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3
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1 #!/usr/bin/env python
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2
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3 import sys
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4 import numpy as np
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5 import matplotlib
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6 matplotlib.use('Agg')
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7 import scipy
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8 import pylab
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9 import scipy.cluster.hierarchy as sch
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10 import scipy.spatial.distance as dis
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11 from scipy import stats
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12
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13 # User defined color maps (in addition to matplotlib ones)
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14 bbcyr = {'red': ( (0.0, 0.0, 0.0),
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15 (0.25, 0.0, 0.0),
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16 (0.50, 0.0, 0.0),
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17 (0.75, 1.0, 1.0),
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18 (1.0, 1.0, 1.0)),
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19 'green': ( (0.0, 0.0, 0.0),
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20 (0.25, 0.0, 0.0),
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21 (0.50, 1.0, 1.0),
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22 (0.75, 1.0, 1.0),
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23 (1.0, 0.0, 1.0)),
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24 'blue': ( (0.0, 0.0, 0.0),
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25 (0.25, 1.0, 1.0),
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26 (0.50, 1.0, 1.0),
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27 (0.75, 0.0, 0.0),
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28 (1.0, 0.0, 1.0))}
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29
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30 bbcry = {'red': ( (0.0, 0.0, 0.0),
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31 (0.25, 0.0, 0.0),
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32 (0.50, 0.0, 0.0),
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33 (0.75, 1.0, 1.0),
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34 (1.0, 1.0, 1.0)),
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35 'green': ( (0.0, 0.0, 0.0),
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36 (0.25, 0.0, 0.0),
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37 (0.50, 1.0, 1.0),
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38 (0.75, 0.0, 0.0),
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39 (1.0, 1.0, 1.0)),
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40 'blue': ( (0.0, 0.0, 0.0),
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41 (0.25, 1.0, 1.0),
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42 (0.50, 1.0, 1.0),
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43 (0.75, 0.0, 0.0),
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44 (1.0, 0.0, 1.0))}
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45 my_colormaps = [ ('bbcyr',bbcyr),
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46 ('bbcry',bbcry)]
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47
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48
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49
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50 def read_params(args):
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51 import argparse as ap
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52 import textwrap
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53
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54 p = ap.ArgumentParser( description= "TBA" )
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55
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56 p.add_argument( '--in', '--inp', metavar='INPUT_FILE', type=str,
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57 nargs='?', default=sys.stdin,
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58 help= "the input archive " )
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59
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60 p.add_argument( '--out', metavar='OUTPUT_FILE', type=str,
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61 nargs = '?', default=None,
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62 help= " the output file, image on screen"
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63 " if not specified. " )
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64
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65 p.add_argument( '-m', metavar='method', type=str,
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66 choices=[ "single","complete","average",
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67 "weighted","centroid","median",
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68 "ward" ],
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69 default="average" )
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70
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71 dist_funcs = [ "euclidean","minkowski","cityblock","seuclidean",
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72 "sqeuclidean","cosine","correlation","hamming",
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73 "jaccard","chebyshev","canberra","braycurtis",
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74 "mahalanobis","yule","matching","dice",
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75 "kulsinski","rogerstanimoto","russellrao","sokalmichener",
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76 "sokalsneath","wminkowski","ward"]
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77 p.add_argument( '-d', metavar='distance function', type=str,
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78 choices=dist_funcs,
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79 default="euclidean" )
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80 p.add_argument( '-f', metavar='distance function for features', type=str,
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81 choices=dist_funcs,
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82 default="d" )
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83
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84 p.add_argument( '--dmf', metavar='distance matrix for features', type=str,
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85 default = None )
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86 p.add_argument( '--dms', metavar='distance matrix for samples', type=str,
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87 default = None )
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88
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89
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90 p.add_argument( '-l', metavar='sample label', type=str,
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91 default = None )
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92
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93 p.add_argument( '-s', metavar='scale norm', type=str,
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94 default = 'lin', choices = ['log','lin'])
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95
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96 p.add_argument( '-x', metavar='x cell width', type=float,
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97 default = 0.1)
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98 p.add_argument( '-y', metavar='y cell width', type=float,
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99 default = 0.1 )
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100
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101 p.add_argument( '--minv', metavar='min value', type=float,
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102 default = 0.0 )
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103 p.add_argument( '--maxv', metavar='max value', type=float,
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104 default = None )
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105
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106 p.add_argument( '--xstart', metavar='x coordinate of the top left cell '
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107 'of the values',
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108 type=int, default=1 )
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109 p.add_argument( '--ystart', metavar='y coordinate of the top left cell '
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110 'of the values',
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111 type=int, default=1 )
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112 p.add_argument( '--xstop', metavar='x coordinate of the bottom right cell '
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113 'of the values (default None = last row)',
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114 type=int, default=None )
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115 p.add_argument( '--ystop', metavar='y coordinate of the bottom right cell '
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116 'of the values (default None = last column)',
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117 type=int, default=None )
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118
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119 p.add_argument( '--perc', metavar='percentile for ordering and rows selection', type=int, default=None )
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120 p.add_argument( '--top', metavar='selection of the top N rows', type=int, default=None )
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121 p.add_argument( '--norm', metavar='whether to normalize columns (default 0)', type=int, default=0 )
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122
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123 p.add_argument( '--sdend_h', metavar='height of the sample dendrogram', type=float,
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124 default = 0.1 )
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125 p.add_argument( '--fdend_w', metavar='width of the feature dendrogram', type=float,
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126 default = 0.1 )
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127 p.add_argument( '--cm_h', metavar='height of the colormap', type=float,
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128 default = 0.03 )
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129 p.add_argument( '--cm_ticks', metavar='label for ticks of the colormap', type=str,
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130 default = None )
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131
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132 p.add_argument( '--font_size', metavar='label_font_size', type=int,
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133 default = 7 )
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134 p.add_argument( '--feat_dend_col_th', metavar='Color threshold for feature dendrogram', type=float,
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135 default = None )
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136 p.add_argument( '--sample_dend_col_th', metavar='Color threshold for sample dendrogram', type=float,
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137 default = None )
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138 p.add_argument( '--clust_ncols', metavar='Number of colors for clusters', type=int,
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139 default = 7 )
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140 p.add_argument( '--clust_line_w', metavar='Cluster line width', type=float,
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141 default = 1.0 )
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142 p.add_argument( '--label_cols', metavar='Label colors', type=str,
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143 default = None )
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144 p.add_argument( '--label2cols', metavar='Label to colors mapping file', type=str,
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145 default = None )
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146 p.add_argument( '--sdend_out', metavar='File for storing the samples dendrogram in PhyloXML format', type=str,
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147 default = None )
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148 p.add_argument( '--fdend_out', metavar='File for storing the features dendrogram in PhyloXML format', type=str,
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149 default = None )
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150
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151
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152 p.add_argument( '--pad_inches', metavar='Proportion of figure to be left blank around the plot', type=float,
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153 default = 0.1 )
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154
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155
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156 p.add_argument( '--flabel', metavar='Whether to show the labels for the features', type=int,
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157 default = 0 )
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158 p.add_argument( '--slabel', metavar='Whether to show the labels for the samples', type=int,
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159 default = 0 )
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160
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161 p.add_argument( '--legend', metavar='Whether to show the samples to label legend', type=int,
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162 default = 0 )
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163 p.add_argument( '--legend_font_size', metavar='Legend font size', type=int,
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164 default = 7 )
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165 p.add_argument( '--legend_ncol', metavar='Number of columns for the legend', type=int,
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166 default = 3 )
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167 p.add_argument( '--grid', metavar='Whether to show the grid (only black for now)', type=int,
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168 default = 0 )
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169
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170 col_maps = ['Accent', 'Blues', 'BrBG', 'BuGn', 'BuPu', 'Dark2', 'GnBu',
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171 'Greens', 'Greys', 'OrRd', 'Oranges', 'PRGn', 'Paired',
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172 'Pastel1', 'Pastel2', 'PiYG', 'PuBu', 'PuBuGn', 'PuOr',
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173 'PuRd', 'Purples', 'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn',
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174 'Reds', 'Set1', 'Set2', 'Set3', 'Spectral', 'YlGn', 'YlGnBu',
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175 'YlOrBr', 'YlOrRd', 'afmhot', 'autumn', 'binary', 'bone',
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176 'brg', 'bwr', 'cool', 'copper', 'flag', 'gist_earth',
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177 'gist_gray', 'gist_heat', 'gist_ncar', 'gist_rainbow',
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178 'gist_stern', 'gist_yarg', 'gnuplot', 'gnuplot2', 'gray',
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179 'hot', 'hsv', 'jet', 'ocean', 'pink', 'prism', 'rainbow',
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180 'seismic', 'spectral', 'spring', 'summer', 'terrain', 'winter'] + [n for n,c in my_colormaps]
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181 p.add_argument( '-c', metavar='colormap', type=str,
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182 choices = col_maps, default = 'jet' )
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183
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184 return vars(p.parse_args())
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185
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186 # Predefined colors for dendrograms brances and class labels
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187 colors = [ "#B22222","#006400","#0000CD","#9400D3","#696969","#8B4513",
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188 "#FF1493","#FF8C00","#3CB371","#00Bfff","#CDC9C9","#FFD700",
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189 "#2F4F4F","#FF0000","#ADFF2F","#B03060" ]
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190
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191 def samples2classes_panel(fig, samples, s2l, idx1, idx2, height, xsize, cols, legendon, fontsize, label2cols, legend_ncol ):
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192 from matplotlib.patches import Rectangle
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193 samples2labels = dict([(l[0],l[1])
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194 for l in [ll.strip().split('\t')
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195 for ll in open(s2l)] if len(l) > 1])
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196
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197 if label2cols:
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198 labels2colors = dict([(l[0],l[1]) for l in [ll.strip().split('\t') for ll in open(label2cols)]])
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199 else:
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200 cs = cols if cols else colors
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201 labels2colors = dict([(l,cs[i%len(cs)]) for i,l in enumerate(set(samples2labels.values()))])
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202 ax1 = fig.add_axes([0.,1.0,1.0,height],frameon=False)
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203 ax1.set_xticks([])
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204 ax1.set_yticks([])
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205 ax1.set_ylim( [0.0, height] )
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206 ax1.set_xlim( [0.0, xsize] )
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207 step = xsize / float(len(samples))
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208 labels = set()
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209 added_labels = set()
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210 for i,ind in enumerate(idx2):
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211 if not samples[ind] in samples2labels or \
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212 not samples2labels[samples[ind]] in labels2colors:
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213 fc, ll = "k", None
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214 else:
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215 ll = samples2labels[samples[ind]]
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216 ll = None if ll in added_labels else ll
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217 added_labels.add( ll )
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218 fc = labels2colors[samples2labels[samples[ind]]]
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219
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220 rect = Rectangle( [float(i)*step, 0.0], step, height,
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221 facecolor = fc,
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222 label = ll,
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223 edgecolor='b', lw = 0.0)
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224 labels.add( ll )
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225 ax1.add_patch(rect)
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226 ax1.autoscale_view()
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227
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228 if legendon:
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229 ax1.legend( loc = 2, ncol = legend_ncol, bbox_to_anchor=(1.01, 3.),
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230 borderpad = 0.0, labelspacing = 0.0,
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231 handlelength = 0.5, handletextpad = 0.3,
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232 borderaxespad = 0.0, columnspacing = 0.3,
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233 prop = {'size':fontsize}, frameon = False)
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234
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235 def samples_dend_panel( fig, Z, Z2, ystart, ylen, lw ):
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236 ax2 = fig.add_axes([0.0,1.0+ystart,1.0,ylen], frameon=False)
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237 Z2['color_list'] = [c.replace('b','k') for c in Z2['color_list']]
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238 mh = max(Z[:,2])
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239 sch._plot_dendrogram( Z2['icoord'], Z2['dcoord'], Z2['ivl'],
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240 Z.shape[0] + 1, Z.shape[0] + 1,
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241 mh, 'top', no_labels=True,
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242 color_list=Z2['color_list'] )
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243 for coll in ax2.collections:
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244 coll._linewidths = (lw,)
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245 ax2.set_xticks([])
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246 ax2.set_yticks([])
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247 ax2.set_xticklabels([])
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248
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249 def features_dend_panel( fig, Z, Z2, width, lw ):
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250 ax1 = fig.add_axes([-width,0.0,width,1.0], frameon=False)
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251 Z2['color_list'] = [c.replace('b','k').replace('x','b') for c in Z2['color_list']]
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252 mh = max(Z[:,2])
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253 sch._plot_dendrogram(Z2['icoord'], Z2['dcoord'], Z2['ivl'], Z.shape[0] + 1, Z.shape[0] + 1, mh, 'right', no_labels=True, color_list=Z2['color_list'])
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254 for coll in ax1.collections:
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255 coll._linewidths = (lw,)
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256 ax1.set_xticks([])
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257 ax1.set_yticks([])
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258 ax1.set_xticklabels([])
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259
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260
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261 def add_cmap( cmapdict, name ):
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262 my_cmap = matplotlib.colors.LinearSegmentedColormap(name,cmapdict,256)
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263 pylab.register_cmap(name=name,cmap=my_cmap)
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264
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265 def init_fig(xsize,ysize,ncol):
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266 fig = pylab.figure(figsize=(xsize,ysize))
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267 sch._link_line_colors = colors[:ncol]
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268 return fig
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269
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270 def heatmap_panel( fig, D, minv, maxv, idx1, idx2, cm_name, scale, cols, rows, label_font_size, cb_offset, cb_l, flabelson, slabelson, cm_ticks, gridon, bar_offset ):
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271 cm = pylab.get_cmap(cm_name)
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272 bottom_col = [ cm._segmentdata['red'][0][1],
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273 cm._segmentdata['green'][0][1],
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274 cm._segmentdata['blue'][0][1] ]
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275 axmatrix = fig.add_axes( [0.0,0.0,1.0,1.0],
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276 axisbg=bottom_col)
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277 if any([c < 0.95 for c in bottom_col]):
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278 axmatrix.spines['right'].set_color('none')
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279 axmatrix.spines['left'].set_color('none')
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280 axmatrix.spines['top'].set_color('none')
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281 axmatrix.spines['bottom'].set_color('none')
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282 norm_f = matplotlib.colors.LogNorm if scale == 'log' else matplotlib.colors.Normalize
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283 im = axmatrix.matshow( D, norm = norm_f( vmin=minv if minv > 0.0 else None,
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284 vmax=maxv),
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285 aspect='auto', origin='lower', cmap=cm, vmax=maxv)
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286
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287 axmatrix2 = axmatrix.twinx()
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288 axmatrix3 = axmatrix.twiny()
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289
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290 axmatrix.set_xticks([])
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291 axmatrix2.set_xticks([])
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292 axmatrix3.set_xticks([])
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293 axmatrix.set_yticks([])
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294 axmatrix2.set_yticks([])
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295 axmatrix3.set_yticks([])
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296
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297 axmatrix.set_xticklabels([])
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298 axmatrix2.set_xticklabels([])
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299 axmatrix3.set_xticklabels([])
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300 axmatrix.set_yticklabels([])
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301 axmatrix2.set_yticklabels([])
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302 axmatrix3.set_yticklabels([])
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303
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304 if any([c < 0.95 for c in bottom_col]):
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305 axmatrix2.spines['right'].set_color('none')
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306 axmatrix2.spines['left'].set_color('none')
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307 axmatrix2.spines['top'].set_color('none')
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308 axmatrix2.spines['bottom'].set_color('none')
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309 if any([c < 0.95 for c in bottom_col]):
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310 axmatrix3.spines['right'].set_color('none')
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311 axmatrix3.spines['left'].set_color('none')
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312 axmatrix3.spines['top'].set_color('none')
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313 axmatrix3.spines['bottom'].set_color('none')
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314 if flabelson:
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315 axmatrix2.set_yticks(np.arange(len(rows))+0.5)
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316 axmatrix2.set_yticklabels([rows[r] for r in idx1],size=label_font_size,va='center')
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317 if slabelson:
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318 axmatrix.set_xticks(np.arange(len(cols)))
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319 axmatrix.set_xticklabels([cols[r] for r in idx2],size=label_font_size,rotation=90,va='top',ha='center')
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320 axmatrix.tick_params(length=0)
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321 axmatrix2.tick_params(length=0)
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322 axmatrix3.tick_params(length=0)
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323 axmatrix2.set_ylim(0,len(rows))
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324
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325 if gridon:
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326 axmatrix.set_yticks(np.arange(len(idx1)-1)+0.5)
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327 axmatrix.set_xticks(np.arange(len(idx2))+0.5)
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328 axmatrix.grid( True )
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329 ticklines = axmatrix.get_xticklines()
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330 ticklines.extend( axmatrix.get_yticklines() )
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331 #gridlines = axmatrix.get_xgridlines()
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332 #gridlines.extend( axmatrix.get_ygridlines() )
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333
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334 for line in ticklines:
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335 line.set_linewidth(3)
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336
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337 if cb_l > 0.0:
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338 axcolor = fig.add_axes([0.0,1.0+bar_offset*1.25,1.0,cb_l])
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339 cbar = fig.colorbar(im, cax=axcolor, orientation='horizontal')
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340 cbar.ax.tick_params(labelsize=label_font_size)
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341 if cm_ticks:
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342 cbar.ax.set_xticklabels( cm_ticks.split(":") )
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343
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344
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345 def read_table( fin, xstart,xstop,ystart,ystop, percentile = None, top = None, norm = False ):
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346 mat = [l.rstrip().split('\t') for l in open( fin )]
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347
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348 if fin.endswith(".biom"):
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349 sample_labels = mat[1][1:-1]
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350 m = [(mm[-1]+"; OTU"+mm[0],np.array([float(f) for f in mm[1:-1]])) for mm in mat[2:]]
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351 #feat_labels = [m[-1].replace(";","_").replace(" ","")+m[0] for m in mat[2:]]
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352 #print len(feat_labels)
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353 #print len(sample_labels)
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354 else:
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355 sample_labels = mat[0][xstart:xstop]
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356 m = [(mm[xstart-1],np.array([float(f) for f in mm[xstart:xstop]])) for mm in mat[ystart:ystop]]
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357
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358 if norm:
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359 msums = [0.0 for l in m[0][1]]
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360 for mm in m:
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361 for i,v in enumerate(mm[1]):
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362 msums[i] += v
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363
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364 if top and not percentile:
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365 percentile = 90
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366
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367 if percentile:
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368 m = sorted(m,key=lambda x:-stats.scoreatpercentile(x[1],percentile))
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369 if top:
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370 if fin.endswith(".biom"):
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371 #feat_labels = [mm[-1].replace(";","_").replace(" ","")+mm[0] for mm in m[:top]]
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372 feat_labels = [mm[0] for mm in m[:top]]
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373 else:
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374 feat_labels = [mm[0] for mm in m[:top]]
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375 if norm:
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376 m = [np.array([n/v for n,v in zip(mm[1],msums)]) for mm in m[:top]]
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377 else:
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378 m = [mm[1] for mm in m[:top]]
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379 else:
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380 if fin.endswith(".biom"):
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381 feat_labels = [mm[0] for mm in m]
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382 else:
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383 feat_labels = [mm[0] for mm in m]
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384 if norm:
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385 m = [np.array([n/v for n,v in zip(mm[1],msums)]) for mm in m]
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386 else:
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387 m = [mm[1] for mm in m]
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388 #m = [mm[1] for mm in m]
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389
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390 D = np.matrix( np.array( m ) )
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391
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392 return D, feat_labels, sample_labels
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393
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394 def read_dm( fin, n ):
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395 mat = [[float(f) for f in l.strip().split('\t')] for l in open( fin )]
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396 nc = sum([len(r) for r in mat])
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397
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398 if nc == n*n:
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399 dm = []
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400 for i in range(n):
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401 dm += mat[i][i+1:]
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402 return np.array(dm)
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403 if nc == (n*n-n)/2:
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404 dm = []
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405 for i in range(n):
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406 dm += mat[i]
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407 return np.array(dm)
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408 sys.stderr.write( "Error in reading the distance matrix\n" )
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409 sys.exit()
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410
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411
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412 def exp_newick( inp, labels, outfile, tree_format = 'phyloxml' ):
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413 n_leaves = int(inp[-1][-1])
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414 from Bio import Phylo
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415 import collections
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416 from Bio.Phylo.BaseTree import Tree as BTree
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417 from Bio.Phylo.BaseTree import Clade as BClade
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418 tree = BTree()
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419 tree.root = BClade()
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420
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421 subclades = {}
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422 sb_cbl = {}
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423
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424 for i,(fr,to,bl,nsub) in enumerate( inp ):
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425 if fr < n_leaves:
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426 fr_c = BClade(branch_length=-1.0,name=labels[int(fr)])
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427 subclades[fr] = fr_c
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428 sb_cbl[fr] = bl
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429 if to < n_leaves:
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430 to_c = BClade(branch_length=-1.0,name=labels[int(to)])
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431 subclades[to] = to_c
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432 sb_cbl[to] = bl
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433 for i,(fr,to,bl,nsub) in enumerate( inp ):
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434 fr_c = subclades[fr]
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435 to_c = subclades[to]
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436 cur_c = BClade(branch_length=bl)
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437 cur_c.clades.append( fr_c )
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|
438 cur_c.clades.append( to_c )
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439 subclades[i+n_leaves] = cur_c
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440
|
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441 def reset_rec( clade, fath_bl ):
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442 if clade.branch_length < 0:
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443 clade.branch_length = fath_bl
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|
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444 return
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445 for c in clade.clades:
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446 reset_rec( c, clade.branch_length )
|
|
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447 clade.branch_length = fath_bl-clade.branch_length
|
|
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448
|
|
|
449 tree.root = cur_c
|
|
|
450 reset_rec( tree.root, 0.0 )
|
|
|
451 tree.root.branch_length = 0.0
|
|
|
452 Phylo.write(tree, outfile, tree_format )
|
|
|
453
|
|
|
454 def hclust( fin, fout,
|
|
|
455 method = "average",
|
|
|
456 dist_func = "euclidean",
|
|
|
457 feat_dist_func = "d",
|
|
|
458 xcw = 0.1,
|
|
|
459 ycw = 0.1,
|
|
|
460 scale = 'lin',
|
|
|
461 minv = 0.0,
|
|
|
462 maxv = None,
|
|
|
463 xstart = 1,
|
|
|
464 ystart = 1,
|
|
|
465 xstop = None,
|
|
|
466 ystop = None,
|
|
|
467 percentile = None,
|
|
|
468 top = None,
|
|
|
469 norm = False,
|
|
|
470 cm_name = 'jet',
|
|
|
471 s2l = None,
|
|
|
472 label_font_size = 7,
|
|
|
473 feat_dend_col_th = None,
|
|
|
474 sample_dend_col_th = None,
|
|
|
475 clust_ncols = 7,
|
|
|
476 clust_line_w = 1.0,
|
|
|
477 label_cols = None,
|
|
|
478 sdend_h = 0.1,
|
|
|
479 fdend_w = 0.1,
|
|
|
480 cm_h = 0.03,
|
|
|
481 dmf = None,
|
|
|
482 dms = None,
|
|
|
483 legendon = False,
|
|
|
484 label2cols = None,
|
|
|
485 flabelon = True,
|
|
|
486 slabelon = True,
|
|
|
487 cm_ticks = None,
|
|
|
488 legend_ncol = 3,
|
|
|
489 pad_inches = None,
|
|
|
490 legend_font_size = 7,
|
|
|
491 gridon = 0,
|
|
|
492 sdend_out = None,
|
|
|
493 fdend_out= None):
|
|
|
494
|
|
|
495 if label_cols and label_cols.count("-"):
|
|
|
496 label_cols = label_cols.split("-")
|
|
|
497
|
|
|
498 for n,c in my_colormaps:
|
|
|
499 add_cmap( c, n )
|
|
|
500
|
|
|
501 if feat_dist_func == 'd':
|
|
|
502 feat_dist_func = dist_func
|
|
|
503
|
|
|
504 D, feat_labels, sample_labels = read_table(fin,xstart,xstop,ystart,ystop,percentile,top,norm)
|
|
|
505
|
|
|
506 ylen,xlen = D[:].shape
|
|
|
507 Dt = D.transpose()
|
|
|
508
|
|
|
509 size_cx, size_cy = xcw, ycw
|
|
|
510
|
|
|
511 xsize, ysize = max(xlen*size_cx,2.0), max(ylen*size_cy,2.0)
|
|
|
512 ydend_offset = 0.025*8.0/ysize if s2l else 0.0
|
|
|
513
|
|
|
514 fig = init_fig(xsize,ysize,clust_ncols)
|
|
|
515
|
|
|
516 nfeats, nsamples = len(D), len(Dt)
|
|
|
517
|
|
|
518 if dmf:
|
|
|
519 p1 = read_dm( dmf, nfeats )
|
|
|
520 Y1 = sch.linkage( p1, method=method )
|
|
|
521 else:
|
|
|
522 p1 = dis.pdist( D, feat_dist_func )
|
|
|
523 Y1 = sch.linkage( p1, method=method ) # , metric=feat_dist_func )
|
|
|
524 #Y1 = sch.linkage( D, method=method, metric=feat_dist_func )
|
|
|
525 Z1 = sch.dendrogram(Y1, no_plot=True, color_threshold=feat_dend_col_th)
|
|
|
526
|
|
|
527 if fdend_out:
|
|
|
528 exp_newick( Y1, feat_labels, fdend_out )
|
|
|
529
|
|
|
530 if dms:
|
|
|
531 p2 = read_dm( dms, nsamples )
|
|
|
532 Y2 = sch.linkage( p2, method=method )
|
|
|
533 else:
|
|
|
534 p2 = dis.pdist( Dt, dist_func )
|
|
|
535 Y2 = sch.linkage( p2, method=method ) # , metric=dist_func )
|
|
|
536 #Y2 = sch.linkage( Dt, method=method, metric=dist_func )
|
|
|
537 Z2 = sch.dendrogram(Y2, no_plot=True, color_threshold=sample_dend_col_th)
|
|
|
538
|
|
|
539 if sdend_out:
|
|
|
540 exp_newick( Y2, sample_labels, sdend_out )
|
|
|
541
|
|
|
542 if fdend_w > 0.0:
|
|
|
543 features_dend_panel(fig, Y1, Z1, fdend_w*8.0/xsize, clust_line_w )
|
|
|
544 if sdend_h > 0.0:
|
|
|
545 samples_dend_panel(fig, Y2, Z2, ydend_offset, sdend_h*8.0/ysize, clust_line_w)
|
|
|
546
|
|
|
547 idx1, idx2 = Z1['leaves'], Z2['leaves']
|
|
|
548 D = D[idx1,:][:,idx2]
|
|
|
549
|
|
|
550 if s2l:
|
|
|
551 samples2classes_panel( fig, sample_labels, s2l, idx1, idx2, 0.025*8.0/ysize, xsize, label_cols, legendon, legend_font_size, label2cols, legend_ncol )
|
|
|
552 heatmap_panel( fig, D, minv, maxv, idx1, idx2, cm_name, scale, sample_labels, feat_labels, label_font_size, -cm_h*8.0/ysize, cm_h*0.8*8.0/ysize, flabelon, slabelon, cm_ticks, gridon, ydend_offset+sdend_h*8.0/ysize )
|
|
|
553
|
|
|
554 fig.savefig( fout, bbox_inches='tight',
|
|
|
555 pad_inches = pad_inches,
|
|
|
556 dpi=300) if fout else pylab.show()
|
|
|
557
|
|
|
558 if __name__ == '__main__':
|
|
|
559 pars = read_params( sys.argv )
|
|
|
560
|
|
|
561 hclust( fin = pars['in'],
|
|
|
562 fout = pars['out'],
|
|
|
563 method = pars['m'],
|
|
|
564 dist_func = pars['d'],
|
|
|
565 feat_dist_func = pars['f'],
|
|
|
566 xcw = pars['x'],
|
|
|
567 ycw = pars['y'],
|
|
|
568 scale = pars['s'],
|
|
|
569 minv = pars['minv'],
|
|
|
570 maxv = pars['maxv'],
|
|
|
571 xstart = pars['xstart'],
|
|
|
572 ystart = pars['ystart'],
|
|
|
573 xstop = pars['xstop'],
|
|
|
574 ystop = pars['ystop'],
|
|
|
575 percentile = pars['perc'],
|
|
|
576 top = pars['top'],
|
|
|
577 norm = pars['norm'],
|
|
|
578 cm_name = pars['c'],
|
|
|
579 s2l = pars['l'],
|
|
|
580 label_font_size = pars['font_size'],
|
|
|
581 feat_dend_col_th = pars['feat_dend_col_th'],
|
|
|
582 sample_dend_col_th = pars['sample_dend_col_th'],
|
|
|
583 clust_ncols = pars['clust_ncols'],
|
|
|
584 clust_line_w = pars['clust_line_w'],
|
|
|
585 label_cols = pars['label_cols'],
|
|
|
586 sdend_h = pars['sdend_h'],
|
|
|
587 fdend_w = pars['fdend_w'],
|
|
|
588 cm_h = pars['cm_h'],
|
|
|
589 dmf = pars['dmf'],
|
|
|
590 dms = pars['dms'],
|
|
|
591 legendon = pars['legend'],
|
|
|
592 label2cols = pars['label2cols'],
|
|
|
593 flabelon = pars['flabel'],
|
|
|
594 slabelon = pars['slabel'],
|
|
|
595 cm_ticks = pars['cm_ticks'],
|
|
|
596 legend_ncol = pars['legend_ncol'],
|
|
|
597 pad_inches = pars['pad_inches'],
|
|
|
598 legend_font_size = pars['legend_font_size'],
|
|
|
599 gridon = pars['grid'],
|
|
|
600 sdend_out = pars['sdend_out'],
|
|
|
601 fdend_out = pars['fdend_out'],
|
|
|
602 )
|
|
|
603
|
