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Gene-gene correlation across single cells

Source: R/correlation.R
sc_correlation.Rd

Correlates a gene of interest against all (sufficiently expressed) genes in a normalised expression matrix, returning correlation, p-value and FDR.

Usage

sc_correlation(
  data.slot,
  goi = "NKG7",
  remove.quantile = 0.3,
  output.tables = "results/tables/Correlation_analysis/",
  debug.mode = FALSE,
  method = c("pearson", "spearman", "kendall")
)

Arguments

data.slot

Numeric expression matrix (genes x cells), e.g. a downsampled Seurat data layer as a matrix.

goi

Gene of interest (a rowname of data.slot).

remove.quantile

Drop genes whose mean expression is in the lowest fraction before testing.

output.tables

Directory to write the results CSV into (created if needed). Use NULL to skip writing.

debug.mode

Deprecated and ignored.

method

Correlation method: "pearson" (default), "spearman" or "kendall".

Value

A data frame with GeneID, Cor.val, P.val and FDR, sorted by decreasing correlation. Genes with undefined correlation (zero variance) are dropped.

Examples

m <- matrix(rnorm(50), nrow = 5,
            dimnames = list(paste0("g", 1:5), paste0("c", 1:10)))
sc_correlation(m, goi = "g1", output.tables = NULL)
#>   GeneID    Cor.val     P.val       FDR
#> 1     g1  1.0000000 0.0000000 0.0000000
#> 2     g4  0.3895636 0.2658045 0.2658045
#> 3     g2 -0.4046410 0.2460947 0.2658045