vd_Inputs: Specify inputs for variance decomposition analysis

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

Specify inputs for variance decomposition analysis

Description

Step 2 of variance decomposition analysis (see examples). Given Seurat object and vd_Formula output, input list for variance decomposition are generated.

Usage

vd_Inputs(
  object,
  vd_model.list,
  features = NULL,
  pct.min = 0,
  variable.features = F,
  subsample.factor = 1
)

Arguments

object	Seurat object.
vd_model.list	Output from vd_Formula.
pct.min	Minimal expressing fraction for genes to be included in analysis. Default is 0.
variable.features	Logical specifying whether to use variable features only. If true, looks for variable features within provided Seurat object.
subsample.factor	Numeric [0,1] specfying how to subsample (i.e., downsample) data. Default is 1 (no subsampling)
Features	Features to include in analysis. If specified, pct.min and variable features are ignored.

Value

list of inputs for vd_Run() function.

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.