ARCScore: Calculate the ambient RNA contamination (ARC) score

In zijianni/SpotClean: SpotClean adjusts for spot swapping in spatial transcriptomics data

Calculate the ambient RNA contamination (ARC) score

Description

The ARC score is intended for subjectively measuring the level of contamination caused by ambient RNAs for a given spatial transcriptomics or droplet-based single-cell RNA-seq data. Intuitively, this score is a lower bound of the average proportion of contaminated expressions in tissue spots or cell droplets in the observed contaminated data.

ARC score is calculated as follows: (1) estimate the average amount of contaminated UMI counts received per spot (droplet) using the total UMI counts in background spots (empty droplets) divided by total number of spots (droplets). This is an underestimation as it does not account for contamination inside tissue spots or cell droplets. (2) estimate the average amount of observed UMI counts per tissue spot (cell droplet) using the total UMI counts in tissue spots (cell droplets) divided by number of tissue spots (cell droplets). (3) Divide the value in (1) by the value in (2) to get the ARC score.

This lower bound is very conservative as it only accounts for observable contamination in background spots or empty droplets, neglecting contamination in tissue spots or cell droplets. However, it is totally subjective, not relying on any complicated assumptions and modelling. ARC score serves as a relative contamination measurement for comparison among different datasets and protocols.

Usage

arcScore(object, ...)

## Default S3 method:
arcScore(object, background_bcs, ...)

## S3 method for class 'SummarizedExperiment'
arcScore(object, ...)

Arguments

object	A gene-by-barcode count matrix or a slide object created or inherited from createSlide(). Ideally this should give the raw count matrix with all genes and barcodes without any filtering.
...	Arguments passed to other methods
background_bcs	(vector of chr) Background barcodes in count_mat. For spatial data, these are background spots not covered by tissue. For single-cell data, these are empty droplets not containing cells.

Value

(num) The ARC score of given data.

Examples

data(mbrain_raw)
spatial_dir <- system.file(file.path("extdata",
                                     "V1_Adult_Mouse_Brain_spatial"),
                           package = "SpotClean")
mbrain_slide_info <- read10xSlide(tissue_csv_file=file.path(spatial_dir,
                                       "tissue_positions_list.csv"),
             tissue_img_file = file.path(spatial_dir,
                                       "tissue_lowres_image.png"),
             scale_factor_file = file.path(spatial_dir,
                                       "scalefactors_json.json"))

background_bcs <- dplyr::filter(mbrain_slide_info$slide, tissue==0)$barcode
arcScore(mbrain_raw, background_bcs)

mbrain_obj <- createSlide(mbrain_raw, mbrain_slide_info)
arcScore(mbrain_obj)

zijianni/SpotClean documentation built on Nov. 15, 2023, 12:53 a.m.