BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads)

Usage

BigSur(
  seurat.obj,
  assay = "RNA",
  counts.slot = "counts",
  cv.est.method = "MeanSpecific",
  null.distribution = "NB",
  variable.features = T,
  correlations = F,
  first.pass.cutoff = 2,
  inverse.fano.moments = T,
  fano.alpha = 0.05,
  min.fano = 1.5,
  cor.alpha = 0.05,
  block.size = 500,
  log.file = F,
  log.file.dir = paste0(getwd(), "/BigSurRun", Sys.Date(), ".txt")
)

Arguments

seurat.obj: Seurat object containing the raw transcript counts filtered for zero count genes.
assay: Assay slot containing raw transcript counts (default "RNA").
counts.slot: Slot within assay containing raw counts matrix (default "counts").
cv.est.method: String. Sets the method used for determining the coefficient of variation used to define null distributions. There are three options here: 1) "Single": Determines a scalar value of c. 2) "MeanSpecific": Estimates the relationship between mean expression and the expected coefficient of variation and predicts a null value for each gene. 3)"TwoComponent": Estimates a the relationship between mean expression and the expected coefficient of variation using a two parameter fit.
null.distribution: String. Sets the null distribution from which to estimate p-values for Fano factors and correlations. "NB": Negative binomial, "PLN": Poisson log-normal. (Defaults to negative binomial.)
variable.features: Boolean. If true, BigSur will identify select variable features based on the modified corrected Fano factor.
correlations: Boolean. If true, BigSur will identify statistically significant gene-gene correlations.
first.pass.cutoff: Integer. Removes roots before p-value calculations if the root is below Abs[Sqrt(2)*InverseErfc(2*10^-first.pass.cutoff)]. The higher the number, the more correlations are removed in initial screening.
inverse.fano.moments: Boolean. If true, BigSur will calculate the moments for the inverse Fano factor pairs before performing Cornish Fisher expansion.
fano.alpha: Double. Desired false discovery cutoff for labeling of variable features. (Default 0.05).
min.fano: Double. Minimum mcFano value considered for variable genes.
cor.alpha: Double. Desired false discovery cutoff for labeling of statistically significant correlations.
block.size: Integer. Determines the block size for correlation cumulant calculation to relieve memory pressure at the cost of some speed. Number indicates the number of gene pairs evaluated at a time. (Default 500)
log.file: Boolean. If true, a log file will be created.
log.file.dir: String. Path of desired location for log file.

Value

If both variable features and correlations are identified, a list containing the updated Seurat object and the statistically significant correlations is returned. If only one process is selected, their respective output is returned alone.

Examples

if (FALSE) { # \dontrun{
out <- BigSur(example.seurat, variable.features = TRUE, correlations = TRUE)
} # }