LTLA

# Reading in the annotation.
library(TxDb.Mmusculus.UCSC.mm10.ensGene)
intronic <- intronicParts(TxDb.Mmusculus.UCSC.mm10.ensGene, linked.to.single.gene.only=TRUE)
exonic <- exonicParts(TxDb.Mmusculus.UCSC.mm10.ensGene, linked.to.single.gene.only=TRUE)

IX <- data.frame(GeneID=intronic$gene_id, Chr=as.character(seqnames(intronic)),
                 Start=start(intronic), End=end(intronic), Strand=strand(intronic), stringsAsFactors=FALSE)
EX <- data.frame(GeneID=exonic$gene_id, Chr=as.character(seqnames(exonic)),
                 Start=start(exonic), End=end(exonic), Strand=strand(exonic), stringsAsFactors=FALSE)

LTLA

library(BiocParallel)
library(DelayedArray)

#' @importFrom stats start end
.grid_iterate <- function(x, grid = NULL) {
    b <- 0L
    function() {
        if (b == length(grid)) {
            return(NULL)
        }

LTLA

#include "beachtest.h"

// Don't forget to set appropriate PKG_CXXFLAGS.
#include <omp.h>

#include <iostream>

extern "C"{ 

SEXP parallel_reader(SEXP input) {

LTLA

library(edgeR)
g <- factor(rep(1:4, c(50, 20, 10, 5)))
N <- 20000
mus <- 100
# mus <- 100 * 2^rnorm(N*length(g)) # Uncomment for variable mu
y <- matrix(rnbinom(N*length(g), mu=mus, size=1), nrow=N, byrow=TRUE)

design <- model.matrix(~gl(2,2))

# Summation.

LTLA

library(scRNAseq)
hESCs <- LaMannoBrainData('human-es')

# Trivial function to get something working on two matrices.
FUN <- function(x, y) { list(colSums(x), colSums(y)) }

library(BiocParallel)
BPPARAM1 <- SerialParam()
BPPARAM5 <- MulticoreParam(5)

LTLA

############# The usual ################
library(scRNAseq)

# Example data ----
sce <- ReprocessedAllenData(assays="tophat_counts")
class(sce)

library(scater)
sce <- logNormCounts(sce, exprs_values="tophat_counts")

LTLA

library(tiledb)
dim <- c(500L, 200L)
context <- tiledb_ctx()

collected <- vector("list", length(dim))
for (i in seq_along(dim)) {
    collected[[i]] <- tiledb_dim(ctx=context, paste0("d", i), c(1L, dim[i]), tile=100L, type="INT32")
}
dom <- tiledb_domain(ctx=context, dims=collected)

LTLA

# The hue is evenly distributed along the circle;
# we oscillate the luminescence to provide a better
# distinction between similar colors.

n <- 30
i <- head(seq(0, 360, length.out=n+1), -1)
cols <- hcl(i, c=50, l=80 + 10 * sin(i/30 * pi))
plot(seq_along(cols), col=cols, pch=16, cex=5)

LTLA

mat <- matrix(rnorm(100000), ncol=10)

library(BiocNeighbors)
res <- findKNN(mat, k=20)
d <- res$distance[,ncol(res$distance)]

processed <- logical(nrow(mat))
candidates <- logical(nrow(mat))

while (!all(processed)) {

LTLA

function range(x) {
    let maxed = Number.NEGATIVE_INFINITY;
    let mined = Number.POSITIVE_INFINITY;
    for(const y of x) {
        if (y > maxed) {
            maxed = y;
        }
        if (y < mined) {
            mined = y;
        }