(variants.by_chromosome()) if segments: chrom_segs = dict(segments.by_chromosome()) elif do_trend: # Pretend a single segment covers each chromosome chrom_segs = {chrom: None for chrom in chrom_snvs} else: chrom_segs = {} for chrom, x_offset in x_starts.items(): if chrom not in chrom_snvs: continue snvs = chrom_snvs[chrom] # Plot the points axis.scatter(snvs['start'].values + x_offset, snvs['alt_freq'].values, color=POINT_COLOR, edgecolor='none', alpha=0.2, marker='.') # Trend bars: always calculated, only shown on request if chrom in chrom_segs: # Draw average VAF within each segment segs = chrom_segs[chrom] for seg, v_freq in get_segment_vafs(snvs, segs): if seg: posn = [seg.start + x_offset, seg.end + x_offset] color = choose_segment_color(seg, segment_color, default_bright=False) else: posn = [snvs.start.iat[0] + x_offset, snvs.start.iat[-1] + x_offset] color = TREND_COLOR axis.plot(posn, [v_freq, v_freq], color=color, linewidth=2, zorder=-1, solid_capstyle='round')
# === Chromosome-level scatter plots ===
[docs]def chromosome_scatter(cnarr, segments, variants, show_range, show_gene, antitarget_marker, do_trend, by_bin, window_width, y_min, y_max, title, segment_color): """Plot a specified region on one chromosome. Possibilities:: Options | Shown ------------ | -------- -c | -g | Genes | Region ------- | -- | ----- | ------ - | + | given | auto: gene(s) + margin chr | - | none | whole chrom chr | + | given | whole chrom chr:s-e | - | all | given chr:s-e | + | given | given """ sel_probes, sel_segs, sel_snvs, window_coords, genes, chrom = \ select_range_genes(cnarr, segments, variants, show_range, show_gene, window_width) # Create plots if cnarr or segments: # Plot CNVs at chromosome level if variants: # Lay out top 3/5 for the CN scatter, bottom 2/5 for SNP plot axgrid = pyplot.GridSpec(5, 1, hspace=.5) axis = pyplot.subplot(axgrid[:3]) axis2 = pyplot.subplot(axgrid[3:], sharex=axis) # Plot allele freqs for only the selected region snv_on_chromosome(axis2, sel_snvs, sel_segs, genes, do_trend, by_bin, segment_color) else: _fig, axis = pyplot.subplots() if by_bin: axis.set_xlabel("Position (bin)") else: axis.set_xlabel("Position (Mb)") cnv_on_chromosome(axis, sel_probes, sel_segs, genes, antitarget_marker=antitarget_marker, do_trend=do_trend, x_limits=window_coords, y_min=y_min, y_max=y_max, segment_color=segment_color) elif variants: # Only plot SNVs in a single-panel layout _fig, axis = pyplot.subplots() snv_on_chromosome(axis, sel_snvs, sel_segs, genes, do_trend, by_bin, segment_color) if title is None: title = "%s %s" % ((cnarr or segments or variants).sample_id, chrom) axis.set_title(title)
[docs]def select_range_genes(cnarr, segments, variants, show_range, show_gene, window_width): """Determine which datapoints to show based on the given options. Behaviors:: start/end show_gene + + given region + genes; err if any gene outside it - + window +/- around genes + - given region, highlighting any genes within it - - whole chromosome, no genes If `show_range` is a chromosome name only, no start/end positions, then the whole chromosome will be shown. If region start/end coordinates are given and `show_gene` is '' or ',' (or all commas, etc.), then instead of highlighting all genes in the selection, no genes will be highlighted. """ chrom, start, end = unpack_range(show_range) if start is None and end is None: # Either the specified range is only chrom, no start-end, or gene names # were given window_coords = () else: # Viewing region coordinates were specified -- take them as given # Fill in open-ended ranges' endpoints if start is None: start = 0 elif start < 0: start = 0 if not end: # Default selection endpoint to the maximum chromosome position end = (cnarr or segments or variants ).filter(chromosome=chrom).end.iat[-1] if end <= start: raise ValueError("Coordinate range {}:{}-{} (from {}) has size <= 0" .format(chrom, start, end, show_range)) window_coords = (start, end) gene_ranges = [] if show_gene is None: if window_coords: if cnarr: # Highlight all genes within the given range gene_ranges = plots.gene_coords_by_range(cnarr, chrom, start, end)[chrom] if not gene_ranges and (end - start) < 10 * window_width: # No genes in the selected region, so if the selection is small # (i.e. <80% of the displayed window, <10x window padding), # highlight the selected region itself. # (To prevent this, use show_gene='' or window_width=0)"No genes found in selection; will highlight the " "selected region itself instead") gene_ranges = [(start, end, "Selection")] window_coords = (max(0, start - window_width), end + window_width) else: gene_names = filter(None, show_gene.split(',')) if gene_names: # Scan for probes matching the specified gene(s) gene_coords = plots.gene_coords_by_name(cnarr or segments, gene_names) if len(gene_coords) > 1: raise ValueError("Genes %s are split across chromosomes %s" % (show_gene, list(gene_coords.keys()))) g_chrom, gene_ranges = gene_coords.popitem() if chrom: # Confirm that the selected chromosomes match core.assert_equal("Chromosome also selected by region (-c) " "does not match", **{"chromosome": chrom, "gene(s)": g_chrom}) else: chrom = g_chrom gene_ranges.sort() if window_coords: # Verify all genes fit in the given window for gene_start, gene_end, gene_name in gene_ranges: if not (start <= gene_start and gene_end <= end): raise ValueError("Selected gene %s (%s:%d-%d) " "is outside specified region %s" % (gene_name, chrom, gene_start, gene_end, show_range)) elif not show_range: # Set the display window to the selected genes +/- a margin window_coords = (max(0, gene_ranges[0][0] - window_width), gene_ranges[-1][1] + window_width) # Prune plotted elements to the selected region sel_probes = (cnarr.in_range(chrom, *window_coords) if cnarr else CNA([])) sel_segs = (segments.in_range(chrom, *window_coords, mode='trim') if segments else CNA([])) sel_snvs = (variants.in_range(chrom, *window_coords) if variants else None)"Showing %d probes and %d selected genes in region %s", len(sel_probes), len(gene_ranges), (chrom + ":%d-%d" % window_coords if window_coords else chrom)) return sel_probes, sel_segs, sel_snvs, window_coords, gene_ranges, chrom
[docs]def cnv_on_chromosome(axis, probes, segments, genes, antitarget_marker=None, do_trend=False, x_limits=None, y_min=None, y_max=None, segment_color=SEG_COLOR): """Draw a scatter plot of probe values with optional segments overlaid. Parameters ---------- genes : list Of tuples: (start, end, gene name) """ # TODO - allow plotting just segments without probes # Get scatter plot coordinates x = 0.5 * (probes['start'] + probes['end']) * MB # bin midpoints y = probes['log2'] if 'weight' in probes: w = 46 * probes['weight'] ** 2 + 2 else: w = np.repeat(30, len(x)) # Configure axes if not y_min: y_min = max(-5.0, min(y.min() - .1, -.3)) if len(y) else -1.1 if not y_max: y_max = max(.3, y.max() + (.25 if genes else .1)) if len(y) else 1.1 if x_limits: x_min, x_max = x_limits axis.set_xlim(x_min * MB, x_max * MB) else: set_xlim_from(axis, probes, segments) setup_chromosome(axis, y_min, y_max, "Copy ratio (log2)") if genes: highlight_genes(axis, genes, min(2.4, y.max() + .1) if len(y) else .1) if antitarget_marker in (None, 'o'): # Plot targets and antitargets with the same marker axis.scatter(x, y, w, color=POINT_COLOR, alpha=0.4, marker='o') else: # Use the given marker to plot antitargets separately x_fg = [] y_fg = [] w_fg = [] x_bg = [] y_bg = [] # w_bg = [] is_bg = probes['gene'].isin(params.ANTITARGET_ALIASES) for x_pt, y_pt, w_pt, is_bg_pt in zip(x, y, w, is_bg): if is_bg_pt: x_bg.append(x_pt) y_bg.append(y_pt) # w_bg.append(w_pt) else: x_fg.append(x_pt) y_fg.append(y_pt) w_fg.append(w_pt) axis.scatter(x_fg, y_fg, w_fg, color=POINT_COLOR, alpha=0.4, marker='o') axis.scatter(x_bg, y_bg, color=POINT_COLOR, alpha=0.5, marker=antitarget_marker) # Add a local trend line if do_trend: axis.plot(x, probes.smoothed(.1), color=POINT_COLOR, linewidth=2, zorder=-1) # Draw segments as horizontal lines if segments: for row in segments: color = choose_segment_color(row, segment_color) axis.plot((row.start * MB, row.end * MB), (row.log2, row.log2), color=color, linewidth=4, solid_capstyle='round')
[docs]def snv_on_chromosome(axis, variants, segments, genes, do_trend, by_bin, segment_color): # TODO set x-limits if not already done for probes/segments # set_xlim_from(axis, None, segments, variants) # setup_chromosome(axis, 0.0, 1.0, "VAF") axis.set_ylim(0.0, 1.0) axis.set_ylabel("VAF") if by_bin: axis.set_xlabel("Position (bin)") else: axis.set_xlabel("Position (Mb)") axis.get_yaxis().tick_left() axis.get_xaxis().tick_top() axis.tick_params(which='both', direction='out', labelbottom=False, labeltop=False) x_mb = variants['start'].values * MB y = variants['alt_freq'].values axis.scatter(x_mb, y, color=POINT_COLOR, alpha=0.3) if segments or do_trend: # Draw average VAF within each segment for seg, v_freq in get_segment_vafs(variants, segments): if seg: posn = [seg.start * MB, seg.end * MB] color = choose_segment_color(seg, segment_color, default_bright=False) else: posn = [variants.start.iat[0] * MB, variants.start.iat[-1] * MB] color = TREND_COLOR axis.plot(posn, [v_freq, v_freq], color=color, linewidth=2, zorder=1, solid_capstyle='round') if genes: highlight_genes(axis, genes, .9)
[docs]def set_xlim_from(axis, probes=None, segments=None, variants=None): """Configure axes for plotting a single chromosome's data. Parameters ---------- probes : CopyNumArray segments : CopyNumArray variants : VariantArray All should already be subsetted to the region that will be plotted. """ min_x = np.inf max_x = 0 for arr in (probes, segments, variants): if arr and len(arr): max_x = max(max_x, arr.end.iat[-1]) min_x = min(min_x, arr.start.iat[0]) if max_x <= min_x: if min_x != np.inf: logging.warning("WARNING: selection start %s > end %s; did you " "correctly sort the input file by genomic " "location?", min_x, max_x) raise ValueError("No usable data points to plot out of " "%d probes, %d segments, %d variants" % (len(probes) if probes else 0, len(segments) if segments else 0, len(variants) if variants else 0)) axis.set_xlim(min_x * MB, max_x * MB)
[docs]def setup_chromosome(axis, y_min=None, y_max=None, y_label=None): """Configure axes for plotting a single chromosome's data.""" if y_min and y_max: axis.set_ylim(y_min, y_max) if y_min < 0 < y_max: axis.axhline(color='k') if y_label: axis.set_ylabel(y_label) axis.tick_params(which='both', direction='out') axis.get_xaxis().tick_bottom() axis.get_yaxis().tick_left()
# === Shared ===
[docs]def choose_segment_color(segment, highlight_color, default_bright=True): """Choose a display color based on a segment's CNA status. Uses the fields added by the 'call' command. If these aren't present, use `highlight_color` for everything. For sex chromosomes, some single-copy deletions or gains might not be highlighted, since sample sex isn't used to infer the neutral ploidies. """ neutral_color = TREND_COLOR if 'cn' not in segment._fields: # No 'call' info return highlight_color if default_bright else neutral_color # Detect copy number alteration expected_ploidies = {'chrY': (0, 1), 'Y': (0, 1), 'chrX': (1, 2), 'X': (1, 2)} if not in expected_ploidies.get(segment.chromosome, [2]): return highlight_color # Detect regions of allelic imbalance / LOH if (segment.chromosome not in expected_ploidies and 'cn1' in segment._fields and 'cn2' in segment._fields and (segment.cn1 != segment.cn2)): return highlight_color return neutral_color
[docs]def get_segment_vafs(variants, segments): """Group SNP allele frequencies by segment. Assume variants and segments were already subset to one chromosome. Yields ------ tuple (segment, value) """ if segments: chunks = variants.by_ranges(segments) else: # Fake segments cover the whole region chunks = [(None, variants)] for seg, seg_snvs in chunks: # ENH: seg_snvs.tumor_boost() freqs = seg_snvs['alt_freq'].values # Separately emit VAFs above and below .5 for plotting idx_above_mid = (freqs > 0.5) for idx_vaf in (idx_above_mid, ~idx_above_mid): if sum(idx_vaf) > 1: yield (seg, np.median(freqs[idx_vaf]))
[docs]def highlight_genes(axis, genes, y_posn): """Show gene regions with background color and a text label.""" # Rotate text in proportion to gene density ngenes = len(genes) text_size = ('small' if ngenes <= 6 else 'x-small') if ngenes <= 3: text_rot = 'horizontal' elif ngenes <= 6: text_rot = 30 elif ngenes <= 10: text_rot = 45 elif ngenes <= 20: text_rot = 60 else: text_rot = 'vertical' for gene in genes: gene_start, gene_end, gene_name = gene # Highlight and label gene region # (rescale positions from bases to megabases) axis.axvspan(gene_start * MB, gene_end * MB, alpha=0.5, color=HIGHLIGHT_COLOR, zorder=-1) axis.text(0.5 * (gene_start + gene_end) * MB, y_posn, gene_name, horizontalalignment='center', rotation=text_rot, size=text_size)