CheckBackgroundCutoff: Check different background cutoffs and recommend an...

In zijianni/scCB2: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data

Check different background cutoffs and recommend an appropriate one

Description

The key parameter of CB2 as well as other similar methods is the background cutoff, which divides barcodes into two groups: (1) small barcodes that are most likely to be background; (2) the rest barcodes that can be either background or cell, and remain to be tested. Those small barcodes will be used to estimate a background distribution, which guides the identification of cells from background. It is crucial to have an unbiased estimation of the background distribution.

Usage

CheckBackgroundCutoff(RawDat)

Arguments

RawDat

Matrix. Supports standard matrix or sparse matrix. This is the raw feature-by-barcode count matrix.

Details

An appropriate background cutoff should be reasonably large to contain enough background information, but shouldn't be too large to mistakenly include real cells. We recommend a background cutoff which (1) puts more than 90 (2) puts more than 10 The smallest cutoff satisfying either condition is the recommended cutoff.

Value

A list containing a data frame summarizing background information under different background cutoffs, and the recommended background cutoff for the input data. For the data frame, 'n_bg_bcs' is the number of barcodes less or equal to the cutoff, 'n_bg_counts' is the number of UMI counts within the barcodes less or equal to the cutoff, 'prop_bg_bcs' and 'prop_bg_counts' are the corresponding proportions.

Examples

data(mbrainSub)
CheckBackgroundCutoff(mbrainSub)

zijianni/scCB2 documentation built on April 24, 2023, 12:55 p.m.