projback: Compute Cell Projections for Randomized Background...

In RaceID: Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data

Compute Cell Projections for Randomized Background Distribution

Description

This function computes the projections of cells onto inter-cluster links for randomized cell positions in a high-dimensional embedded space. Significance of link based on an increased number of cells on a link is inferred based on this background model.

Usage

projback(object, pdishuf = 500, fast = FALSE, rseed = 17000, verbose = TRUE)

Arguments

object	Ltree class object.
pdishuf	Number of randomizations of cell positions for which to compute projections of cells on inter-cluster links. Default is 2000. No randomizations are needed in this mode and the function will do nothing. Default is TRUE.
fast	logical. If TRUE and nmode=FALSE cells will still be assigned to links based on maximum projections but a fast approximate background model will be used to infer significance. The function will do nothing in this case. Default is FALSE.
rseed	Integer number used as seed to ensure reproducibility of randomizations. Defaut is 17000.
verbose	logical. If FALSE then status output messages are disabled. Default is TRUE.

Value

An Ltree class object with all information on randomized cell projections onto links stored in the prbacka slot.

Examples

sc <- SCseq(intestinalDataSmall)
sc <- filterdata(sc)
sc <- compdist(sc)
sc <- clustexp(sc)
sc <- findoutliers(sc)
sc <- comptsne(sc)
ltr <- Ltree(sc)
ltr <- compentropy(ltr)
ltr <- projcells(ltr,nmode=FALSE)
ltr <- projback(ltr,pdishuf=50)

RaceID documentation built on Sept. 28, 2023, 5:06 p.m.