Fast, sensitive and accurate integration of single-cell data with Harmony
Check out the manuscript in Nature Methods:
For Python users, check out the harmonypy package by Kamil Slowikowski.
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.
To run Harmony, open R and install harmony from CRAN:
If you'd like the latest development version, install from this github directly:
We made it easy to run Harmony in most common R analysis pipelines.
Check out this vignette for a quick start tutorial.
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
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.
library(harmony) my_harmony_embeddings <- HarmonyMatrix(normalized_counts, meta_data, "dataset")
You can run Harmony within your Seurat workflow. You'll only need to make two changes to your code.
- Run Harmony with the
- In downstream analyses, use the Harmony embeddings instead of PCA.
For example, run Harmony and then UMAP in two lines.
seuratObj <- RunHarmony(seuratObj, "dataset") seuratObj <- RunUMAP(seuratObj, reduction = "harmony")
For details, check out these vignettes:
Harmony with two or more covariates
Harmony can integrate over multiple covariates. To do this, specify a vector covariates to integrate.
my_harmony_embeddings <- HarmonyMatrix( my_pca_embeddings, meta_data, c("dataset", "donor", "batch_id"), do_pca = FALSE )
Do the same with your Seurat object:
seuratObject <- RunHarmony(seuratObject, c("dataset", "donor", "batch_id"))
The examples above all return integrated PCA embeddings. We created a more advanced tutorial 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.