loopTillConvergence {ADAPTS}

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)