diagnose {scBFA} | R Documentation

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

### 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)

[Package scBFA version 1.2.0 Index]