Calculate FDR for SingleCellExperiment object

Calculate FDR for SingleCellExperiment object

calculate_FDR_scEpigenomics(
  scExp,
  differential_function = differential_ChromSCape,
  by = "IDcluster",
  limit = 5,
  qval.th = 0.01,
  logFC.th = log2(2),
  min.pct = 0.01,
  min_frac_cell_assigned = 0.1,
  cluster_of_origin = "Omega",
  nThreads = 10,
  iterations = 10,
  ...
)

Arguments

scExp	A SingleCellExperiment object
differential_function	A function that take in entry a SingleCellExperiment object and parameters passed in ... and returns a data.frame containing the significantly differential features for each cluster.
by	A character specifying the name of the metadata column referencing the clusters.
limit	An integer specifying the minimum number of significantly enriched / depleted features required in order for a subcluster to be called a 'true' subcluster
qval.th	A numeric specifying the adjusted p-value below which a feature is considered as significantly differential.
logFC.th	A numeric specifying the log Fold-Change of activation (% cells activated) threshold above which a feature is defined as significantly differential.
min.pct	Minimum percentage of cells to be active in the cells of the cluster to consider a feature as potentially significantly differential.
min_frac_cell_assigned	A numeric between 0 and 1 specifying the minimum percentage of the total cells in the SingleCellExperiment object that needs to be assigned. If a lower proportion is assigned, all cells are
cluster_of_origin	Name of the parent cluster
nThreads	If runFDR==TRUE. An integer specifying of threads to use for the calculation of the FDR.
iterations	An integer specifyung the number of iterations of random subsampling to run.
...	Additional parameters passed to the differential_function. See differential_ChromSCape() for more information on additional parameters for the default function.

Value

Examples

if(requireNamespace("ChromSCape", quietly=TRUE)){
data("scExp", package = "IDclust")
scExp = ChromSCape::find_clusters_louvain_scExp(scExp, resolution = 0.1)
FDR_df = calculate_FDR_scEpigenomics(scExp, nThreads = 1, iterations = 2)
head(FDR_df)
}