inst/doc/DeLorean.R

In DeLorean: Estimates Pseudotimes for Single Cell Expression Data

## ----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')