I'm analyzing single-cell RNA-Seq data and encountering issues with identifying mitochondrial genes for quality control.

I am using the Seurat workflow and have implemented the following command to retrieve the percentage of mitochondrial genes:

pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")

However, the output shows 0% mitochondrial genes. Upon further inspection, I found that none of the gene annotations start with "MT-". Despite this, I have identified some mitochondrial genes (e.g., ATP6) in the dataset, indicating that the genes are present but without the "MT-" label.

I am using the hg38 NCBI reference genome.

I attempted to resolve this issue using the following resource: https://github.com/hbctraining/scRNA-seq/blob/master/lessons/mitoRatio.md, but encountered multiple issues with rlang and dbplyr, even after installing previous versions of these packages.

Does anyone have suggestions for performing this quality control step effectively? Any insights or alternative approaches would be greatly appreciated.

sessionInfo()
R version 4.2.3 (2023-03-15 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.utf8  LC_CTYPE=English_United States.utf8    LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                           LC_TIME=English_United States.utf8

attached base packages:
[1] stats4    stats     graphics  grDevices datasets  utils     methods   base

other attached packages:
[1] org.Hs.eg.db_3.16.0  AnnotationDbi_1.60.2 IRanges_2.32.0       S4Vectors_0.36.2     Biobase_2.58.0
[6] BiocGenerics_0.44.0  Seurat_5.1.0         SeuratObject_5.0.2   sp_2.1-4

loaded via a namespace (and not attached):
  [1] shadowtext_0.1.3       spam_2.10-0            fastmatch_1.1-4        plyr_1.8.9             igraph_2.0.3
  [6] lazyeval_0.2.2         splines_4.2.3          RcppHNSW_0.6.0         BiocParallel_1.32.6    listenv_0.9.1
 [11] scattermore_1.2        GenomeInfoDb_1.34.9    ggplot2_3.5.1          digest_0.6.36          htmltools_0.5.8.1
 [16] yulab.utils_0.1.4      GOSemSim_2.24.0        viridis_0.6.5          GO.db_3.16.0           fansi_1.0.6
 [21] magrittr_2.0.3         memoise_2.0.1          tensor_1.5             cluster_2.1.6          ROCR_1.0-11
 [26] globals_0.16.3         Biostrings_2.66.0      graphlayouts_1.1.1     matrixStats_1.3.0      spatstat.sparse_3.1-0
 [31] enrichplot_1.18.4      colorspace_2.1-0       blob_1.2.4             ggrepel_0.9.5          dplyr_1.1.4
 [36] crayon_1.5.3           RCurl_1.98-1.14        jsonlite_1.8.8         scatterpie_0.2.3       spatstat.data_3.1-2
 [41] progressr_0.14.0       survival_3.7-0         zoo_1.8-12             ape_5.8                glue_1.7.0
 [46] polyclip_1.10-6        gtable_0.3.5           zlibbioc_1.44.0        XVector_0.38.0         leiden_0.4.3.1
 [51] future.apply_1.11.2    abind_1.4-5            scales_1.3.0           DOSE_3.24.2            DBI_1.2.3
 [56] spatstat.random_3.2-3  miniUI_0.1.1.1         Rcpp_1.0.12            xtable_1.8-4           viridisLite_0.4.2
 [61] gridGraphics_0.5-1     tidytree_0.4.6         reticulate_1.38.0      bit_4.0.5              dotCall64_1.1-1
 [66] htmlwidgets_1.6.4      httr_1.4.7             fgsea_1.24.0           RColorBrewer_1.1-3     ica_1.0-3
 [71] pkgconfig_2.0.3        farver_2.1.2           uwot_0.2.2             deldir_2.0-4           utf8_1.2.4
 [76] later_1.3.2            ggplotify_0.1.2        tidyselect_1.2.1       rlang_1.1.4            reshape2_1.4.4
 [81] munsell_0.5.1          tools_4.2.3            cachem_1.1.0           downloader_0.4         cli_3.6.3
 [86] generics_0.1.3         RSQLite_2.3.7          gson_0.1.0             ggridges_0.5.6         stringr_1.5.1
 [91] fastmap_1.2.0          goftest_1.2-3          ggtree_3.6.2           bit64_4.0.5            fs_1.6.4
 [96] fitdistrplus_1.1-11    tidygraph_1.3.1        purrr_1.0.2            RANN_2.6.1             KEGGREST_1.38.0
[101] ggraph_2.2.1           pbapply_1.7-2          future_1.33.2          nlme_3.1-165           mime_0.12
[106] aplot_0.2.3            compiler_4.2.3         rstudioapi_0.16.0      plotly_4.10.4          png_0.1-8
[111] spatstat.utils_3.0-5   treeio_1.22.0          tibble_3.2.1           tweenr_2.0.3           stringi_1.8.4
[116] RSpectra_0.16-1        lattice_0.22-6         Matrix_1.6-4           vctrs_0.6.5            pillar_1.9.0
[121] lifecycle_1.0.4        BiocManager_1.30.23    spatstat.geom_3.2-9    lmtest_0.9-40          RcppAnnoy_0.0.22
[126] data.table_1.15.4      cowplot_1.1.3          bitops_1.0-7           irlba_2.3.5.1          httpuv_1.6.15
[131] patchwork_1.2.0        qvalue_2.30.0          R6_2.5.1               promises_1.3.0         renv_1.0.7
[136] KernSmooth_2.23-24     gridExtra_2.3          parallelly_1.37.1      codetools_0.2-20       fastDummies_1.7.3
[141] MASS_7.3-60            withr_3.0.0            sctransform_0.4.1      GenomeInfoDbData_1.2.9 parallel_4.2.3
[146] clusterProfiler_4.6.2  grid_4.2.3             ggfun_0.1.5            tidyr_1.3.1            HDO.db_0.99.1
[151] Rtsne_0.17             spatstat.explore_3.2-7 ggforce_0.4.2          shiny_1.8.1.1