README.md · sanyecao2314/harmony

Harmony <img src="man/figures/logo.png" width="181px" align="right" />
===========

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*Fast, sensitive and accurate integration of single-cell data with Harmony*

Check out the manuscript in Nature Methods: 
- [nature website](https://www.nature.com/articles/s41592-019-0619-0)
- [read link](https://www.nature.com/articles/s41592-019-0619-0.epdf?shared_access_token=rDg_Rd07lrFXExt_ySj7V9RgN0jAjWel9jnR3ZoTv0NfDJkKCfDV_X9Mq3lweQmKiXEXxhrebQRjJEZdc-xNv6-7ZN1XotlD_mo5TSS4Z4eWn-kUo6mBwA5dEAKlTfR8OT6E10MZY_E-906ajbzvgg%3D%3D)

For Python users, check out the [harmonypy package](https://github.com/slowkow/harmonypy) by Kamil Slowikowski.

# System requirements

Harmony has been tested on R versions >= 3.4. Please consult the DESCRIPTION file for more details on required R packages. Harmony has been tested on Linux, OS X, and Windows platforms.

# Installation

To run Harmony, open R and install directly from github using the following commands:

```r
library(devtools)
install_github("immunogenomics/harmony")
```

Installation may include compiling C++ code from source, so it can take a few minutes.

# Usage/Demos

We made it easy to run Harmony in most common R analysis pipelines.

## Quick Start

Check out this [vignette](https://github.com/immunogenomics/harmony/blob/master/vignettes/quickstart.Rmd) for a quick start tutorial.

## PCA matrix

The Harmony algorithm iteratively corrects PCA embeddings. To input your own low dimensional embeddings directly, set `do_pca=FALSE`. Harmony is packaged with a small dataset

```r
library(harmony)
my_harmony_embeddings <- HarmonyMatrix(my_pca_embeddings, meta_data, "dataset", do_pca=FALSE)
```

## Normalized gene matrix

You can also run Harmony on a sparse matrix of library size normalized expression counts. Harmony will scale these counts, run PCA, and finally perform integration.

```r
library(harmony)
my_harmony_embeddings <- HarmonyMatrix(normalized_counts, meta_data, "dataset")
```

## Seurat

You can run Harmony within your Seurat workflow. You'll only need to make two changes to your code.

1) Run Harmony with the `RunHarmony()` function
2) In downstream analyses, use the Harmony embeddings instead of PCA.

For example, run Harmony and then UMAP in two lines.

```r
seuratObj <- RunHarmony(seuratObj, "dataset")
seuratObj <- RunUMAP(seuratObj, reduction = "harmony")
```

For details, check out these vignettes:

- [Seurat V2](http://htmlpreview.github.io/?https://github.com/immunogenomics/harmony/blob/master/docs/SeuratV2.html)
- [Seurat V3](http://htmlpreview.github.io/?https://github.com/immunogenomics/harmony/blob/master/docs/SeuratV3.html)

## MUDAN

You can run Harmony with functions from the [MUDAN package](https://github.com/jefworks/mudan). For more, details, check out this [vignette](http://htmlpreview.github.io/?https://github.com/immunogenomics/harmony/blob/master/docs/mudan.html).

## Harmony with two or more covariates

Harmony can integrate over multiple covariates. To do this, specify a vector covariates to integrate.

```r
my_harmony_embeddings <- HarmonyMatrix(
  my_pca_embeddings, meta_data, c("dataset", "donor", "batch_id"),
  do_pca = FALSE
)
```

Do the same with your Seurat object:

```r
seuratObject <- RunHarmony(seuratObject, c("dataset", "donor", "batch_id"))
```

## Advanced

The examples above all return integrated PCA embeddings. We created a more [advanced tutorial](http://htmlpreview.github.io/?https://github.com/immunogenomics/harmony/blob/master/docs/advanced.html) that explores the internal data structures used in the Harmony algorithm.

# Reproducing results from manuscript

Code to reproduce Harmony results from the Korsunsky et al 2019 manuscript will be made available on github.com/immunogenomics/harmony2019.

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