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copairs

Find pairs and compute metrics between them.

Installation

pip install git+https://github.com/cytomining/copairs.git@v0.4.1

Usage

Data

Say you have a dataset with 20 samples taken in 3 plates p1, p2, p3, each plate is composed of 5 wells w1, w2, w3, w4, w5, and each well has one or more labels (t1, t2, t3, t4) assigned.

import pandas as pd
import random

random.seed(0)
n_samples = 20
dframe = pd.DataFrame({
    'plate': [random.choice(['p1', 'p2', 'p3']) for _ in range(n_samples)],
    'well': [random.choice(['w1', 'w2', 'w3', 'w4', 'w5']) for _ in range(n_samples)],
    'label': [random.choice(['t1', 't2', 't3', 't4']) for _ in range(n_samples)]
})
dframe = dframe.drop_duplicates()
dframe = dframe.sort_values(by=['plate', 'well', 'label'])
dframe = dframe.reset_index(drop=True)
plate well label
0 p1 w2 t4
1 p1 w3 t2
2 p1 w3 t4
3 p1 w4 t1
4 p1 w4 t3
5 p2 w1 t1
6 p2 w2 t1
7 p2 w3 t1
8 p2 w3 t2
9 p2 w3 t3
10 p2 w4 t2
11 p2 w5 t1
12 p2 w5 t3
13 p3 w1 t3
14 p3 w1 t4
15 p3 w4 t2
16 p3 w5 t2
17 p3 w5 t4

Getting valid pairs

To get pairs of samples that share the same label but comes from different plates at different well positions:

from copairs import Matcher
matcher = Matcher(dframe, ['plate', 'well', 'label'], seed=0)
pairs_dict = matcher.get_all_pairs(sameby=['label'], diffby=['plate', 'well'])

pairs_dict is a label_id: pairs dictionary containing the list of valid pairs for every unique value of labels

{'t4': [(0, 17), (0, 14), (17, 2), (2, 14)],
 't2': [(1, 16), (1, 10), (1, 15), (8, 16), (8, 15), (10, 16)],
 't1': [(3, 11), (3, 5), (3, 6), (3, 7)],
 't3': [(9, 4), (9, 13), (13, 4), (13, 12), (4, 12)]}

Getting valid pairs from a multilabel column

For eficiency reasons, you may not want to have duplicated rows. You can group all the labels in a single row and use MatcherMultilabel to find the corresponding pairs:

dframe_multi = dframe.groupby(['plate', 'well'])['label'].unique().reset_index()
plate well label
0 p1 w2 ['t4']
1 p1 w3 ['t2', 't4']
2 p1 w4 ['t1', 't3']
3 p2 w1 ['t1']
4 p2 w2 ['t1']
5 p2 w3 ['t1', 't2', 't3']
6 p2 w4 ['t2']
7 p2 w5 ['t1', 't3']
8 p3 w1 ['t3', 't4']
9 p3 w4 ['t2']
10 p3 w5 ['t2', 't4'] 
from copairs import MatcherMultilabel
matcher_multi = MatcherMultilabel(dframe_multi,
                                  columns=['plate', 'well', 'label'],
                                  multilabel_col='label',
                                  seed=0)
pairs_multi = matcher_multi.get_all_pairs(sameby=['label'],
                                          diffby=['plate', 'well'])

pairs_multi is also a label_id: pairs dictionary with the same structure discussed before:

{'t4': [(0, 10), (0, 8), (10, 1), (1, 8)],
 't2': [(1, 10), (1, 6), (1, 9), (5, 10), (5, 9), (6, 10)],
 't1': [(2, 7), (2, 3), (2, 4), (2, 5)],
 't3': [(5, 2), (5, 8), (8, 2), (8, 7), (2, 7)]}