ImageScripts/FISH_Imaris.py at master · Phlya/ImageScripts · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import glob
import seaborn as sns
from scipy.spatial.distance import pdist
from itertools import combinations
from scipy.stats import wilcoxon
from functools import partial
import os
from tkinter import filedialog
from tkinter import *

def read_data_multicolor(f, n):
    d = pd.read_csv(f, skiprows=3)
    d = d.sort_values('ID').iloc[:, 0:3].reset_index(drop=True)
    d['Image'] = f.split('/')[-1].split(' - ')[0]
    if d.shape[0]%n!=0:
        raise ValueError("Number of measurements incompatible with number of\
                         probes! Please check input data for missing or extra\
                         spots. File %s." % f)
    d['colour'] = list(np.arange(n))*(d.shape[0]//n)
    d['group'] = np.repeat(np.arange(d.shape[0]//n), n)
    return d

def distance(group, probe_names):
    return pd.Series(pdist(group.iloc[:, :3]), index=combinations(probe_names, 2))

def make_distances_multicolour(coords, probe_names):
    dists = coords.groupby(['Image', 'group']).apply(distance, probe_names=probe_names)
    return pd.DataFrame(dists).reset_index()

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--probes", default=None, type=str, required=False)
    parser.add_argument("--folder", default=None, type=str, required=False)
    args = parser.parse_args()
    if args.probes is None: #Get probes interactively
#         def get_number():
#             global w
#             n = w.get()
#             global N_probes
#             N_probes.set(int(n))
#             root.destroy()

#         root = Tk()
#         root.title('Number of probes')

#         w = Spinbox(root, from_=0, to=10)
#         w.pack()

#         b = Button(root, text="OK", command=get_number)
#         b.pack()

#         N_probes = IntVar()

#         root.mainloop()
#         N = N_probes.get()

        root = Tk()
        root.title('Probe names')
        probes = [StringVar() for i in range(4)]
        es = [Entry(root, textvariable=p) for p in probes]
        for e in es:
            e.pack()

        def callback():
            for e in es:
                e.get()
            root.destroy()

        b = Button(root, text="Done", width=10, command=callback)
        b.pack()

        mainloop()
        probes = [probe.get() for probe in probes]
        probes = list(filter(lambda x: bool(x) is True, probes))
        N = len(probes)
    else:
        probes = args.probes.split(',')
        N = len(probes)

    print('Got %s probes: %s' % (N, probes))

    if args.folder is None:
        def browse_button():
            # Allow user to select a directory and store it in global var
            # called folder_path
            global folder_path
            filename = filedialog.askdirectory()
            folder_path.set(filename)
            print(filename)

        root = Tk()
        folder_path = StringVar()
        browse_button()
        root.destroy()
        folder = folder_path.get()
    else:
        folder = args.folder

    print("Analysing folder %s" % folder)

    files = filter(lambda x: os.path.isfile and x.endswith('.csv'), glob.glob(folder+'/*/*') + glob.glob(folder+'/*'))

    f_dist = partial(make_distances_multicolour, probe_names=probes)
    f_read = partial(read_data_multicolor, n=N)
    data = pd.concat(map(f_dist, map(f_read, files))).reset_index(drop=True)
    f_sort = partial(sorted, key=lambda x: probes.index(x))
    data.columns = list(data.columns[:2])+[tuple(i) for i in map(sorted, data.columns[2:])]
    data.to_csv(os.path.join(folder, 'Distances.tsv'), index=False, sep='\t')

    order = data.columns[2:]
    sns.set_context('notebook')
    f, ax = plt.subplots(figsize=(8, 6))
    sns.swarmplot(data=data, order=order, ax=ax)
    sns.boxplot(data=data, order=order, palette=['grey'], showfliers=False,
                ax=ax)
    ax.set_ylabel('Distance, $\mu m$')
    ax.set_xlabel('Probe pair')
    ax.set_title('n=%s' % data.shape[0])
    plt.savefig(os.path.join(folder, 'Plot.png'), dpi=150)

    pvals = pd.DataFrame(index=order, columns=order)
    for i, j  in combinations(data.columns[2:], 2):
        _, p = wilcoxon(data[i], data[j])
        pvals.loc[i, j] = p
        pvals.loc[j, i] = p
    pvals.to_csv(os.path.join(folder, 'Pvals_Wilcoxon.tsv'), sep='\t')