diffNoisyGenesTB function

Function for extracting genes with differential biological variability in a cluster

This function infers genes with differential biological variability in a cluster versus a background set of clusters on the basis of a Wilcoxon rank sum-test between cells in a cluster and in the background set.

diffNoisyGenesTB(
  noise,
  cl,
  set,
  bgr = NULL,
  no_cores = 1,
  minobs = 5,
  ps = 0.1,
  rseed = 17000
)

Arguments

• noise: List object with noise parameters returned by the compTBNoise function.
• cl: List object with clustering information, returned by the graphCluster function.
• set: Postive integer number or vector of integers corresponding to valid cluster numbers. The function reports genes with differential variability in all clusters contained in set versus vlusters in bgr.
• bgr: Postive integer number or vector of integers corresponding to valid cluster numbers. Background set for comparison. The function reports genes with differential variability in all clusters contained in set compared to clusters in bgr. Default is NULL and bgr equals the set of all clusters not in bgr.
• no_cores: Positive integer number. Number of cores for multithreading. If set to NULL then the number of available cores minus two is used. Default is NULL.
• minobs: Positive integer number. Only genes with at least minobs neighbourhoods with non-zero biological noise levels in set are included for the p-value computation. Otherwise, a p-value or 0.5 is reported. Default is 5.
• ps: Real number greater or equal to zero. A small random variable sampled from a uniform distribution in the interval [0,ps] is added to the noise quantification to avoid inclusion of genes with small noise differences. Default is 0.1.
• rseed: Integer number. Random seed to enforce reproducible results. Default is 17000.

Returns

Data.frame with five columns: - mu.set: Mean expression across clusters in set.

• mu.bgr: Mean expression across clusters in bgr (or all clusters not in set).

• mu.all: Mean expression across clusters in set and bgr (or all clusters).

• eps.set: Average variability across clusters in set.

• eps.bgr: Average variability across clusters in bgr (or all clusters not in set).

• eps.all: Average variability across clusters in set and bgr (or all clusters).

• log2FC: log2 fold change of variability between between clusters in set and clusters in bgr (or all clusters).

• pvalue: Banjamini-Hochberg corrected Wilcoxon rank sum test p-value for differential variability.

Rows are ordered by decreasing log2 fold change of variability.

Examples

## Not run:

res <- pruneKnn(intestinalDataSmall,knn=10,alpha=1,no_cores=1,FSelect=FALSE)
noise <- compTBNoise(res,intestinalDataSmall,pvalue=0.01,genes = NULL,no_cores=1)
cl <- graphCluster(res,pvalue=0.01)
ngenes <- diffNoisyGenesTB(noise,cl,c(1,2),no_cores=1)
## End(Not run)