harmonypy

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

https://github.com/slowkow/harmonypy

Science Score: 33.0%

This score indicates how likely this project is to be science-related based on various indicators:

○
CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
○
.zenodo.json file
○
DOI references
✓
Academic publication links
Links to: zenodo.org
✓
Committers with academic emails
2 of 4 committers (50.0%) from academic institutions
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (11.3%) to scientific vocabulary

Keywords

bioinformatics data-integration data-science single-cell-analysis

Last synced: 6 months ago · JSON representation

Repository

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

Basic Info

Host: GitHub
Owner: slowkow
License: gpl-3.0
Language: Python
Default Branch: master
Homepage: https://portals.broadinstitute.org/harmony/
Size: 2.77 MB

Statistics

Stars: 224
Watchers: 5
Forks: 24
Open Issues: 3
Releases: 6

Topics

bioinformatics data-integration data-science single-cell-analysis

Created about 6 years ago · Last pushed over 1 year ago

Metadata Files

Readme Changelog Contributing License

README.md

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:

bash pip install harmonypy

Usage

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

```python

Load data

import pandas as pd

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

meta_data

cellid dataset nGene percentmito 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

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

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.runharmony(datamat, metadata, varsuse)

Write the adjusted PCs to a new file.

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

Owner

Name: Kamil Slowikowski
Login: slowkow
Kind: user
Company: Mass General Brigham

Website: https://slowkow.com
Twitter: slowkow
Repositories: 22
Profile: https://github.com/slowkow

Computational biologist. Using transcriptomics to learn about inflammation and cancer.

GitHub Events

Total

Issues event: 12
Watch event: 23
Issue comment event: 9
Pull request event: 1
Fork event: 5

Last Year

Issues event: 12
Watch event: 23
Issue comment event: 9
Pull request event: 1
Fork event: 5

Committers

Last synced: 9 months ago

All Time

Total Commits: 67
Total Committers: 4
Avg Commits per committer: 16.75
Development Distribution Score (DDS): 0.149

Past Year

Commits: 3
Committers: 2
Avg Commits per committer: 1.5
Development Distribution Score (DDS): 0.333

Top Committers

Name	Email	Commits
Kamil Slowikowski	k**i@g**m	57
John Arevalo	j**o@g**m	6
Bo Li	b**8@m**u	3
Jonathan Manning	j**g@e**k	1

Committer Domains (Top 20 + Academic)

ebi.ac.uk: 1 mgh.harvard.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 32
Total pull requests: 11
Average time to close issues: about 1 month
Average time to close pull requests: 21 days
Total issue authors: 30
Total pull request authors: 7
Average comments per issue: 2.84
Average comments per pull request: 1.91
Merged pull requests: 6
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 6
Pull requests: 1
Average time to close issues: 21 days
Average time to close pull requests: N/A
Issue authors: 5
Pull request authors: 1
Average comments per issue: 0.67
Average comments per pull request: 5.0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

Zhangruiqi111 (2)
liboxun (2)
shihsama (1)
deevdevil88 (1)
Arkkienkeli (1)
bholmes-datasight (1)
FFB-Lab (1)
LetiTode (1)
Sayyam-Shah (1)
dangeles (1)
aichander (1)
hurleyLi (1)
gmoore5 (1)
swemeshy (1)
HelloWorldLTY (1)

Pull Request Authors

johnarevalo (6)
rilango (2)
pinin4fjords (1)
bli25 (1)
tariqdaouda (1)
hurleyLi (1)
milescsmith (1)

Top Labels

Issue Labels

question (3) enhancement (2)

Pull Request Labels

Packages

Total packages: 1
Total downloads:
- pypi 13,679 last-month
Total docker downloads: 1,694

Total dependent packages: 20
Total dependent repositories: 16
Total versions: 9
Total maintainers: 1

pypi.org: harmonypy

A data integration algorithm.

Homepage: https://github.com/slowkow/harmonypy
Documentation: https://harmonypy.readthedocs.io/
License: GNU General Public License v3 or later (GPLv3+)
Latest release: 0.0.10
published over 1 year ago

Versions: 9
Dependent Packages: 20
Dependent Repositories: 16
Downloads: 13,679 Last month
Docker Downloads: 1,694

Rankings

Dependent packages count: 0.7%

Docker downloads count: 2.5%

Dependent repos count: 3.6%

Downloads: 4.1%

Average: 4.1%

Stargazers count: 5.7%

Forks count: 8.1%

Maintainers (1)

slowkow

Last synced: 6 months ago

Dependencies

setup.py pypi

numpy *
pandas *
scipy *

requirements.txt pypi

numpy >=1.13
pandas >=0.25
scikit-learn >=0.21
scipy >=1.2