View source: R/VarID_functions.R
| compTBNoise | R Documentation | 
This function fits negative binomial models to transcript counts of pruned k-nearest neighbourhoods inferred by pruneKnn thereby deconvoluting variability into sampling noise, global cell-to-cell variability of transcript counts, and residual variability, which corresponds to biological noise.
compTBNoise(
  res,
  expData,
  pvalue = 0.01,
  genes = NULL,
  minN = 5,
  no_cores = NULL,
  gamma = 0.5,
  x0 = 0,
  lower = 0,
  upper = 100
)
| res | List object with k nearest neighbour information returned by  | 
| expData | Matrix of gene expression values with genes as rows and cells as columns. These values have to correspond to unique molecular identifier counts. | 
| pvalue | Positive real number between 0 and 1. All nearest neighbours with link probability  | 
| genes | Vector of gene names corresponding to a subset of rownames of  | 
| minN | Positive integer number. Noise inference is only done for k-nearest neighbourhoods with at least  | 
| no_cores | Positive integer number. Number of cores for multithreading. If set to  | 
| gamma | Positive real number. Scale paramter of the cauchy prior. Default is 0.5. | 
| x0 | Real number greater or equal to zero. Location parameter of the cauchy prior. | 
| lower | Real number greater or equal to zero. Lower bound for the maximum a posterior inference of the biological noise. Default is 0. | 
| upper | Real number greater or equal to zero. Upper bound for the maximum a posterior inference of the biological noise. Default is 100. | 
List object of three components:
| mu | Vector of mean expression for all k-nearest neighbourhoods. Componenets are set to  | 
| rt | Vector of dispersion parameters capturing global cell-to-cell variability of transcript counts for all k-nearest neighbourhoods. Componenets are set to  | 
| epsilon | Matrix of biological noise estimates for all genes across for all k-nearest neighbourhoods. Componenets are set to  | 
| pars | List of parameters. | 
## 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)
## End(Not run)
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