Skip to content

🎼 Integrate multiple high-dimensional datasets with fuzzy k-means and locally linear adjustments.

License

Notifications You must be signed in to change notification settings

slowkow/harmonypy

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

72 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

harmonypy

Latest PyPI Version PyPI Downloads tests DOI

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:

pip install harmonypy

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)