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Single-cell, Spatial & Cytometry · Single-Cell Batch Correction

Harmony

By Broad Institute / Harvard Medical School (Raychaudhuri Lab) →

Fast, sensitive and flexible integration of single-cell data

Harmony is one of the most widely used algorithms for integrating single-cell datasets, iteratively removing technical batch differences while preserving subtle biological cell states.

Iterative maximum diversity clustering to align batch-specific distributionsExtremely fast execution scaling linearly with millions of cellsAvailable in both R and Python (harmonypy) packagesIntegrated natively into Seurat and Scanpy pipelines
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Overview

Harmony is one of the most widely used algorithms for integrating single-cell datasets, iteratively removing technical batch differences while preserving subtle biological cell states.

Information checked against an official source; not a hands-on test. Source · Last reviewed: 20/09/2026, 11:10:15

Key Features

  • Iterative maximum diversity clustering to align batch-specific distributions
  • Extremely fast execution scaling linearly with millions of cells
  • Available in both R and Python (harmonypy) packages
  • Integrated natively into Seurat and Scanpy pipelines

Academic Context & Research Evidence

Biological & Workflow Fit

Biological Application
Cross-tissue cell atlas integration, patient cohort harmonization, and multi-donor studies
Research Workflow
Uncorrected PCA matrix -> Harmony batch alignment -> corrected embedding for clustering and UMAP
Compute & Hardware
Standard CPU workstation
Licensing & Academic Use
GPL-3.0
Documented Evidence
View validation publication / source ↗

Cite this Tool

Use this citation format when referencing Harmony in scientific publications and benchmark papers.

@software{harmony_2026,
  title = {{Harmony}},
  author = {{Broad Institute / Harvard Medical School (Raychaudhuri Lab)}},
  year = {2026},
  url = {https://github.com/immunogenomics/harmony},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations streamed from Europe PMC and PubMed for Harmony.

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Technical / Product Information

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeSoftware Library (R / Python)
Access modeOpen Source
AI roleIterative Dimensionality Alignment
Input dataNot recorded
Output dataNot recorded
Licence conditionsGPL-3.0
Commercial eligibilityPermissive open source release
Compute requirementsStandard CPU workstation
ValidationNot recorded
TypeSingle-cell batch integration algorithm
Intended useNot recorded
CompatibilityNot recorded
ManufacturerBroad Institute of MIT and Harvard
Biological applicationCross-tissue cell atlas integration, patient cohort harmonization, and multi-donor studies
Research workflowUncorrected PCA matrix -> Harmony batch alignment -> corrected embedding for clustering and UMAP
Evidence levelPeer-reviewed research (Nature Methods 2019) with thousands of citations
Integration evidencehttps://github.com/immunogenomics/harmony
Laboratory handoffProduces clean cell clusters for cell-type sorting and downstream validation
AvailabilityAvailable on CRAN, PyPI, and GitHub
Price / accessFree Open Source

Research fit & compatibility

No software–hardware integration has been verified for this entry yet. Explore documented research workflows.

Gallery & resources

No licensed product photograph recorded for Harmony yet. Visit the official resource below for manufacturer imagery.

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FAQ

Where is this product available?

Available on CRAN, PyPI, and GitHub

How is pricing handled?

Prices reflect the source at its last check. Confirm current pricing and regional availability on the official site.

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Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating Harmony.

5.0
★★★★★Based on 0 researcher evaluations
Biological Accuracy
4.8/5
Ease of Installation
4.3/5
Documentation & Code
4.6/5