Last updated: 2020-11-18

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source("code/load_packages.R")
source("code/plot_QC_function.R") #change settings in script to get different layout per subpanel for this figure
dir.create("output/paper_figures") # location where paper figures are stored

Introduction

The inDrop-RAID data contains samples 9 timepoints of aIg stimulated BJAB, and 3 additional timepoints with ibrutinib drug. Script below extracts all metadata (sequencing sample names, adds metadata info per sample (prot or RNA library)), and reads all data tables into R. The metadata table is saved in output folder: “output/metadata.csv”

For QC and filtering, cells with matching RNA and protein information are used to create a Seurat object (settings RNA: min.cells = 100, min.features = 100; proteins added as additional modality PROT). Several QC stats are calculated, and the object is saved in: “output/seu_combined_raw.rds”

source("code/Import_and_create_seuratObj.R")

Before QC dataset properties

Seurat object:

seu_combined
An object of class Seurat 
9300 features across 11061 samples within 2 assays 
Active assay: RNA (9220 features, 0 variable features)
 1 other assay present: PROT

Table Overview of per sample properties.

kable(seu_combined@meta.data %>% 
        group_by(condition) %>% 
        summarise(`Total number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Ribosomal counts (Median %)` = round(median(percent.rb),2),
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  )) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
condition Total number of cells Median counts RNA Median Number genes Median Mitochondrial counts (Median %) Ribosomal counts (Median %) Median counts PROT Number proteins
000.aIg.contr 688 1379 961 7.24 6.68 1108 61
002.aIg.contr 958 780 594 7.72 6.74 984 59
004.aIg.contr 545 842 628 7.92 5.97 1035 59
006.aIg.contr 820 1060 774 8.26 6.86 720 54
006.aIg.ibr 1148 464 360 10.99 6.04 756 57
010.aIg.contr 748 528 412 11.19 6.18 724 54
030.aIg.contr 1263 753 552 11.89 6.49 813 56
030.aIg.ibr 1601 767 563 12.18 5.92 817 55
060.aIg.contr 879 642 496 8.74 6.65 891 57
180.aIg.contr 1121 717 555 7.37 6.79 867 57
180.aIg.ibr 1290 743 580 6.30 6.90 808 57

Table Overview of full dataset properties.

kable(seu_combined@meta.data %>% 
        summarise(`Number of cells` = round(n(),0),
          `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Ribosomal counts (Median %)` = round(median(percent.rb),2),
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  ) %>%
        t()) %>%

  kable_styling(bootstrap_options = c("striped", "hover"))
Number of cells 11061.00
Median counts RNA 725.00
Median Number genes 550.00
Median Mitochondrial counts (Median %) 9.20
Ribosomal counts (Median %) 6.46
Median counts PROT 835.00
Number proteins 57.00

Quality Control & filtering

plot_RNA_nCount <- plot_QC_paper(seu_object = seu_combined, 
                                 feature = "nCount_RNA", 
                                 ytext = "Total UMI counts per cell",
                                 xtext = "Time aIg stimulation (minutes)",
                                 paneltitle = "Keep cells < 4000 RNA counts",
                                 colorviolin = "dodgerblue2" ) + 
                        geom_hline(yintercept = 4000, size = 0.3) +
                        theme(axis.title.x = element_blank())

plot_RNA_ngenes <- plot_QC_paper(seu_object = seu_combined, 
                                 feature = "nFeature_RNA", 
                                 ytext = "Total genes per cell",
                                 xtext = "Time aIg stimulation (minutes)",
                                 paneltitle = "Keep cells >150 genes",
                                 colorviolin = "dodgerblue2" ) + 
                        geom_hline(yintercept = 150, size = 0.3) +
                        theme(axis.title.x = element_blank())
  
plot_percent.mt <- plot_QC_paper(seu_object = seu_combined, 
                                 feature = "percent.mt", 
                                 ytext = "% Mitochondrial counts",
                                 xtext = "Time aIg stimulation (minutes)",
                                 paneltitle = "Keep cells < 20 % and samples < median 10 % mitochondrial",
                                 colorviolin = "dodgerblue2" ) +
                        geom_hline(yintercept = 10, size = 0.3) +
                        geom_hline(yintercept = 15, color = "grey", size = 0.3) +
                        theme(axis.title.x = element_blank())

plot_percent.rb <- plot_QC_paper(seu_object = seu_combined, 
                                 feature = "percent.rb", 
                                 ytext = "% Ribosomal counts",
                                 xtext = "Time aIg stimulation (minutes)",
                                 paneltitle = "Stable % ribosomal counts over time",
                                 colorviolin = "dodgerblue2" ) +
                        theme(axis.title.x = element_blank())
  
  
plot_PROT_nCount <- plot_QC_paper(seu_object = seu_combined, 
                                 feature = "nCount_PROT", 
                                 ytext =  "Total UMI counts per cell",
                                 xtext = "Time aIg stimulation (minutes)",
                                 paneltitle = "Keep cells < 3000 PROT counts",
                                 colorviolin = "deeppink3" ) +
                        geom_hline(yintercept = 3000, size = 0.3) +
                        theme(axis.title.x = element_blank())

plot_PROT_nproteins <- plot_QC_paper(seu_object = seu_combined, 
                                 feature = "nFeature_PROT", 
                                 ytext =  "Total proteins per cell",
                                 xtext = "Time aIg stimulation (minutes)",
                                 paneltitle = "Keep cells >45 proteins",
                                 colorviolin = "deeppink3" ) +
                        geom_hline(yintercept = 45, size = 0.3) 

plot_percent.H3 <- plot_QC_paper(seu_object = seu_combined, 
                                 feature = "percent.HisH3", 
                                 ytext =   "% Histone H3 counts",
                                 xtext = "Time aIg stimulation (minutes)",
                                 paneltitle = "Variation in % Histone H3 counts",
                                 colorviolin = "deeppink3" ) 
  
plot.QC <- plot_grid(plot_RNA_nCount, plot_RNA_ngenes,  plot_percent.mt, plot_percent.rb,plot_PROT_nCount,plot_PROT_nproteins, plot_percent.H3, labels = c('a', 'b', 'c','d' , 'e', 'f', 'g'), label_size = 10, ncol = 2)

ggsave(plot.QC, filename = "output/paper_figures/Suppl_QC_filters.pdf", width = 183, height = 200, units = "mm",  dpi = 300, useDingbats = FALSE)
ggsave(plot.QC, filename = "output/paper_figures/Suppl_QC_filters.png", width = 183, height = 200, units = "mm",  dpi = 300)
plot.QC

Supplementary Figure Thresholds for selection of high-quality samples and cells from the QuRIE-seq datasets.

Based on the indicated cut-offs, high-quality cellsare filtered for further analysis. Based on the good protein quality of samples and m<11% median mitochondiral counts per sample, timepoints 0, 2, 4, 6, 60 and 180 are kept for further analysis.

seu_combined_filtered <- subset(seu_combined, subset = nFeature_RNA > 150 & nCount_RNA < 4000 & nFeature_PROT > 45 & nCount_PROT < 3000 & percent.mt < 15)

seu_combined_filtered <- subset(seu_combined_filtered, idents = c("010.aIg.contr","030.aIg.contr","030.aIg.ibr"), invert = TRUE)

Normalize and scale

# run sctransform with default settings.
seu_combined_filtered <- SCTransform(seu_combined_filtered,
                            assay = "RNA",
                            new.assay.name = "SCT.RNA",
                            do.correct.umi = TRUE,
                            ncells = NULL,
                            variable.features.n = 3000,
                            vars.to.regress = c("percent.mt", "nCount_RNA"), # substantial variation between samples & cells in mito and ncount
                            do.scale = FALSE,
                            do.center = TRUE,
                            conserve.memory = FALSE,
                            return.only.var.genes = FALSE,
                            seed.use = 42,
                            verbose = FALSE
                            )
# Add some metadata to normalized data (ncounts & percent mt)
seu_combined_filtered <- AddMetaData(seu_combined_filtered, as.data.frame(seu_combined_filtered@assays$SCT.RNA@counts) %>% summarise_all(funs(sum)) %>% unlist(), col.name = "nCount_RNA_SCT")

seu_combined_filtered <- PercentageFeatureSet(seu_combined_filtered, pattern = "^MT\\.|^MTRN", col.name = "percent.mt.aftersct", assay = "SCT.RNA")

## cell cycle scoring metadata
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
seu_combined_filtered <- CellCycleScoring(seu_combined_filtered, s.features = s.genes, g2m.features = g2m.genes, set.ident = FALSE, assay = "SCT.RNA")

seu_combined_filtered[["S.score"]] <- seu_combined_filtered@meta.data$S.Score
seu_combined_filtered[["G2M.score"]] <- seu_combined_filtered@meta.data$G2M.Score
seu_combined_filtered[["CCphase"]] <- seu_combined_filtered@meta.data$Phase
all.prot <- rownames(seu_combined_filtered[["PROT"]])

seu_combined_filtered <- NormalizeData(seu_combined_filtered, assay = "PROT", normalization.method = "CLR", verbose = FALSE)

seu_combined_filtered <- ScaleData(seu_combined_filtered, assay = "PROT", features = all.prot, vars.to.regress = c("nFeature_PROT","nCount_PROT", "percent.HisH3"))
Regressing out nFeature_PROT, nCount_PROT, percent.HisH3
Centering and scaling data matrix

Seurat object with filtered cells and normalized counts is stored in “output/seu_combined_filtered_normalized.rds”

saveRDS(seu_combined_filtered, "output/seu_combined_filtered_normalized.rds")

Subset of samples

The manuscript describes two analysis of different collection of samples:
* Effect of aIg stimulation over two time-scales (see MOFA aIg page) * Effect of ibrutinib on the cell-state at these two timescales (see MOFA ibru page)

seu_combined_aIg_selected <- subset(seu_combined_filtered, idents = c("006.aIg.ibr", "180.aIg.ibr"), invert = TRUE)

saveRDS(seu_combined_aIg_selected, "output/seu_aIG_samples.rds")
seu_combined_ibru_selected <- subset(seu_combined_filtered, idents = c("002.aIg.contr","004.aIg.contr","060.aIg.contr"), invert = TRUE)

saveRDS(seu_combined_ibru_selected, "output/seu_ibru_samples.rds")

Filtered dataset properties

Overview of the number of cells and data properties of all samples, aIg subset of samples, or ibrutinib subset of samples.

Full dataset

seu_combined_filtered
An object of class Seurat 
18520 features across 6976 samples within 3 assays 
Active assay: SCT.RNA (9220 features, 3000 variable features)
 2 other assays present: RNA, PROT

Table Overview of per sample properties after filtering

kable(seu_combined_filtered@meta.data %>% 
        group_by(condition) %>% 
        summarise(`Number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Ribosomal counts (Median %)` = round(median(percent.rb),2),
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  )) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
condition Number of cells Median counts RNA Median Number genes Median Mitochondrial counts (Median %) Ribosomal counts (Median %) Median counts PROT Number proteins
000.aIg.contr 649 1407 977 7.11 6.70 1108 60
002.aIg.contr 927 788 602 7.61 6.76 992 59
004.aIg.contr 511 866 646 7.75 6.02 1043 59
006.aIg.contr 716 1098 804 8.02 6.93 752 54
006.aIg.ibr 953 498 390 10.41 5.98 776 57
060.aIg.contr 865 644 497 8.72 6.65 894 57
180.aIg.contr 1099 717 555 7.37 6.79 871 57
180.aIg.ibr 1256 740 578 6.31 6.90 810 57

Table Overview of full filtered dataset properties.

kable(seu_combined_filtered@meta.data %>% 
        summarise(`Number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Ribosomal counts (Median %)` = round(median(percent.rb),2),
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  ) %>%
        t()) %>%

  kable_styling(bootstrap_options = c("striped", "hover"))
Number of cells 6976.00
Median counts RNA 746.00
Median Number genes 572.00
Median Mitochondrial counts (Median %) 7.83
Ribosomal counts (Median %) 6.64
Median counts PROT 876.00
Number proteins 58.00

aIg samples

seu_combined_aIg_selected
An object of class Seurat 
18520 features across 4767 samples within 3 assays 
Active assay: SCT.RNA (9220 features, 3000 variable features)
 2 other assays present: RNA, PROT

Table Overview of aIg dataset properties per sample

kable(seu_combined_aIg_selected@meta.data %>% 
        group_by(condition) %>% 
        summarise(`Number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Ribosomal counts (Median %)` = round(median(percent.rb),2),
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  )) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
condition Number of cells Median counts RNA Median Number genes Median Mitochondrial counts (Median %) Ribosomal counts (Median %) Median counts PROT Number proteins
000.aIg.contr 649 1407 977 7.11 6.70 1108 60
002.aIg.contr 927 788 602 7.61 6.76 992 59
004.aIg.contr 511 866 646 7.75 6.02 1043 59
006.aIg.contr 716 1098 804 8.02 6.93 752 54
060.aIg.contr 865 644 497 8.72 6.65 894 57
180.aIg.contr 1099 717 555 7.37 6.79 871 57

Table Overview of aIg dataset properties.

kable(seu_combined_aIg_selected@meta.data %>% 
        summarise(`Number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Ribosomal counts (Median %)` = round(median(percent.rb),2),
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  ) %>%
        t()) %>%

  kable_styling(bootstrap_options = c("striped", "hover"))
Number of cells 4767.00
Median counts RNA 816.00
Median Number genes 616.00
Median Mitochondrial counts (Median %) 7.77
Ribosomal counts (Median %) 6.69
Median counts PROT 917.00
Number proteins 58.00

aIg + ibrutinib samples

seu_combined_ibru_selected
An object of class Seurat 
18520 features across 4673 samples within 3 assays 
Active assay: SCT.RNA (9220 features, 3000 variable features)
 2 other assays present: RNA, PROT

Table Overview of ibru dataset properties per sample

kable(seu_combined_ibru_selected@meta.data %>% 
        group_by(condition) %>% 
        summarise(`Number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Ribosomal counts (Median %)` = round(median(percent.rb),2),
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  )) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
condition Number of cells Median counts RNA Median Number genes Median Mitochondrial counts (Median %) Ribosomal counts (Median %) Median counts PROT Number proteins
000.aIg.contr 649 1407 977 7.11 6.70 1108 60
006.aIg.contr 716 1098 804 8.02 6.93 752 54
006.aIg.ibr 953 498 390 10.41 5.98 776 57
180.aIg.contr 1099 717 555 7.37 6.79 871 57
180.aIg.ibr 1256 740 578 6.31 6.90 810 57

Table Overview of ibru dataset properties.

kable(seu_combined_ibru_selected@meta.data %>% 
        summarise(`Number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Ribosomal counts (Median %)` = round(median(percent.rb),2),
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  ) %>%
        t()) %>%

  kable_styling(bootstrap_options = c("striped", "hover"))
Number of cells 4673.00
Median counts RNA 756.00
Median Number genes 581.00
Median Mitochondrial counts (Median %) 7.68
Ribosomal counts (Median %) 6.67
Median counts PROT 839.00
Number proteins 57.00

sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)

Matrix products: default

locale:
[1] LC_COLLATE=English_Netherlands.1252  LC_CTYPE=English_Netherlands.1252   
[3] LC_MONETARY=English_Netherlands.1252 LC_NUMERIC=C                        
[5] LC_TIME=English_Netherlands.1252    

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

other attached packages:
 [1] png_0.1-7                   forcats_0.5.0              
 [3] clusterProfiler_3.14.3      clusterProfiler.dplyr_0.0.2
 [5] enrichplot_1.6.1            org.Hs.eg.db_3.10.0        
 [7] AnnotationDbi_1.48.0        IRanges_2.20.2             
 [9] S4Vectors_0.24.4            Biobase_2.46.0             
[11] BiocGenerics_0.32.0         magick_2.4.0               
[13] cowplot_1.1.0               ggtext_0.1.0               
[15] ggplotify_0.0.5             ggcorrplot_0.1.3           
[17] ggrepel_0.8.2               ggpubr_0.4.0               
[19] scico_1.2.0                 MOFA2_1.1                  
[21] extrafont_0.17              patchwork_1.0.1            
[23] RColorBrewer_1.1-2          viridis_0.5.1              
[25] viridisLite_0.3.0           ggsci_2.9                  
[27] sctransform_0.3.1           ggthemes_4.2.0             
[29] matrixStats_0.57.0          kableExtra_1.2.1           
[31] gridExtra_2.3               Seurat_3.2.2               
[33] ggplot2_3.3.2               scales_1.1.1               
[35] tidyr_1.1.2                 dplyr_1.0.2                
[37] stringr_1.4.0               workflowr_1.6.1            

loaded via a namespace (and not attached):
  [1] reticulate_1.16       tidyselect_1.1.0      RSQLite_2.2.1        
  [4] htmlwidgets_1.5.2     BiocParallel_1.20.1   Rtsne_0.15           
  [7] munsell_0.5.0         codetools_0.2-16      ica_1.0-2            
 [10] future_1.19.1         miniUI_0.1.1.1        withr_2.3.0          
 [13] GOSemSim_2.12.1       colorspace_1.4-1      highr_0.8            
 [16] knitr_1.30            rstudioapi_0.11       ROCR_1.0-11          
 [19] ggsignif_0.6.0        tensor_1.5            DOSE_3.12.0          
 [22] Rttf2pt1_1.3.8        listenv_0.8.0         labeling_0.4.2       
 [25] git2r_0.27.1          urltools_1.7.3        polyclip_1.10-0      
 [28] farver_2.0.3          bit64_4.0.5           pheatmap_1.0.12      
 [31] rhdf5_2.30.1          rprojroot_1.3-2       vctrs_0.3.4          
 [34] generics_0.0.2        xfun_0.18             R6_2.4.1             
 [37] graphlayouts_0.7.0    rsvd_1.0.3            fgsea_1.12.0         
 [40] spatstat.utils_1.17-0 gridGraphics_0.5-0    DelayedArray_0.12.3  
 [43] promises_1.1.0        ggraph_2.0.3          gtable_0.3.0         
 [46] globals_0.13.1        goftest_1.2-2         tidygraph_1.2.0      
 [49] rlang_0.4.8           splines_3.6.3         rstatix_0.6.0        
 [52] extrafontdb_1.0       lazyeval_0.2.2        europepmc_0.4        
 [55] broom_0.7.1           BiocManager_1.30.10   yaml_2.2.1           
 [58] reshape2_1.4.4        abind_1.4-5           backports_1.1.10     
 [61] httpuv_1.5.2          qvalue_2.18.0         gridtext_0.1.1       
 [64] tools_3.6.3           ellipsis_0.3.1        ggridges_0.5.2       
 [67] Rcpp_1.0.4.6          plyr_1.8.6            progress_1.2.2       
 [70] purrr_0.3.4           prettyunits_1.1.1     rpart_4.1-15         
 [73] deldir_0.1-29         pbapply_1.4-3         zoo_1.8-8            
 [76] haven_2.3.1           cluster_2.1.0         fs_1.4.1             
 [79] magrittr_1.5          data.table_1.13.0     DO.db_2.9            
 [82] openxlsx_4.2.2        triebeard_0.3.0       lmtest_0.9-38        
 [85] RANN_2.6.1            whisker_0.4           fitdistrplus_1.1-1   
 [88] hms_0.5.3             mime_0.9              evaluate_0.14        
 [91] xtable_1.8-4          rio_0.5.16            readxl_1.3.1         
 [94] compiler_3.6.3        tibble_3.0.4          KernSmooth_2.23-16   
 [97] crayon_1.3.4          htmltools_0.5.0       mgcv_1.8-31          
[100] later_1.0.0           DBI_1.1.0             tweenr_1.0.1         
[103] corrplot_0.84         MASS_7.3-53           rappdirs_0.3.1       
[106] Matrix_1.2-18         car_3.0-10            igraph_1.2.6         
[109] pkgconfig_2.0.3       rvcheck_0.1.8         foreign_0.8-75       
[112] plotly_4.9.2.1        xml2_1.3.2            webshot_0.5.2        
[115] rvest_0.3.6           digest_0.6.26         RcppAnnoy_0.0.16     
[118] spatstat.data_1.4-3   fastmatch_1.1-0       rmarkdown_2.4        
[121] cellranger_1.1.0      leiden_0.3.3          uwot_0.1.8           
[124] curl_4.3              shiny_1.5.0           lifecycle_0.2.0      
[127] nlme_3.1-144          jsonlite_1.7.1        Rhdf5lib_1.8.0       
[130] carData_3.0-4         pillar_1.4.6          lattice_0.20-38      
[133] GO.db_3.10.0          fastmap_1.0.1         httr_1.4.2           
[136] survival_3.1-8        glue_1.4.2            zip_2.1.1            
[139] spatstat_1.64-1       bit_4.0.4             ggforce_0.3.2        
[142] stringi_1.4.6         HDF5Array_1.14.4      blob_1.2.1           
[145] memoise_1.1.0         irlba_2.3.3           future.apply_1.6.0