Identify and annotate artifacts in spatial transcriptomics data

This function identifies and annotates potential artifacts in spatial transcriptomics data. Artifacts are detected based on local mito variance, and the results are added to the original SpatialExperiment (sce) object.

Usage

findArtifacts(
  spe,
  mito_percent = "expr_chrM_ratio",
  mito_sum = "expr_chrM",
  samples = "sample_id",
  n_rings = 5,
  log = TRUE,
  name = "artifact",
  var_output = TRUE
)

Arguments

spe

A SingleCellExperiment object.

mito_percent

The column name representing the mitochondrial percent. Default is 'expr_chrM_ratio'.

mito_sum

The column name representing sum mitochondrial expression. Default is 'expr_chrM'.

samples

The column name representing sample IDs. Default is 'sample_id'.

n_rings

The number of rings for local mito variance calculation. Default is 5.

log

Logical, indicating whether to log1p transform mito_percent. Default is TRUE.

name

Prefix for the local variance column names. Default is 'artifact'.

var_output

Logical, indicating whether to include local variances in the output. Default is TRUE.

Value

Returns the modified SingleCellExperiment object with artifact annotations.

See also

localVariance

Examples

library(SpotSweeper)
library(SpatialExperiment)
#> Loading required package: SingleCellExperiment
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#>     pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
#>     union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#> 
#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     anyMissing, rowMedians
library(escheR)
#> Loading required package: ggplot2

data(DLPFC_artifact)
spe <- DLPFC_artifact

# find artifacts
spe <- findArtifacts(spe,
    mito_percent = "expr_chrM_ratio",
    mito_sum = "expr_chrM",
    n_rings = 2, # 5 recommended, using 1 for time
    name = "artifact"
)