import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import pandas as pd # Not used here but will be required for creating dataframe import re import seaborn.apionly as sns """ MIT License Copyright (c) [2016] [Parashar Dhapola] Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ __author__ = "Parashar Dhapola" __email__ = "parashar.dhapola@gmail.com" class ChromVis(object): chromInfo = { 'hg38': { 'lengths': { 'chr1': 248956422, 'chr2': 242193529, 'chr3': 198295559, 'chr4': 190214555, 'chr5': 181538259, 'chr6': 170805979, 'chr7': 159345973, 'chr8': 145138636, 'chr9': 138394717, 'chr10': 133797422, 'chr11': 135086622, 'chr12': 133275309, 'chr13': 114364328, 'chr14': 107043718, 'chr15': 101991189, 'chr16': 90338345, 'chr17': 83257441, 'chr18': 80373285, 'chr19': 58617616, 'chr20': 64444167, 'chr21': 46709983, 'chr22': 50818468, 'chrX': 156040895, 'chrY': 57227415 }, 'centromeres': { 'chr1': 123400000, 'chr10': 39800000, 'chr11': 53400000, 'chr12': 35500000, 'chr13': 17700000, 'chr14': 17200000, 'chr15': 19000000, 'chr16': 36800000, 'chr17': 25100000, 'chr18': 18500000, 'chr19': 26200000, 'chr2': 93900000, 'chr20': 28100000, 'chr21': 12000000, 'chr22': 15000000, 'chr3': 90900000, 'chr4': 50000000, 'chr5': 48800000, 'chr6': 59800000, 'chr7': 60100000, 'chr8': 45200000, 'chr9': 43000000, 'chrX': 61000000, 'chrY': 10400000 } } } def __init__(self, data, chromosomes='all', chromosome_lengths='hg38', centromere_pos='hg38', height=15, width=15, descaling_factor=1000, val_scaling_factor=10, grid_ratio=[1, 1, 8], chrom_spacing=0.1, bar_lim=1, max_val=None, seaborn_palette='muted'): """ Args: data: A pandas dataframe with three columns compulsary columns: 'chromosomes': Chromosome name 'positions': position of feature on chromosome in bases '': Value at the corresponding position for a given sample. Should be positive intergers. ..'': Further sample values might be added in additional columns. chromosomes: Chromosome name in the order to be plotted. chromosome_lengths: List of length of each chromosome in order given in argument 'chroms'. If a string is provided than the class attribute 'chromInfo' (dict) is looked up for the key. centromere_pos: List containing the position of centromeres (center of centromere to be indicated).If a string is provided than the class attribute 'chromInfo' (dict) is looked up for the key. height: Figure height in inches. width: Figure width in inches. descaling_factor: The longest chromosome is descaled to this length val_scaling_factor: A multiplication factor for values. Increasing this will make the vertical bars over chromosomes taller. grid_ratio: A list of three integer values defining the the width ratio of columns in the figure. First column containing the chromsome name, second contains the bar plots for sum of values plotted in each chromosome and the third the annotated chromosomes. chrom_spacing: Spacing between chromosomes. max_val: If set to none, the maximum value is automatically found from the dataframe. This value is used to normalize the rest values to set the scale to one, which might be further increased using 'val_scaling_factor' . However, in certain cases where user wants to plot to chromosome maps and compare there values, this might be set. seaborn_palette: Seaborn color palette """ self.data = data self.chroms = chromosomes self.chromLengths = chromosome_lengths self.centromerePos = centromere_pos self.figHeight = height self.figWidth = width self.descalingFactor = descaling_factor self.valScalingFactor = val_scaling_factor self.gridRatio = grid_ratio self.chromSpacing = chrom_spacing self.colorPalette = seaborn_palette if self._sanitize() is True: self.maxChromLen = self._get_max_chrom_len() self.maxVal = self._get_max_val() self.figure = self._make_canvas() self.grid = self._make_grid() self.sampleNames = self._get_sample_names() self.barVals, self.maxBarVal = self._make_bar_values() self.colors = self._make_colors() for n, c in enumerate(self.chroms): self.curChromName = c self.curChromNum = n self.curChromScaledLen = self._get_chrom_scaled_len() self.curCenPos = self._get_centromere_position() self.curPositions, self.curValues = self._get_chrom_data() _ = self._make_label_axis() _ = self._make_bar_axis() _ = self._make_chrom_axis() else: print "ERROR" @staticmethod def _clean_axis(ax): ax.set_yticklabels([]) ax.set_xticklabels([]) ax.set_xticks([]) ax.set_yticks([]) for i in ax.spines: ax.spines[i].set_visible(False) return True @staticmethod def _natsort(l): convert = lambda text: int(text) if text.isdigit() else text alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] return sorted(l, key=alphanum_key) def _sanitize(self): if type(self.chroms) is list: pass else: if self.chroms == 'all': self.chroms = ChromVis._natsort(list(set( self.data.chromosomes.values))) else: pass if type(self.chromLengths) is list: pass else: if self.chromLengths in ChromVis.chromInfo: info = ChromVis.chromInfo[self.chromLengths]['lengths'] self.chromLengths = [] for i in self.chroms: if i in info: self.chromLengths.append(info[i]) if type(self.centromerePos) is list: pass else: if self.centromerePos in ChromVis.chromInfo: info = ChromVis.chromInfo[self.centromerePos]['centromeres'] self.centromerePos = [] for i in self.chroms: if i in info: self.centromerePos.append(info[i]) return True def _get_max_chrom_len(self): return max(self.chromLengths) def _get_max_val(self): max_val = 0 for sample in self.data: if sample not in ['chromosomes', 'positions']: if max(self.data[sample]) > max_val: max_val = max(self.data[sample]) return float(max_val) def _make_canvas(self): return plt.figure(figsize=(self.figWidth, self.figHeight)) def _make_grid(self): return mpl.gridspec.GridSpec( len(self.chromLengths), sum(self.gridRatio), hspace=self.chromSpacing) def _get_sample_names(self): names = [] for sample in self.data: if sample not in ['chromosomes', 'positions']: names.append(sample) return names def _get_chrom_scaled_len(self): return int(self.chromLengths[self.curChromNum] * self.descalingFactor / self.maxChromLen) def _get_centromere_position(self): return int((self.centromerePos[self.curChromNum] * self.curChromScaledLen) / self.chromLengths[self.curChromNum]) def _get_chrom_data(self): df = self.data[self.data.chromosomes == self.curChromName] positions = df.positions.apply( lambda x: int((x * self.curChromScaledLen) / self.chromLengths[self.curChromNum])) values = [] for sample in self.sampleNames: values.append(df[sample].apply( lambda x: np.log2(x) / np.log2(self.maxVal))) return positions, np.asarray(values).T def _make_colors(self): return sns.color_palette(self.colorPalette, len(self.sampleNames), desat=0.7) def _make_bar_values(self): bar_vals = {} max_bar_val = 0 for chrom in self.chroms: df = self.data[self.data.chromosomes == chrom] temp = [] for sample in self.sampleNames: v = df[sample].sum() temp.append(v) if v > max_bar_val: max_bar_val = v bar_vals[chrom] = temp return bar_vals, max_bar_val def _make_label_axis(self): ax = plt.subplot(self.grid[self.curChromNum, :self.gridRatio[0]]) ax.set_ylim((0, 1)) ax.set_xlim((0, 1)) ax.text(1, 0.25, self.curChromName, fontsize=20, horizontalalignment='right') return ChromVis._clean_axis(ax) def _make_bar_axis(self): ax = plt.subplot(self.grid[self.curChromNum, self.gridRatio[0]: self.gridRatio[0] + self.gridRatio[1]]) x = [i for i in range(len(self.barVals[self.curChromName]))] y = [i for i in self.barVals[self.curChromName]] bar_list = ax.barh(x, y, 0.75) for i in range(len(bar_list)): bar_list[i].set_color(self.colors[i]) ax.set_ylim((0, len(self.barVals[self.curChromName]))) ax.set_xlim((0, self.maxBarVal)) return ChromVis._clean_axis(ax) def _make_chrom_axis(self): ax = plt.subplot(self.grid[self.curChromNum, self.gridRatio[0] + self.gridRatio[1]:]) ax.plot([0, self.curCenPos - 5], [-1, -1], alpha=0.6, c='grey', lw=3) ax.plot([self.curCenPos + 5, self.curChromScaledLen], [-1, -1], alpha=0.6, c='grey', lw=3) ax.scatter([self.curCenPos], [-1], c='crimson', lw=0, s=30, alpha=1) for p, vv in zip(self.curPositions, self.curValues): for c, v in zip(self.colors, vv): ax.plot([p, p + 0.01], [0, v * self.valScalingFactor], lw=3, c=c, alpha=0.8) ax.set_xlim((0, self.descalingFactor)) ax.set_ylim((-3, self.valScalingFactor)) return ChromVis._clean_axis(ax)