Nothing
## ----eval_saver, include = FALSE-----------------------------------------
# Whether or not to evaluate saver. The generated vignette was run setting it
# to be TRUE but since running requires multiple cores, this was set to be
# FALSE for purposes of submission to CRAN.
eval.saver <- FALSE
## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = eval.saver)
## ---- eval = FALSE-------------------------------------------------------
# # install.packages("devtools")
# devtools::install_github("mohuangx/SAVER")
## ---- eval = FALSE-------------------------------------------------------
# # install.packages("devtools")
# devtools::install_github("mohuangx/SAVER@*release")
## ---- eval = TRUE--------------------------------------------------------
library(SAVER)
packageVersion("SAVER")
## ------------------------------------------------------------------------
# data.path <- "../data/expression_mRNA_17-Aug-2014.txt"
#
# # Need to remove first 10 rows of metadata
# raw.data <- read.table(data.path, header = FALSE, skip = 11, row.names = 1,
# check.names = FALSE)
# cortex <- as.matrix(raw.data[, -1])
#
# cellnames <- read.table(data.path, skip = 7, nrows = 1, row.names = 1,
# stringsAsFactors = FALSE)
# colnames(cortex) <- cellnames[-1]
#
# dim(cortex)
## ------------------------------------------------------------------------
# cortex.saver <- saver(cortex, ncores = 12)
# str(cortex.saver)
## ---- eval=FALSE---------------------------------------------------------
# cortex.saver <- saver(cortex, ncores = 12, estimates.only = TRUE)
## ---- eval=FALSE---------------------------------------------------------
# # Identify the indices of the genes of interest
# genes <- c("Thy1", "Mbp", "Stim2", "Psmc6", "Rps19")
# genes.ind <- which(rownames(cortex) %in% genes)
#
# # Generate predictions for those genes and return entire dataset
# cortex.saver.genes <- saver(cortex, pred.genes = genes.ind,
# estimates.only = TRUE)
#
# # Generate predictions for those genes and return only those genes
# cortex.saver.genes.only <- saver(cortex, pred.genes = genes.ind,
# pred.genes.only = TRUE, estimates.only = TRUE)
## ---- eval=FALSE---------------------------------------------------------
# saver1 <- saver(x, pred.genes = 1:2500, pred.genes.only = TRUE,
# do.fast = FALSE)
# saver2 <- saver(x, pred.genes = 2501:5000, pred.genes.only = TRUE,
# do.fast = FALSE)
# saver3 <- saver(x, pred.genes = 5001:7500, pred.genes.only = TRUE,
# do.fast = FALSE)
# saver4 <- saver(x, pred.genes = 7501:10000, pred.genes.only = TRUE,
# do.fast = FALSE)
#
# saver.all <- combine.saver(list(saver1, saver2, saver3, saver4))
## ---- eval=FALSE---------------------------------------------------------
# samp1 <- sample.saver(saver1, rep = 1, seed = 50)
# samp5 <- sample.saver(saver1, rep = 5, seed = 50)
## ---- eval=FALSE---------------------------------------------------------
# saver1.cor.gene <- cor.genes(saver1)
# saver1.cor.cell <- cor.cells(saver1)
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