harmony (version 1.2.1)

RunHarmony.Seurat: Applies harmony on a Seurat object cell embedding.

Description

Applies harmony on a Seurat object cell embedding.

Usage

# S3 method for Seurat
RunHarmony(
  object,
  group.by.vars,
  reduction.use = "pca",
  dims.use = NULL,
  reduction.save = "harmony",
  project.dim = TRUE,
  ...
)

Value

Seurat object. Harmony dimensions placed into a new slot in the Seurat object according to the reduction.save. For downstream Seurat analyses, use reduction='harmony'.

Arguments

object

the Seurat object. It needs to have the appropriate slot of cell embeddings precomputed.

group.by.vars

the name(s) of covariates that harmony will remove its effect on the data.

reduction.use

Name of dimension reduction to use. Default is pca.

dims.use

indices of the cell embedding features to be used

reduction.save

the name of the new slot that is going to be created by harmony. By default, harmony.

project.dim

Project dimension reduction loadings. Default TRUE.

...

Arguments passed on to RunHarmony.default

theta

Diversity clustering penalty parameter. Specify for each variable in vars_use Default theta=2. theta=0 does not encourage any diversity. Larger values of theta result in more diverse clusters.

sigma

Width of soft kmeans clusters. Default sigma=0.1. Sigma scales the distance from a cell to cluster centroids. Larger values of sigma result in cells assigned to more clusters. Smaller values of sigma make soft kmeans cluster approach hard clustering.

lambda

Ridge regression penalty. Default lambda=1. Bigger values protect against over correction. If several covariates are specified, then lambda can also be a vector which needs to be equal length with the number of variables to be corrected. In this scenario, each covariate level group will be assigned the scalars specified by the user. If set to NULL, harmony will start lambda estimation mode to determine lambdas automatically and try to minimize overcorrection (Use with caution still in beta testing).

nclust

Number of clusters in model. nclust=1 equivalent to simple linear regression.

max_iter

Maximum number of rounds to run Harmony. One round of Harmony involves one clustering and one correction step.

early_stop

Enable early stopping for harmony. The harmonization process will stop when the change of objective function between corrections drops below 1e-4

ncores

Number of processors to be used for math operations when optimized BLAS is available. If BLAS is not supporting multithreaded then this option has no effect. By default, ncore=1 which runs as a single-threaded process. Although Harmony supports multiple cores, it is not optimized for multithreading. Increase this number for large datasets iff single-core performance is not adequate.

plot_convergence

Whether to print the convergence plot of the clustering objective function. TRUE to plot, FALSE to suppress. This can be useful for debugging.

verbose

Whether to print progress messages. TRUE to print, FALSE to suppress.

.options

Setting advanced parameters of RunHarmony. This must be the result from a call to `harmony_options`. See ?`harmony_options` for parameters not listed above and more details.

See Also

Other RunHarmony: RunHarmony.SingleCellExperiment(), RunHarmony.default(), RunHarmony()

Examples

if (FALSE) {
## seu is a Seurat single-Cell R object
seu <- RunHarmony(seu, "donor_id")
}