diagnose: Perform diagnoisis of dispersion on the expression profile to...

In scBFA: A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq

Description

Perform diagnoisis of dispersion on the expression profile to check whether scBFA works on specific dataset

Usage

diagnose(
  scData,
  sampleInfo = NULL,
  disperType = "Fitted",
  diagnose_feature = "dispersion"
)

Arguments

scData	can be a raw count matrix, in which rows are genes and columns are cells; can be a seurat object; can be a SingleCellExperiment object.
sampleInfo	sample level feature matrix,e.g batch effect,experimental conditions in which rows are cells,columns are number of covariates.Default is NULL
disperType	a parameter to tell which dispersion estimate the user can plot DESeq2 offers stepwise dispersion estimate, a gene wise dispersion estimate using "GeneEst", dispersion estimate from fitted disperions ~ mean curve (using "Fitted") And final MAP estimate,using "Map". Default value is "Fitted"
diagnose_feature	a parameter to determine whether the user want to check GDR or dispersion.

Value

A Figure to tell the where the input data's dispersion ~ tpm curve align to the 14 benchmark datasets in Figure 2.a or Gene detection rate

Examples

data(exprdata)
diagnose(scData = exprdata)

scBFA documentation built on Nov. 8, 2020, 5:24 p.m.