vd_UMAP: Generate UMAPs with each variance decomposition covariate...

In NMikolajewicz/scMiko: scRNAseq analysis functions. Developed using Seurat framework.

Generate UMAPs with each variance decomposition covariate overlaid.

Description

Generate UMAPs with each variance decomposition covariate overlaid.

Usage

vd_UMAP(object, vd_model.list)

Arguments

object	Seurat Object
vd_model.list	Output from vd_Formula().

Value

List ggplot handles, one for each model covariate.

Author(s)

Nicholas Mikolajewicz

See Also

vd_Run

Examples

parameter.list <- list(
 covariates = c( "cluster", "percent.mt", "batch", "cycle", "seq.coverage"),
 interactions = c("batch:cluster")
)

# step 1: model formulation
vd_model.list <- vd_Formula(object = so.query,
                           covariates = parameter.list$covariates,
                           interactions = parameter.list$interactions)

# step 2: prep model inputs
vd_inputs.list <- vd_Inputs(object = so.query, vd_model.list = vd_model.list, features = NULL,
                           pct.min =  0.9, variable.features = F, subsample.factor = 1)

# step 3: run variance decomposition
vd_results.list <- vd_Run(vd_inputs.list, n.workers = 20)

# step 4 (optional): visualize UMAP distribution of covariates
plt.umap.list <- vd_UMAP(object = so.query, vd_model.list = vd_model.list)

# step 5 (optional): visualize decomposition
res.var2 <- vd_results.list$varPart.format1
plt.var <- plotVarPart( res.var2 ) +
 theme_miko() +
 labs(title = "Variance Decomposition", subtitle = "Linear Mixed-Effects Model")  +
 theme(axis.text.x = element_text(angle = 45, hjust = 1))

NMikolajewicz/scMiko documentation built on June 28, 2023, 1:41 p.m.