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samuel-marsh / scCustomize

R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.
https://samuel-marsh.github.io/scCustomize/
GNU General Public License v3.0
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QC_Plots_Mito for merged dual cellbender-cellranger seurat object does not match that for individual cellbender-cellranger seurat object #208

Open mph270 opened 3 weeks ago

mph270 commented 3 weeks ago

Hi! I am using your functions to merge cellbender and cellranger h5 files to assess background removal. I have a snRNAseq experiment from 4x WT and 4x KO samples. Everything seems to work smoothly, except for some reason one of my animals (KO2) looks very different (many cells with very high % mitochondrial genes) in the QC_Plots_Mito function when looking at the merged seurat object. When I process this animal individually, the QC_Plots_Mito looks much more similar to the % mitochondrial genes of the other animals (lower %). I repeated cellbender with custom "expected-cells" and "total-droplets-included" based on the barcode ranked plot for each sample and got the same result. Could you help me figure out why the % mitochondrial genes are plotting differently for this animal depending on whether the data are merged first?

I do get an error message when running this plot: "Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead." but the plot is still generated.

N.B. in attached image file, the individual KO2 QC_Plots_Mito is on the right (plotted as SeuratProject).

Thank you so much for your help!

Done.

CellBender Functionality & Plotting using scCustomize for MERGED data (4x WT and 4x KO)

library(ggplot2) library(dplyr) library(magrittr) library(patchwork) library(viridis) library(Seurat) library(scCustomize) library(qs) library(future)

cell_bender_merged <- Read_CellBender_h5_Multi_File(data_dir = "/d2/studies/Molly/snRNAseq/cellbender_second_pass", custom_name = "_cellbender_filtered.h5", sample_names = c("WT1", "WT2", "WT3", "WT4", "KO1", "KO2", "KO3", "KO4"), merge = TRUE)

cell_ranger_merged <- Read10X_h5_GEO(data_dir = "/d2/studies/Molly/snRNAseq/cellranger_h5files", sample_names = c("WT1", "WT2", "WT3", "WT4", "KO1", "KO2", "KO3", "KO4"), shared_suffix = "_filtered_feature_bc_matrix.h5", merge = TRUE)

dual_seurat <- Create_CellBender_Merged_Seurat(raw_cell_bender_matrix = cell_bender_merged, raw_counts_matrix = cell_ranger_merged, raw_assay_name = "RAW")

Add a new column 'genotype' to the Seurat object's metadata (1 and 2 refer to the first two characters/letters of "WT" and "KO")

dual_seurat$genotype <- substr(dual_seurat$orig.ident, 1, 2)

Verify that the new column was added and contains the correct values

table(dual_seurat$genotype)

Reorder levels of seurat object so WT first KO second

Example for reordering orig.ident

dual_seurat$orig.ident <- factor(dual_seurat$orig.ident, levels = c("WT1", "WT2", "WT3", "WT4", "KO1", "KO2", "KO3", "KO4"))

Example for reordering genotype

dual_seurat$genotype <- factor(dual_seurat$genotype, levels = c("WT", "KO"))

dual_seurat <- Add_CellBender_Diff(seurat_object = dual_seurat, raw_assay_name = "RAW", cell_bender_assay_name = "RNA")

head(dual_seurat@meta.data, 5)

dual_seurat <- Add_Mito_Ribo(object = dual_seurat, species = "Mouse")

dual_seurat <- Add_Cell_Complexity(object = dual_seurat)

median_stats <- Median_Stats(seurat_object = dual_seurat, group_by_var = "orig.ident", median_var = c("nCount_RAW", "nFeature_RAW", "nCount_Diff", "nFeature_Diff")) median_stats #view results

write.csv(median_stats, file = "cellbender_median_stats.csv", row.names = FALSE)

feature_diff <- CellBender_Feature_Diff(seurat_object = dual_seurat, raw_assay = "RAW", cell_bender_assay = "RNA") feature_diff #view results write.csv(feature_diff, file = "cellbender_feature_diff.csv", row.names = TRUE)

p1 <- CellBender_Diff_Plot(feature_diff_df = feature_diff) p2 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, pct_diff_threshold = 50) p3 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, num_features = 500, pct_diff_threshold = NULL) p4 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, num_labels = 10) p5 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, label = F) p6 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, custom_labels = "Rpl32")

combined_plot <- wrap_plots(p1, p2, p3, p4, p5, p6, ncol = 2)

p7 <- QC_Plots_Genes(seurat_object = dual_seurat, low_cutoff = 500, high_cutoff = 8000) + scale_x_discrete(limits = levels(dual_seurat$orig.ident)) p8 <- QC_Plots_UMIs(seurat_object = dual_seurat, low_cutoff = 1000, high_cutoff = 80000) + scale_x_discrete(limits = levels(dual_seurat$orig.ident)) p9 <- QC_Plots_Mito(seurat_object = dual_seurat, high_cutoff = 5) + scale_x_discrete(limits = levels(dual_seurat$orig.ident)) p10 <- QC_Plots_Complexity(seurat_object = dual_seurat, high_cutoff = 0.8) + scale_x_discrete(limits = levels(dual_seurat$orig.ident))

wrap_plots(p7, p8, p9, p10, ncol = 2)

combined_cellbender_QC_plots <- wrap_plots(p7, p8, p9, p10, ncol = 2)

p11 <- QC_Plots_Genes(seurat_object = dual_seurat, low_cutoff = 500, high_cutoff = 8000, plot_median = TRUE, pt.size = 0) + scale_x_discrete(limits = levels(dual_seurat$orig.ident)) p12 <- QC_Plots_UMIs(seurat_object = dual_seurat, low_cutoff = 1000, high_cutoff = 80000, plot_median = TRUE, pt.size = 0, y_axis_log = TRUE) + scale_x_discrete(limits = levels(dual_seurat$orig.ident)) p13 <- QC_Plots_Mito(seurat_object = dual_seurat, high_cutoff = 5, plot_median = TRUE, pt.size = 0) + scale_x_discrete(limits = levels(dual_seurat$orig.ident)) p14 <- QC_Plots_Complexity(seurat_object = dual_seurat, high_cutoff = 0.8, , plot_median = TRUE, pt.size = 0) + scale_x_discrete(limits = levels(dual_seurat$orig.ident))

CellBender Functionality & Plotting using scCustomize for INDIVIDUAL data (KO2)

cellbender_KO2 <- Read_CellBender_h5_Mat(file_name = "/d2/studies/Molly/snRNAseq/cellbender_second_pass/KO2_cellbender_filtered.h5")

cellranger_KO2 <- Read10X_h5("/d2/studies/Molly/snRNAseq/cellranger_h5files/KO2_filtered_feature_bc_matrix.h5")

dual_KO2 <- Create_CellBender_Merged_Seurat(raw_cell_bender_matrix = cellbender_KO2, raw_counts_matrix = cellranger_KO2, raw_assay_name = "RAW")

dual_KO2 <- Add_CellBender_Diff(seurat_object = dual_KO2, raw_assay_name = "RAW", cell_bender_assay_name = "RNA")

head(dual_KO2@meta.data, 5)

dual_KO2 <- Add_Mito_Ribo(object = dual_KO2, species = "Mouse")

dual_KO2 <- Add_Cell_Complexity(object = dual_KO2)

median_stats <- Median_Stats(seurat_object = dual_KO2, median_var = c("nCount_RAW", "nFeature_RAW", "nCount_Diff", "nFeature_Diff")) median_stats #view results

setwd("/d2/studies/Molly/snRNAseq/output/cellbender2")

write.csv(median_stats, file = "cellbender_KO2_median_stats.csv", row.names = FALSE)

p16 <- QC_Plots_Genes(seurat_object = dual_KO2, low_cutoff = 500, high_cutoff = 8000) p17 <- QC_Plots_UMIs(seurat_object = dual_KO2, low_cutoff = 1000, high_cutoff = 80000) p18 <- QC_Plots_Mito(seurat_object = dual_KO2, high_cutoff = 5) p19 <- QC_Plots_Complexity(seurat_object = dual_KO2, high_cutoff = 0.8)

wrap_plots(p16, p17, p18, p19, ncol = 4)

wrap_plots(p9, p18) #this is image attached below

Rplot

sessionInfo() R version 4.4.1 (2024-06-14) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 22.04.5 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

time zone: US/Eastern tzcode source: system (glibc)

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

other attached packages: [1] qs_0.27.2 scCustomize_2.1.2 Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4
[6] viridis_0.6.5 viridisLite_0.4.2 patchwork_1.3.0 magrittr_2.0.3 dplyr_1.1.4
[11] ggplot2_3.5.1

loaded via a namespace (and not attached): [1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.9 shape_1.4.6.1
[5] spatstat.utils_3.1-0 ggbeeswarm_0.7.2 farver_2.1.2 ragg_1.3.3
[9] GlobalOptions_0.1.2 vctrs_0.6.5 ROCR_1.0-11 spatstat.explore_3.3-2 [13] paletteer_1.6.0 janitor_2.2.0 htmltools_0.5.8.1 forcats_1.0.0
[17] sctransform_0.4.1 parallelly_1.38.0 KernSmooth_2.23-24 htmlwidgets_1.6.4
[21] ica_1.0-3 plyr_1.8.9 plotly_4.10.4 zoo_1.8-12
[25] lubridate_1.9.3 igraph_2.0.3 mime_0.12 lifecycle_1.0.4
[29] pkgconfig_2.0.3 Matrix_1.6-5 R6_2.5.1 fastmap_1.2.0
[33] fitdistrplus_1.2-1 future_1.34.0 shiny_1.9.1 snakecase_0.11.1
[37] digest_0.6.37 colorspace_2.1-1 rematch2_2.1.2 tensor_1.5
[41] prismatic_1.1.2 RSpectra_0.16-2 irlba_2.3.5.1 textshaping_0.4.0
[45] labeling_0.4.3 progressr_0.14.0 fansi_1.0.6 spatstat.sparse_3.1-0 [49] timechange_0.3.0 httr_1.4.7 polyclip_1.10-7 abind_1.4-8
[53] compiler_4.4.1 bit64_4.5.2 withr_3.0.1 fastDummies_1.7.4
[57] MASS_7.3-61 tools_4.4.1 vipor_0.4.7 lmtest_0.9-40
[61] beeswarm_0.4.0 httpuv_1.6.15 future.apply_1.11.2 goftest_1.2-3
[65] glue_1.8.0 nlme_3.1-165 promises_1.3.0 grid_4.4.1
[69] Rtsne_0.17 cluster_2.1.6 reshape2_1.4.4 generics_0.1.3
[73] hdf5r_1.3.11 gtable_0.3.5 spatstat.data_3.1-2 tidyr_1.3.1
[77] RApiSerialize_0.1.4 data.table_1.16.0 stringfish_0.16.0 utf8_1.2.4
[81] spatstat.geom_3.3-3 RcppAnnoy_0.0.22 ggrepel_0.9.6 RANN_2.6.2
[85] pillar_1.9.0 stringr_1.5.1 spam_2.10-0 RcppHNSW_0.6.0
[89] ggprism_1.0.5 later_1.3.2 circlize_0.4.16 splines_4.4.1
[93] lattice_0.22-5 bit_4.5.0 survival_3.7-0 deldir_2.0-4
[97] tidyselect_1.2.1 miniUI_0.1.1.1 pbapply_1.7-2 gridExtra_2.3
[101] svglite_2.1.3 scattermore_1.2 matrixStats_1.4.1 stringi_1.8.4
[105] lazyeval_0.2.2 codetools_0.2-19 tibble_3.2.1 cli_3.6.3
[109] RcppParallel_5.1.9 uwot_0.2.2 systemfonts_1.1.0 xtable_1.8-4
[113] reticulate_1.39.0 munsell_0.5.1 Rcpp_1.0.13 globals_0.16.3
[117] spatstat.random_3.3-2 png_0.1-8 ggrastr_1.0.2 spatstat.univar_3.0-1 [121] parallel_4.4.1 dotCall64_1.1-1 listenv_0.9.1 scales_1.3.0
[125] ggridges_0.5.6 leiden_0.4.3.1 purrr_1.0.2 rlang_1.1.4
[129] cowplot_1.1.3

samuel-marsh commented 1 week ago

Hi @mph270,

Sorry for the delay getting back to you. So my current best guess if that the issue here is likely related to use of multiple datasets vs 1 dataset. By default Create_CellBender_Merged_Seurat sets min_cells = 5 and min_features = 200. While min_features will filter the same cells regardless but which genes are kept vs removed based on min_cells will be different.

Can you post the plots if you re-run your above code but set min_cells = 0 and min_features = 0?

Thanks, Sam

mph270 commented 1 week ago

Hi Sam,

Thanks so much for your reply. I'm attaching the image of the plot after running my code but setting min_cells and min_features to 0. It looks much the same as before, so not sure if that is the issue.

Thanks again, Molly Mito repeat

samuel-marsh commented 1 week ago

Hi Molly,

Are you able to share the raw files for these samples? If needed I can provide code to replace and randomize non-mito gene names to anonymize the data. If so please send me email at samuel.marsh@childrens.harvard.edu and I can share links to upload data.

Best, Sam

mph270 commented 1 week ago

Hi Sam,

Yes, I'd be happy to share the raw files. I sent an email to you from my work email. Thanks so much for your help!

Best, Molly