Entering edit mode
5 weeks ago
sakura
•
0
Hello! I ran SCTransform from Seurat on only cancer cells from a few patients (individually) and some cells are showing very low gene expression for all genes. When running FindAllMarkers the clusters with these cells are vastly defined by negative markers:
Top 10 Genes returned for cluster in question (light blue bottom right):
I also noted the pattern of expression matches with nCount_SCT (3rd row, 3rd from left) and this is true of my other samples.
After extracting a column from the counts matrix of of SCT and RNA for a single cell from each end of the UMAP, I saw SCTransform resulted in counts dropping for that cell (Row 2-3, histogram of expression across all genes for one cell pre and post transformation, row 4-5 show expression for each gene pre and post transformation):
Is there anything I can do about this? Why is this happening?
Thank you for your help!
My Session:
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
[8] qs_0.25.7 stringr_1.5.1 openxlsx_4.2.5.2 dplyr_1.1.4 data.table_1.15.2 RColorBrewer_1.1-3 patchwork_1.2.0
[15] ggplot2_3.5.0 SeuratDisk_0.0.0.9021 Seurat_5.0.1 SeuratObject_5.0.1 sp_2.1-3
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.3.2 later_1.3.2 bitops_1.0-7 filelock_1.0.3
[6] polyclip_1.10-6 fastDummies_1.7.3 lifecycle_1.0.4 hdf5r_1.3.10 globals_0.16.3
[11] lattice_0.22-5 MASS_7.3-60 magrittr_2.0.3 rmarkdown_2.26 plotly_4.10.4
[16] yaml_2.3.8 httpuv_1.6.14 sctransform_0.4.1 zip_2.3.1 spam_2.10-0
[21] spatstat.sparse_3.0-3 reticulate_1.35.0 cowplot_1.1.3 pbapply_1.7-2 DBI_1.2.2
[26] abind_1.4-5 zlibbioc_1.48.2 Rtsne_0.17 GenomicRanges_1.54.1 BiocGenerics_0.48.1
[31] RCurl_1.98-1.14 rappdirs_0.3.3 GenomeInfoDbData_1.2.11 IRanges_2.36.0 S4Vectors_0.40.2
[36] ggrepel_0.9.5 irlba_2.3.5.1 listenv_0.9.1 spatstat.utils_3.0-4 goftest_1.2-3
[41] RSpectra_0.16-1 spatstat.random_3.2-3 fitdistrplus_1.1-11 parallelly_1.37.1 leiden_0.4.3.1
[46] codetools_0.2-19 DelayedArray_0.28.0 RApiSerialize_0.1.2 tidyselect_1.2.1 farver_2.1.1
[51] UCell_2.6.2 matrixStats_1.2.0 stats4_4.3.2 BiocFileCache_2.10.1 spatstat.explore_3.2-6
[56] jsonlite_1.8.8 BiocNeighbors_1.20.2 ellipsis_0.3.2 progressr_0.14.0 ggridges_0.5.6
[61] survival_3.5-7 tools_4.3.2 ica_1.0-3 Rcpp_1.0.12 glue_1.7.0
[66] gridExtra_2.3 SparseArray_1.2.4 xfun_0.42 MatrixGenerics_1.14.0 GenomeInfoDb_1.38.7
[71] withr_3.0.0 BiocManager_1.30.22 fastmap_1.1.1 fansi_1.0.6 digest_0.6.35
[76] timechange_0.3.0 R6_2.5.1 mime_0.12 colorspace_2.1-0 scattermore_1.2
[81] tensor_1.5 spatstat.data_3.0-4 RSQLite_2.3.5 utf8_1.2.4 generics_0.1.3
[86] httr_1.4.7 htmlwidgets_1.6.4 S4Arrays_1.2.1 uwot_0.1.16 pkgconfig_2.0.3
[91] gtable_0.3.4 blob_1.2.4 lmtest_0.9-40 SingleCellExperiment_1.24.0 XVector_0.42.0
[96] htmltools_0.5.7 dotCall64_1.1-1 scales_1.3.0 Biobase_2.62.0 png_0.1-8
[101] knitr_1.45 rstudioapi_0.15.0 tzdb_0.4.0 reshape2_1.4.4 nlme_3.1-163
[106] curl_5.2.1 cachem_1.0.8 zoo_1.8-12 BiocVersion_3.18.1 KernSmooth_2.23-22
[111] parallel_4.3.2 miniUI_0.1.1.1 AnnotationDbi_1.64.1 pillar_1.9.0 grid_4.3.2
[116] vctrs_0.6.5 RANN_2.6.1 promises_1.2.1 stringfish_0.16.0 dbplyr_2.4.0
[121] xtable_1.8-4 cluster_2.1.4 evaluate_0.23 cli_3.6.2 compiler_4.3.2
[126] rlang_1.1.3 crayon_1.5.2 future.apply_1.11.1 labeling_0.4.3 plyr_1.8.9
[131] stringi_1.8.3 viridisLite_0.4.2 deldir_2.0-4 BiocParallel_1.36.0 munsell_0.5.0
[136] Biostrings_2.70.3 lazyeval_0.2.2 spatstat.geom_3.2-9 Matrix_1.6-5 ExperimentHub_2.10.0
[141] RcppHNSW_0.6.0 hms_1.1.3 bit64_4.0.5 future_1.33.1 KEGGREST_1.42.0
[146] shiny_1.8.0 SummarizedExperiment_1.32.0 interactiveDisplayBase_1.40.0 AnnotationHub_3.10.0 ROCR_1.0-11
[151] igraph_2.0.3 memoise_2.0.1 RcppParallel_5.1.7 bit_4.0.5
