copairs 0.4.1

Find pairs and compute metrics between them

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)]}