| 1 | #!/usr/bin/env python3 |
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| 2 | # -*- coding: utf-8 -*- |
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| 3 | import sys |
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| 4 | import numpy as np |
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| 5 | from sklearn import manifold |
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| 6 | import matplotlib.pyplot as plt |
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| 7 | from mpl_toolkits.mplot3d import Axes3D |
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| 8 | from matplotlib import cm |
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| 9 | import argparse |
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| 10 | |
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| 11 | def rand_jitter(arr): |
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| 12 | stdev = arr.max() / 100. |
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| 13 | return arr + np.random.randn(len(arr)) * stdev * 2 |
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| 14 | |
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| 15 | |
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| 16 | def read_file(fname, separator): |
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| 17 | distances = np.genfromtxt(fname, delimiter=separator) |
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| 18 | if np.isnan(distances[0][len(distances[0])-1]):#separator after the last element in row |
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| 19 | distances = np.array([row[:-1] for row in distances]) |
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| 20 | return distances |
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| 21 | |
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| 22 | |
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| 23 | def compute_mds(distance_matrix, dim): |
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| 24 | seed = np.random.RandomState(seed=3) |
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| 25 | mds = manifold.MDS(n_components=int(dim), metric=True, max_iter=3000, eps=1e-9, random_state=seed, dissimilarity="precomputed") |
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| 26 | embed = mds.fit(distance_matrix).embedding_ |
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| 27 | return embed |
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| 28 | |
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| 29 | |
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| 30 | def compute_variances(embed): |
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| 31 | variances = [] |
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| 32 | for i in range(len(embed[0])): |
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| 33 | variances.append(np.var(embed[:,i])) |
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| 34 | percent_variances = [sum(variances[:i+1])/sum(variances) for i in range(len(variances))] |
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| 35 | return percent_variances |
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| 36 | |
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| 37 | |
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| 38 | def plot(coordinates, dimensions, jitter=0, outname=""): |
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| 39 | fig = plt.figure() |
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| 40 | |
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| 41 | if dimensions < 3: |
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| 42 | ax = fig.add_subplot(111) |
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| 43 | else: |
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| 44 | ax = fig.add_subplot(111, projection='3d') |
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| 45 | |
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| 46 | add_jitter = lambda tab : rand_jitter(tab) if jitter==1 else tab |
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| 47 | |
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| 48 | x_dim = len(coordinates[0]) |
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| 49 | y_dim = len(coordinates) |
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| 50 | |
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| 51 | ax.scatter(*[add_jitter(coordinates[:, i]) for i in range(x_dim)], alpha=0.5) |
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| 52 | |
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| 53 | plt.title('Phenotypes distances') |
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| 54 | plt.tight_layout() |
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| 55 | plt.axis('tight') |
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| 56 | |
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| 57 | if outname == "": |
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| 58 | plt.show() |
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| 59 | |
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| 60 | else: |
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| 61 | plt.savefig(outname+".pdf") |
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| 62 | |
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| 63 | |
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| 64 | def main(filename,dimensions=3, outname="", jitter=0, separator='\t'): |
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| 65 | distances = read_file(filename, separator) |
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| 66 | embed = compute_mds(distances, dimensions) |
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| 67 | |
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| 68 | variances_perc = compute_variances(embed) |
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| 69 | for i,vc in enumerate(variances_perc): |
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| 70 | print(i+1,"dimension:",vc) |
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| 71 | |
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| 72 | dimensions = int(dimensions) |
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| 73 | if dimensions == 1: |
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| 74 | embed = np.array([np.insert(e, 0, 0, axis=0) for e in embed]) |
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| 75 | |
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| 76 | plot(embed, dimensions) |
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| 77 | |
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| 78 | |
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| 79 | if __name__ == '__main__': |
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| 80 | parser = argparse.ArgumentParser() |
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| 81 | parser.add_argument('--in', dest='input', required=True, help='input file with dissimilarity matrix') |
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| 82 | parser.add_argument('--out', dest='output', required=False, help='output file name without extension') |
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| 83 | parser.add_argument('--dim', required=False, help='number of dimensions of the new space') |
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| 84 | parser.add_argument('--sep', required=False, help='separator of the source file') |
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| 85 | parser.add_argument('--j', required=False, help='for j=1 random jitter is added to the plot') |
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| 86 | |
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| 87 | args = parser.parse_args() |
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| 88 | set_value = lambda value, default : default if value == None else value |
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| 89 | main(args.input, set_value(args.dim, 3), set_value(args.output, ""), set_value(args.j, 0), set_value(args.sep, "\t")) |
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