Feature selection — featureSelect • BatchSVG

The function computes batch-adjusted deviance values, ranks the genes accordingly, and quantifies batch effects in terms of standard deviations from the mean difference. The list follows the order of batch effects provided in batch_effects.

Usage

featureSelect(input, batch_effects = NULL, VGs = NULL)

Arguments

input: A SpatialExperiment object containing spatial transcriptomics data..
batch_effects: A character vector specifying column names in colData(input) that indicate batch effects. Must match existing column names.
VGs: A character vector specifying the variable genes (VGs) to be analyzed. Only genes present in this vector will be retained for feature selection.

Value

A named list where each element corresponds to a batch effect. Each batch contains a data frame with the following columns:

"gene_id": Gene identifier.
"gene_name": Gene name.
"dev_default": Deviance score without batch correction.
"dev_": Deviance score with batch correction.
"rank_default": Rank of the gene based on deviance without batch correction.
"rank_": Rank of the gene based on deviance with batch correction.
"d_diff": Relative change in deviance between default and batch-corrected models.
"nSD_dev_": number of standard deviation of relative change in deviance for the batch.
"r_diff": Rank difference between default and batch-corrected models.
"nSD_rank_": number of standard deviation of rank difference for the batch.

Examples

library(spatialLIBD)
#> Loading required package: 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, saveRDS, 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
spatialLIBD_spe <- fetch_data(type = "spe")
#> adding rname 'https://www.dropbox.com/s/f4wcvtdq428y73p/Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata?dl=1'
#> 2025-03-31 00:32:35.29746 loading file /home/runner/.cache/R/BiocFileCache/38245eec57cd_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1
libd_svg <- read.csv(
    system.file("extdata","libd-all_nnSVG_p-05-features-df.csv",
              package = "BatchSVG"),
    row.names = 1, check.names = FALSE)
   
list_batch_df <- featureSelect(input = spatialLIBD_spe, 
   batch_effects = "subject", VGs = libd_svg$gene_id)
#> Running feature selection without batch...
#> Batch Effect: subject
#> Running feature selection without batch...
#> Calculating deviance and rank difference...