decent: DECENT
In cz-ye/DECENT: Differential Expression with Capture Efficiency adjustmeNT

decent

R Documentation

DECENT

Description

Differential Expression with Capture Efficiency adjustmeNT

Usage

decent(data.obs, X, W = NULL, spikes = NULL, spike.conc = NULL,
  CE.range = c(0.02, 0.1), tau.init = c(-5, 0), tau.global = T,
  tau.est = "endo", use.spikes = FALSE, normalize = "ML",
  GQ.approx = TRUE, maxit = 30, parallel = T, n.cores = 0,
  s.imputed = F, E.imputed = F, dir = "./")

Arguments

`data.obs`	Observed count matrix for endogeneous genes, rows represent genes, columns represent cells.
`X`	An R model formula with covariates of interest (cell-type) as factor on the righthand side.
`W`	An R model formula with other covariates to adjust DE analysis on the righthand side. Default NULL
`spike.conc`	A vector of theoretical count for each spike-in in one cell (ONLY needed if spikes = `TRUE`).
`CE.range`	A two-element vector of the lower limit and upper limit for the estimated range of capture efficiencies (ONLY needed if spikes = `FALSE`, default [0.02, 0.10]).
`tau.init`	initial estimates (intcp,slope) that link Beta-Binomial dispersion parameter to the mean expression.
`tau.global`	whether to use the same tau parameters across cell. Default TRUE
`tau.est`	Methods to estimate tau parameters. The default 'endo' corresponds to using endogeneous genes. Other options are 'spikes' that corresponds to using spike-ins and 'none', which means tau.init is not further estimated.
`use.spikes`	If `TRUE`, use spike-ins to estimate capture efficiencies.
`normalize`	Method for estimating size factors, either 'ML' (maximum likelihood, Ye et al., 2017) or 'TMM' (Robinson et al., 2010).
`GQ.approx`	If `TRUE`, use Gaussian-Quadrature approximation to speed up E-step.
`parallel`	If `TRUE`, run DECENT in parallel.
`n.cores`	Number of CPU cores to use, default is all (ONLY if parallel=`TRUE`).
`s.imputed`	If `TRUE`, save the single imputed data matrix under the output diretory.
`E.imputed`	If `TRUE`, save the mean imputed data matrix under the output diretory.
`dir`	Directory to save all outputs, including EM algorithm estimates of no-DE model and the LRT output.
`spike`	Observed count matrix for spike-ins, rows represent spike-ins, columns represent cells. Only needed if spikes = `TRUE`).