## ----echo=FALSE--------------------------------------------------------------------------------------------- options(width=110) ## ----eval=FALSE--------------------------------------------------------------------------------------------- # if (!requireNamespace("BiocManager", quietly=TRUE)) # install.packages("BiocManager") # BiocManager::install("SCArray") ## ----------------------------------------------------------------------------------------------------------- suppressPackageStartupMessages(library(SCArray)) suppressPackageStartupMessages(library(SingleCellExperiment)) # load a SingleCellExperiment object fn <- system.file("extdata", "example.rds", package="SCArray") sce <- readRDS(fn) # convert to a GDS file scConvGDS(sce, "test.gds") # list data structure in the GDS file (f <- scOpen("test.gds")) scClose(f) ## ----------------------------------------------------------------------------------------------------------- library(Matrix) cnt <- matrix(0, nrow=4, ncol=8) set.seed(100); cnt[sample.int(length(cnt), 8)] <- rpois(8, 4) (cnt <- as(cnt, "sparseMatrix")) # convert to a GDS file scConvGDS(cnt, "test.gds") ## ----------------------------------------------------------------------------------------------------------- # a GDS file in the SCArray package (fn <- system.file("extdata", "example.gds", package="SCArray")) # load a SingleCellExperiment object from the file sce <- scExperiment(fn) sce # it is a DelayedMatrix (the whole matrix is not loaded) assays(sce)$counts # column data colData(sce) # row data rowData(sce) ## ----------------------------------------------------------------------------------------------------------- cnt <- assays(sce)$counts logcnt <- log2(cnt + 1) logcnt ## ----------------------------------------------------------------------------------------------------------- suppressPackageStartupMessages(library(scuttle)) sce <- logNormCounts(sce) logcounts(sce) ## ----------------------------------------------------------------------------------------------------------- col_mean <- colMeans(logcnt) str(col_mean) row_mean <- rowMeans(logcnt) str(row_mean) # calculate the mean and variance at the same time mvar <- scRowMeanVar(logcnt) head(mvar) ## ----------------------------------------------------------------------------------------------------------- suppressPackageStartupMessages(library(scater)) # run umap analysis sce <- runPCA(sce) ## ----------------------------------------------------------------------------------------------------------- sce <- scRunPCA(sce) ## ----fig.align="center",fig.width=4,fig.height=3------------------------------------------------------------ plotReducedDim(sce, dimred="PCA") ## ----------------------------------------------------------------------------------------------------------- suppressPackageStartupMessages(library(scater)) # run umap analysis sce <- runUMAP(sce) ## ----fig.align="center",fig.width=4,fig.height=3------------------------------------------------------------ plotReducedDim(sce, dimred="UMAP") ## ----------------------------------------------------------------------------------------------------------- options(SCArray.verbose=TRUE) m <- rowMeans(logcnt) str(m) ## ----------------------------------------------------------------------------------------------------------- # print version information about R, the OS and attached or loaded packages sessionInfo() ## ----echo=FALSE--------------------------------------------------------------------------------------------- unlink("test.gds", force=TRUE)