## ----echo=FALSE, results="hide"----------------------------------------------- knitr::opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE) library(BiocStyle) ## ----------------------------------------------------------------------------- library(DelayedRandomArray) X <- RandomUnifArray(c(1e6, 1e6)) X ## ----------------------------------------------------------------------------- RandomNormArray(c(100, 50)) RandomPoisArray(c(100, 50), lambda=5) RandomGammaArray(c(100, 50), shape=2, rate=5) RandomWeibullArray(c(100, 50), shape=5) ## ----------------------------------------------------------------------------- RandomNormArray(c(100, 50), mean=1) ## ----------------------------------------------------------------------------- RandomNormArray(c(100, 50), mean=1:100) ## ----------------------------------------------------------------------------- means <- RandomNormArray(c(100, 50)) RandomPoisArray(c(100, 50), lambda=2^means) ## ----------------------------------------------------------------------------- ngenes <- 20000 log.abundances <- runif(ngenes, -2, 5) nclusters <- 20 # define 20 clusters and their population means. cluster.means <- matrix(2^rnorm(ngenes*nclusters, log.abundances, sd=2), ncol=nclusters) ncells <- 1e6 clusters <- sample(nclusters, ncells, replace=TRUE) # randomly allocate cells cell.means <- DelayedArray(cluster.means)[,clusters] dispersions <- 0.05 + 10/cell.means # typical mean variance trend. y <- RandomNbinomArray(c(ngenes, ncells), mu=cell.means, size=1/dispersions) y ## ----------------------------------------------------------------------------- # Row-wise chunks: RandomUnifArray(c(1000, 500), chunkdim=c(1, 500)) # Column-wise chunks: RandomUnifArray(c(1000, 500), chunkdim=c(1000, 1)) ## ----------------------------------------------------------------------------- set.seed(199) RandomUnifArray(c(10, 5), chunkdim=c(1, 5)) set.seed(199) RandomUnifArray(c(10, 5), chunkdim=c(10, 1)) ## ----------------------------------------------------------------------------- set.seed(999) RandomNormArray(c(10, 5)) set.seed(999) RandomNormArray(c(10, 5)) ## ----------------------------------------------------------------------------- RandomPoisArray(c(1e6, 1e6), lambda=0.5) # dense by default RandomPoisArray(c(1e6, 1e6), lambda=0.5, sparse=TRUE) # treat as sparse ## ----------------------------------------------------------------------------- sessionInfo()