Not retrieving Mitochondrial Genes in Single Cell analysis
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Entering edit mode
14 days ago

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  
cell single • 391 views
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isn't it chrM ?

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Thank you Pierre for your time! Actually, SC analysis is a novelty to me! The database searching was performed by a colleague previously. I've verified that mitochondrial genes doesn't present "MT-" nor "chrM" label.

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14 days ago

Are you completely sure that mitochondrial genes in your organism start with "MT-"?

Are you completely sure that your reference genome and gtf contain mitochondrial genes?

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Thank you swbarnes2 for your time! No, I'm not completely sure of that since it was a colleague that perform this step. I'm begginer in transcriptomics analysis, do you have any suggestion on how can I correctly select reference genome? Btw, my colleague informed that hg38 NCBI reference genome was used.

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13 days ago
Parsa • 0

try pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT") which is without the "-"

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