get_gene_correlation_scores: get_gene_correlation_scores

In MarioniLab/geneBasisR: What the Package Does (One Line, Title Case)

get_gene_correlation_scores

Description

Calculates per gene correlation between measured expression levels and estimated expression levels (average across neighbors). It is an intermediate step for 'get_gene_prediction_scores' function. It can be handy to calculate this part separately to be recycled multiple times to compare different seelctions.

Usage

get_gene_correlation_scores(
  sce,
  genes,
  batch = NULL,
  n.neigh = 5,
  nPC = NULL,
  genes.predict = rownames(sce),
  method = "spearman",
  ...
)

Arguments

sce	SingleCellExperiment object containing gene counts matrix (stored in 'logcounts' assay).
genes	Character vector containing names of selected genes.
batch	Name of the field in colData(sce) to specify batch. Default batch=NULL if no batch is applied.
n.neigh	Positive integer > 1, specifying number of neighbors to use for kNN-graph. Default n.neigh=5.
nPC	Scalar (or NULL) specifying number of PCs to use for construction of kNN-graph. Default nPC=NULL. We advise to set it to 50 if length(genes.selection) > 50.
genes.predict	Character vector containing names of genes for which we want to calculate gene prediction score. Default = rownames(sce).
method	Character specifying method for correlation. Availbale options are c("spearman", "pearson", "kendall"). Default method="spearman".
...	Additional arguments.

Examples

require(SingleCellExperiment)
n_row = 1000
n_col = 100
sce = SingleCellExperiment(assays = list(logcounts = matrix(rnorm(n_row*n_col), ncol=n_col)))
rownames(sce) = as.factor(1:n_row)
colnames(sce) = c(1:n_col)
sce$cell = colnames(sce)
genes = rownames(sce)
out = get_gene_correlation_scores(sce, genes)

MarioniLab/geneBasisR documentation built on June 30, 2023, 2:04 p.m.