testAllSigMatrices | R Documentation |
This wrapper is helpful for repetitively matrix generation. It generates seed matrix, all-gene matrix, augmented matrix, shrunk matrix, and all the clustered matrices in one call.
testAllSigMatrices( exprData, randomize = TRUE, skipShrink = FALSE, proportional = FALSE, handMetaCluster = NULL, testOnHalf = TRUE, condTol = 1.01, numChunks = 100, plotIt = TRUE, fastStop = TRUE, singleCore = TRUE )
exprData |
The gene express data. Each row is a gene, and each column is an example of a particular cell type. |
randomize |
Set to to TRUE randomize the sets selected by ADAPTS::scSample (DEFAULT: TRUE) |
skipShrink |
Set to TRUE to skip shrinking the signatrure matrix (DEFAULT: TRUE) |
proportional |
Set to true to make the training set cell type proportional. Ignores group size (DEFAULT: FALSE) |
handMetaCluster |
A List of pre-defined meta clusters.Set to NULL to automatically group indistinguishable cells into same cluster using clustWspillOver.(DEFAULT: NULL) |
testOnHalf |
Set to TRUE to leave half the data as a test set |
condTol |
The tolerance in the reconstruction algorithm. 1.0 = no tolerance, 1.05 = 5% tolerance (DEFAULT: 1.01) |
numChunks |
The number of groups of genes to remove while shrinking (DEFAULT: NULL, i.e. 1) |
plotIt |
Set to FALSE to suppress plots (DEFAULT: TRUE) |
fastStop |
Halt early when the condition number changes by less than 1 for 3 iterations (DEFAULT: TRUE) |
singleCore |
TRUE for a single core (DEFAULT: TRUE) |
A list of results including prediction accuracy and cell enrichment
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 metaList <- list() colnames(dataMat) <- sub('\\..*','', colnames(dataMat)) metaList[[1]] <- c(unique(colnames(dataMat))[1]) #Cell Type 1 metaList[[2]] <- c(unique(colnames(dataMat))[2]) #Cell Type 2 metaList[[3]] <- c(unique(colnames(dataMat))[3]) #Cell Type 3 metaList[[4]] <- c(unique(colnames(dataMat))[4:length(unique(colnames(dataMat)))]) #Cell Type 4 #options(mc.cores=2) # This is a meta-function that calls other functions, # The execution speed is too slow for the CRAN automated check #testAllSigMatrices(exprData=dataMat, randomize = TRUE, skipShrink=FALSE, # proportional=FALSE, handMetaCluster=metaList, testOnHalf=TRUE, numChunks=NULL)
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