computeMarkers: computeMarkers

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

Calculates markers and differentially expressed genes.

Usage

computeMarkers(object, cap.ones = F, ...)

Arguments

sce

object of the SingleCellExperiment class.

speed.preset

by default speed.preset='slow'. It 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

Value

object of the SingleCellExperiment class with raw data of differential expression safely stored inside in an hidden place.

Examples

sce=computeMarkers(sce)

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