bigscale: bigSCale2.0

In zhongmicai/bigSCale2_singleCell: A framework for the analysis of single cell data (RNA-seq and ATAC-seq) including clustering,phenotyping,pseudotime and inferring gene regulatory networks.

Description

Compute cell clusters, markers and pseudotime

Usage

bigscale(sce, speed.preset = "slow", compute.pseudo = TRUE,
  memory.save = FALSE, clustering = "normal")

Arguments

sce	object of the SingleCellExperiment class. The required elements are counts(sce) and rownames(sce). Optionally, you can also fill colData(sce) with any annotation such as batch, condition: they will be displayed in the plots.
speed.preset	regulates the speed vs. accuracy in the computation of the marker and differentially expressed genes. slow Reccomended for most datasets, provides best marker accuracy but slowest computational time. normal A balance between marker accuracy and computational time. fast Fastest computational time, if you are in a hurry and you have lots of cell (>15K) you can use this
memory.save	enables a series of tricks to reduce RAM memory usage. Aware of one case (in linux) in which this option causes irreversible error.
clustering	setting clustering='recursive' forces the immediate and accurate detection of cell subtypes.

Value

An sce object storing the markers, pseudotime, cluster and other results. To access the results you can use several S4 methods liste below. Also check the online quick start tutorial over

See Also

[ViewSignatures()]

Examples

sce=bigscale(sce)

zhongmicai/bigSCale2_singleCell documentation built on Nov. 5, 2019, 1:26 p.m.