GitHub - slowkow/harmonypy: 🎼 Integrate multiple high-dimensional datasets with fuzzy k-means and locally linear adjustments. (original) (raw)

harmonypy

Harmony is an algorithm for integrating multiple high-dimensional datasets.

harmonypy is a port of the harmony R package by Ilya Korsunsky.

Example

This animation shows the Harmony alignment of three single-cell RNA-seq datasets from different donors.

→ How to make this animation.

Installation

This package has been tested with Python 3.7.

Use pip to install:

Usage

Here is a brief example using the data that comes with the R package:

Load data

import pandas as pd

meta_data = pd.read_csv("data/meta.tsv.gz", sep = "\t") vars_use = ['dataset']

meta_data

cell_id dataset nGene percent_mito cell_type

0 half_TGAAATTGGTCTAG half 3664 0.017722 jurkat

1 half_GCGATATGCTGATG half 3858 0.029228 t293

2 half_ATTTCTCTCACTAG half 4049 0.015966 jurkat

3 half_CGTAACGACGAGAG half 3443 0.020379 jurkat

4 half_ACGCCTTGTTTACC half 2813 0.024774 t293

.. ... ... ... ... ...

295 t293_TTACGTACGACACT t293 4152 0.033997 t293

296 t293_TAGAATTGTTGGTG t293 3097 0.021769 t293

297 t293_CGGATAACACCACA t293 3157 0.020411 t293

298 t293_GGTACTGAGTCGAT t293 2685 0.027846 t293

299 t293_ACGCTGCTTCTTAC t293 3513 0.021240 t293

data_mat = pd.read_csv("data/pcs.tsv.gz", sep = "\t") data_mat = np.array(data_mat)

data_mat[:5,:5]

array([[ 0.0071695 , -0.00552724, -0.0036281 , -0.00798025, 0.00028931],

[-0.011333 , 0.00022233, -0.00073589, -0.00192452, 0.0032624 ],

[ 0.0091214 , -0.00940727, -0.00106816, -0.0042749 , -0.00029096],

[ 0.00866286, -0.00514987, -0.0008989 , -0.00821785, -0.00126997],

[-0.00953977, 0.00222714, -0.00374373, -0.00028554, 0.00063737]])

meta_data.shape # 300 cells, 5 variables

(300, 5)

data_mat.shape # 300 cells, 20 PCs

(300, 20)

Run Harmony

import harmonypy as hm ho = hm.run_harmony(data_mat, meta_data, vars_use)

Write the adjusted PCs to a new file.

res = pd.DataFrame(ho.Z_corr) res.columns = ['X{}'.format(i + 1) for i in range(res.shape[1])] res.to_csv("data/adj.tsv.gz", sep = "\t", index = False)