Scalable integration of single cell RNAseq data for batch correction and meta analysis

Check out the latest preprint of Harmony on bioRxiv

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

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

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

The simplest way to run Harmony is 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")

The Harmony algorithm iteratively corrects PCA embeddings. To input your own low dimensional embeddings directly, set do_pca=FALSE.

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

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.

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

For details, check out these vignettes:

• Seurat V2
• Seurat V3

You can run Harmony with functions from the MUDAN package. For more, details, check out this vignette.

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.