Nothing
## ----setup, echo = FALSE-------------------------------------------------
knitr::opts_chunk$set(
fig.width = 12,
fig.height = 12 / 1.618,
out.width = '685px',
dpi = 144,
collapse = TRUE,
comment = "#>",
message = FALSE)
## ----guoData-------------------------------------------------------------
library(DeLorean)
library(dplyr)
data(GuoDeLorean)
# Limit number of cores to 2 for CRAN
options(DL.num.cores=min(default.num.cores(), 2))
## ------------------------------------------------------------------------
dl <- de.lorean(guo.expr, guo.gene.meta, guo.cell.meta)
## ------------------------------------------------------------------------
dl <- estimate.hyper(
dl,
sigma.tau=0.5,
length.scale=1.5,
model.name='exactsizes')
## ------------------------------------------------------------------------
num.at.each.stage <- 5
epi.sampled.cells <- guo.cell.meta %>%
filter(capture < "32C" |
"EPI" == cell.type |
"ICM" == cell.type) %>%
group_by(capture) %>%
do(sample_n(., num.at.each.stage))
dl <- filter_cells(dl, cells=epi.sampled.cells$cell)
## ----aov-----------------------------------------------------------------
dl <- aov.dl(dl)
## ------------------------------------------------------------------------
head(dl$aov)
## ------------------------------------------------------------------------
tail(dl$aov)
## ---- exec=FALSE---------------------------------------------------------
dl <- filter_genes(dl, genes=head(dl$aov, 20)$gene)
## ----fitDL, cache=TRUE, results='hide'-----------------------------------
dl <- fit.dl(dl, method='vb')
## ------------------------------------------------------------------------
dl <- examine.convergence(dl)
plot(dl, type='Rhat')
## ----plot----------------------------------------------------------------
plot(dl, type='pseudotime')
## ------------------------------------------------------------------------
dl <- make.predictions(dl)
plot(dl, type='profiles')
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