library(Seurat)
library(ggplot2)
library(patchwork)
options(future.globals.maxSize = 3e+09)

# on my data
obj <- Read10X( data.dir = "./filtered_feature_bc_matrix" );
obj <- CreateSeuratObject( counts = obj );
obj[[ "percent.mt" ]] <- PercentageFeatureSet( obj, pattern="^mt-" );
a <- SCTransform( obj, method = "glmGamPoi", variable.features.n = 3000, vars.to.regress = NULL, verbose = F );
b <- SCTransform( obj, method = "glmGamPoi", variable.features.n = 3000, vars.to.regress = "percent.mt", verbose = F );
identical( a[[ "SCT" ]]$scale.data, b[[ "SCT" ]]$scale.data );
[1] TRUE

library(SeuratData)

# on pbmcsca data
pbmcsca <- LoadData( "pbmcsca" );
pbmcsca[[ "percent.mt" ]] <- PercentageFeatureSet( pbmcsca, pattern="^MT-" );
c <- SCTransform( pbmcsca, method = "glmGamPoi", variable.features.n = 3000, vars.to.regress = NULL, verbose = F );
d <- SCTransform( pbmcsca, method = "glmGamPoi", variable.features.n = 3000, vars.to.regress = "percent.mt", verbose = F );
identical( c[[ "SCT" ]]$scale.data, d[[ "SCT" ]]$scale.data );
[1] TRUE

# on pbmc3k
pbmc <- LoadData( "pbmc3k" );
pbmc[[ "percent.mt" ]] <- PercentageFeatureSet( pbmc, pattern = "^MT-" );
g <- SCTransform( pbmc, method = "glmGamPoi", variable.features.n = 3000, vars.to.regress = NULL, verbose = F );
h <- SCTransform( pbmc, method = "glmGamPoi", variable.features.n = 3000, vars.to.regress = "percent.mt", verbose = F );
identical( g[[ "SCT" ]]$scale.data, h[[ "SCT" ]]$scale.data );
[1] FALSE

sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] pbmcsca.SeuratData_3.0.0 pbmcref.SeuratData_1.0.0 pbmc3k.SeuratData_3.1.4
[4] SeuratData_0.2.2.9001    patchwork_1.2.0          ggplot2_3.4.4
[7] Seurat_5.0.1             SeuratObject_5.0.1       sp_2.1-2

loaded via a namespace (and not attached):
  [1] Rtsne_0.17                  colorspace_2.1-0
  [3] deldir_2.0-2                ellipsis_0.3.2
  [5] ggridges_0.5.5              XVector_0.38.0
  [7] GenomicRanges_1.50.2        RcppHNSW_0.5.0
  [9] spatstat.data_3.0-4         leiden_0.4.3.1
 [11] listenv_0.9.0               ggrepel_0.9.5
 [13] RSpectra_0.16-1             fansi_1.0.6
 [15] sparseMatrixStats_1.10.0    codetools_0.2-19
 [17] splines_4.2.3               R.methodsS3_1.8.2
 [19] polyclip_1.10-6             spam_2.10-0
 [21] jsonlite_1.8.8              ica_1.0-3
 [23] cluster_2.1.6               png_0.1-8
 [25] R.oo_1.25.0                 uwot_0.1.16
 [27] shiny_1.8.0                 sctransform_0.4.1
 [29] spatstat.sparse_3.0-3       compiler_4.2.3
 [31] httr_1.4.7                  Matrix_1.6-4
 [33] fastmap_1.1.1               lazyeval_0.2.2
 [35] cli_3.6.2                   later_1.3.2
 [37] htmltools_0.5.7             tools_4.2.3
 [39] igraph_1.6.0                dotCall64_1.1-1
 [41] GenomeInfoDbData_1.2.9      gtable_0.3.4
 [43] glue_1.7.0                  RANN_2.6.1
 [45] reshape2_1.4.4              dplyr_1.1.4
 [47] rappdirs_0.3.3              Rcpp_1.0.12
 [49] Biobase_2.58.0              scattermore_1.2
 [51] vctrs_0.6.5                 spatstat.explore_3.2-5
 [53] nlme_3.1-164                progressr_0.14.0
 [55] DelayedMatrixStats_1.20.0   lmtest_0.9-40
 [57] spatstat.random_3.2-2       stringr_1.5.1
 [59] globals_0.16.2              mime_0.12
 [61] miniUI_0.1.1.1              lifecycle_1.0.4
 [63] irlba_2.3.5.1               goftest_1.2-3
 [65] future_1.33.1               zlibbioc_1.44.0
 [67] MASS_7.3-60.0.1             zoo_1.8-12
 [69] scales_1.3.0                MatrixGenerics_1.10.0
 [71] promises_1.2.1              spatstat.utils_3.0-4
 [73] SummarizedExperiment_1.28.0 parallel_4.2.3
 [75] RColorBrewer_1.1-3          reticulate_1.34.0
 [77] pbapply_1.7-2               gridExtra_2.3
 [79] stringi_1.8.3               S4Vectors_0.36.2
 [81] fastDummies_1.7.3           BiocGenerics_0.44.0
 [83] GenomeInfoDb_1.34.9         bitops_1.0-7
 [85] rlang_1.1.3                 pkgconfig_2.0.3
 [87] matrixStats_1.1.0           lattice_0.22-5
 [89] glmGamPoi_1.10.2            ROCR_1.0-11
 [91] purrr_1.0.2                 tensor_1.5
 [93] htmlwidgets_1.6.4           cowplot_1.1.2
 [95] tidyselect_1.2.0            parallelly_1.36.0
 [97] RcppAnnoy_0.0.21            plyr_1.8.9
 [99] magrittr_2.0.3              R6_2.5.1
[101] IRanges_2.32.0              generics_0.1.3
[103] DelayedArray_0.24.0         pillar_1.9.0
[105] withr_3.0.0                 fitdistrplus_1.1-11
[107] RCurl_1.98-1.14             survival_3.5-7
[109] abind_1.4-5                 tibble_3.2.1
[111] future.apply_1.11.1         crayon_1.5.2
[113] KernSmooth_2.23-22          utf8_1.2.4
[115] spatstat.geom_3.2-7         plotly_4.10.4
[117] grid_4.2.3                  data.table_1.14.10
[119] digest_0.6.34               xtable_1.8-4
[121] tidyr_1.3.0                 httpuv_1.6.13
[123] R.utils_2.12.3              stats4_4.2.3
[125] munsell_0.5.0               viridisLite_0.4.2