Source code for limix.plot.manhattan

from __future__ import division

from numpy import arange, asarray, cumsum, flipud, log10


[docs]def plot_manhattan(df, alpha=None, null_style=dict(alpha=0.1, color='DarkBlue'), alt_style=dict(alpha=0.5, color='Orange'), ax=None): r"""Produce a manhattan plot. Parameters ---------- df : :class:`pandas.DataFrame` A Pandas DataFrame containing columns pv for p-values, pos for base-pair positions, and chrom for chromossome names.. alpha : float Threshold for significance. Defaults to 0.01 significance level (bonferroni-adjusted). ax : :class:`matplotlib.axes.AxesSubplot`: The target handle for this figure. If None, the current axes is set. Returns ------- :class:`matplotlib.axes.AxesSubplot` Axes object. Examples -------- .. plot:: from numpy.random import RandomState from numpy import arange, ones, kron import pandas as pd from limix.plot import plot_manhattan from matplotlib import pyplot as plt random = RandomState(1) pv = random.rand(5000) pv[1200:1250] = random.rand(50)**4 chrom = kron(arange(1,6), ones(1000)) pos = kron(ones(5), arange(1,1001)) data = dict(pv=pv, chrom=chrom, pos=pos) plot_manhattan(pd.DataFrame(data=data)) plt.tight_layout() plt.show() """ import matplotlib.pyplot as plt ax = plt.gca() if ax is None else ax if 'pos' not in df: df['pos'] = arange(df.shape[0]) if 'label' not in df: chrom = df['chrom'].astype(int).astype(str) pos = df['pos'].astype(int).astype(str) df['label'] = ( 'chrom' + chrom + '_pos' + pos) df = _abs_pos(df) if alpha is None: alpha = 0.01 / df.shape[0] ytop = -1.2 * log10(min(df['pv'].min(), alpha)) _plot_chrom_strips(ax, df, ytop) _plot_points(ax, df, alpha, null_style, alt_style) _set_frame(ax, df, ytop) ax.set_ylabel('-log$_{10}$pv') ax.set_xlabel('chromosome') _set_ticks(ax, _chrom_bounds(df)) return ax
def _set_frame(ax, df, ytop): ax.set_ylim(0, ytop) ax.set_xlim(0, df['abs_pos'].max()) ax.spines["right"].set_visible(False) ax.spines["top"].set_visible(False) def _plot_points(ax, df, alpha, null_style, alt_style): null_df = df.loc[df['pv'] >= alpha, :] alt_df = df.loc[df['pv'] < alpha, :] ax.plot(null_df['abs_pos'], -log10(null_df['pv']), '.', ms=5, **null_style) ax.plot(alt_df['abs_pos'], -log10(alt_df['pv']), '.', ms=5, **alt_style) for i in range(alt_df.shape[0]): x = alt_df['abs_pos'].values[i] y = -log10(alt_df['pv'].values[i]) _annotate(ax, x, y, alt_df['label'].values[i]) def _plot_chrom_strips(ax, df, ytop): uchroms = df['chrom'].unique() for i in range(0, len(uchroms), 2): ax.fill_between( df['abs_pos'], 0, ytop, where=df['chrom'] == uchroms[i], facecolor='LightGray', linewidth=0, alpha=0.5) def _set_ticks(ax, chrom_bounds): n = len(chrom_bounds) - 1 xticks = asarray([chrom_bounds[i:i + 2].mean() for i in range(n)]) ax.set_xticks(xticks) ax.tick_params(axis='x', which='both', labelsize=6) ax.set_xticklabels(arange(1, n + 2)) ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') def _abs_pos(df): uchroms = df['chrom'].unique() chrom_ends = [df['pos'][df['chrom'] == c].max() for c in uchroms] offset = flipud(cumsum(chrom_ends)[:-1]) df['abs_pos'] = df['pos'].copy() uchroms = list(reversed(uchroms)) for i in range(len(offset)): ix = df['chrom'] == uchroms[i] df.loc[ix, 'abs_pos'] = df.loc[ix, 'abs_pos'] + offset[i] return df def _chrom_bounds(df): uchroms = df['chrom'].unique() v = [df['abs_pos'][df['chrom'] == c].min() for c in uchroms] return asarray(v + [df['abs_pos'].max()]) def _annotate(ax, x, y, text): ax.annotate( text, xy=(x, y), xytext=(-18, 18), textcoords='offset points', fontsize=6, ha='center', va='bottom', bbox=dict(boxstyle='round,pad=0.2', fc='yellow', alpha=0.3), arrowprops=dict( arrowstyle='->', connectionstyle='arc3,rad=0.5', color='red'))