loopTillConvergence: Loop testAllSigMatrices until convergence

In ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific Cells

Loop testAllSigMatrices until convergence

Description

Iteratively call testAllSigMatrices numLoops times with the option to fast stop if correlation, correlation spear, mae and rmse all converge

Usage

loopTillConvergence(
  numLoops,
  fastStop,
  exprData,
  changePer,
  handMetaCluster,
  testOnHalf,
  condTol = 1.01
)

Arguments

numLoops	The number of iterations. Set to null to loop until results converge.
fastStop	Set to TRUE to break the loop when correlation, correlation spear, mae and rmse all converge
exprData	The single cell matrix
changePer	The maximum percentage of change allowed for convergence
handMetaCluster	A List of pre-defined meta clusters. Set to NULL to automatically group indistinguishable cells into same cluster use clustWspillOver (DEFAULT: NULL)
testOnHalf	Set to TRUE to leave half the data as a test set to validate all the matrices
condTol	The tolerance in the reconstruction algorithm. 1.0 = no tolerance, 1.05 = 5% tolerance (DEFAULT: 1.01)

Value

A list of results generated from all the iterative calls of testAllSigMatrices

Examples

ct1 <- runif(1000, 0, 100)
ct2 <- runif(1000, 0, 100)
ct3 <- runif(1000, 0, 100)
ct4 <- runif(1000, 0, 100)
dataMat <- cbind(ct1, ct1, ct1, ct1, ct1, ct1, ct2, ct2, ct2, ct2, ct3, ct3, ct3,ct3,ct4,ct4)
rownames(dataMat) <- make.names(rep('gene', nrow(dataMat)), unique=TRUE)
noise <- matrix(runif(nrow(dataMat)*ncol(dataMat), -2, 2), nrow = nrow(dataMat), byrow = TRUE)
dataMat <- dataMat + noise
#options(mc.cores=2)
#  This is a meta-function that calls other functions, 
#  The execution speed is too slow for the CRAN automated check
#loopTillConvergence(numLoops=10, fastStop=TRUE, exprData=dataMat, 
#    changePer=10,handMetaCluster=NULL, testOnHalf=TRUE)

ADAPTS documentation built on Sept. 14, 2022, 9:05 a.m.