buildSeed | R Documentation |
Use ranger to select features and build a genesInSeed gene matrix
buildSeed( trainSet, genesInSeed = 200, groupSize = 30, randomize = TRUE, num.trees = 1000, plotIt = TRUE, trainSet.3sam = NULL, trainSet.30sam = NULL, proportional = FALSE )
trainSet |
Each row is a gene, and each column is an example of a particular cell type, ie from single cell data |
genesInSeed |
The maximum number of genes in the returned seed matrix (DEFAULT: 200) |
groupSize |
The number of groups to break the trainSet into by ADAPTS::scSample (DEFAULT: 30) |
randomize |
Set to TRUE randomize the sets selected by ADAPTS::scSample (DEFAULT: TRUE) |
num.trees |
The number of trees to be used by ranger (DEFAULT: 1000) |
plotIt |
Set to TRUE to plot (DEFAULT: TRUE) |
trainSet.3sam |
Optional pre-calculated ADAPTS::scSample(trainSet, groupSize = 3) (DEFAULT: NULL) |
trainSet.30sam |
Optional pre-calculated ADAPTS::scSample(trainSet, groupSize=groupSize, randomize=randomize) (DEFAULT: NULL) |
proportional |
Set to true to make the training set cell type proportional. Ignores group size (DEFAULT: FALSE) |
A list with condition numbers and gene lists
library(ADAPTS) ct1 <- runif(1000, 0, 100) ct2 <- runif(1000, 0, 100) dataMat <- cbind(ct1, ct1, ct1, ct1, ct1, ct1, ct2, ct2, ct2, ct2) 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 newSigMat <- buildSeed(trainSet=dataMat)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.