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Figure 4

Supplementary Figure X

Session info

Last updated: 2025-01-06

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Knit directory: paed-inflammation-CITEseq/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/99.0_Figure_4.Rmd) and HTML (docs/99.0_Figure_4.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

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Rmd	0a567aa	Jovana Maksimovic	2025-01-06	wflow_publish("analysis/99.0_Figure_4.Rmd")

Load libraries.

suppressPackageStartupMessages({
 library(SingleCellExperiment)
 library(edgeR)
 library(tidyverse)
 library(ggplot2)
 library(Seurat)
 library(glmGamPoi)
 library(dittoSeq)
 library(here)
 library(clustree)
 library(patchwork)
 library(AnnotationDbi)
 library(org.Hs.eg.db)
 library(glue)
 library(speckle)
 library(tidyHeatmap)
 library(paletteer)
 library(dsb)
 library(ggh4x)
  library(readxl)
})

source(here("code/utility.R"))

Load data

files <- list.files(here("data/C133_Neeland_merged"),
                    pattern = "C133_Neeland_full_clean.*(macrophages|t_cells|other_cells)_annotated_diet.SEU.rds",
                    full.names = TRUE)

seuLst <- lapply(files[2:4], function(f) readRDS(f))

seu <- merge(seuLst[[1]], 
             y = c(seuLst[[2]], 
                   seuLst[[3]]))
seu

An object of class Seurat 
21568 features across 194407 samples within 1 assay 
Active assay: RNA (21568 features, 0 variable features)

             used    (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   12100924   646.3   20376418  1088.3   14090832   752.6
Vcells 1354185555 10331.7 3693781618 28181.4 3551519297 27096.0

Create sample meta data table.

props <- getTransformedProps(clusters = seu$ann_level_3,
                             sample = seu$sample.id, transform="asin")

seu@meta.data %>%
  dplyr::select(sample.id,
                Participant,
                Disease,
                Treatment,
                Severity,
                Group,
                Group_severity,
                Batch, 
                Age, 
                Sex) %>%
     left_join(props$Counts %>% 
                 data.frame %>%
                 group_by(sample) %>%
                 summarise(ncells = sum(Freq)),
               by = c("sample.id" = "sample")) %>%
    distinct() -> info

head(info) %>% knitr::kable()

sample.id	Participant	Disease	Treatment	Severity	Group	Group_severity	Batch	Age	Sex	ncells
sample_33.1	sample_33	CF	treated (ivacaftor)	severe	CF.IVA	CF.IVA.S	1	5.950685	M	2139
sample_25.1	sample_25	CF	untreated	severe	CF.NO_MOD	CF.NO_MOD.S	1	4.910000	F	3272
sample_29.1	sample_29	CF	untreated	severe	CF.NO_MOD	CF.NO_MOD.S	1	5.989041	F	1568
sample_27.1	sample_27	CF	treated (ivacaftor)	mild	CF.IVA	CF.IVA.M	1	4.917808	M	2467
sample_32.1	sample_32	CF	untreated	mild	CF.NO_MOD	CF.NO_MOD.M	1	5.926027	F	2963
sample_26.1	sample_26	CF	untreated	mild	CF.NO_MOD	CF.NO_MOD.M	1	5.049315	M	2040

Prepare figure panels

# HSP+ B, CD4 T-IFN

props <- getTransformedProps(clusters = seu$ann_level_3[!str_detect(seu$ann_level_3, "macro")],
                             sample = seu$sample.id[!str_detect(seu$ann_level_3, "macro")], transform="asin")

props$Proportions %>% data.frame %>%
  left_join(info,
            by = c("sample" = "sample.id")) %>%
  dplyr::filter(Group_severity %in% c("CF.NO_MOD.S", "CF.NO_MOD.M"),
                clusters %in% c("CD4 T-IFN", 
                                "HSP+ B cells")) -> dat

sig_names <- as_labeller(
     c("CD4 T-IFN" = "CD4 T-IFN",
       "HSP+ B cells" = "HSP+ B cells"))

pal <- RColorBrewer::brewer.pal(8, "Accent")[1:2]
names(pal) <- c("CF.NO_MOD.S", "CF.NO_MOD.M")
  
dat %>%
ggplot(aes(x = Group_severity,
                y = Freq,
                colour = Group_severity)) +
  geom_jitter(stat = "identity",
              width = 0.15,
              size = 1) +
  theme_classic() +
  theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.text.y = element_text(7),
          legend.position = "bottom",
          legend.direction = "horizontal",
          strip.text = element_text(size = 8)) +
  labs(x = "Group", y = "Proportion",
       colour = "Group") +
  facet_wrap(~clusters, scales = "free_y", ncol = 4,
             labeller = sig_names) +
  stat_summary(
    geom = "point",
    fun.y = "mean",
    col = "black",
    shape = "_",
    size = 10) +
  scale_color_manual(values = pal) -> p1

p1

seu@meta.data %>% 
    data.frame %>% 
    dplyr::select(ann_level_2) %>%
    dplyr::filter(str_detect(ann_level_2, "macro")) %>%
    group_by(ann_level_2) %>% 
    count() %>%
    janitor::adorn_totals(name = "macrophages") %>%
    arrange(-n) %>%
    dplyr::rename(cell = ann_level_2) -> cell_freq

cell_freq

                   cell      n
            macrophages 165209
         macro-alveolar  52563
            macro-IFI27  24864
              macro-CCL  21246
 macro-monocyte-derived  13461
           macro-APOC2+  13354
            macro-lipid  12452
             macro-IGF1   8229
    macro-proliferating   6821
               macro-MT   4037
     macro-interstitial   3412
                macro-T   2722
              macro-IFN   2048

files <- list.files(here("data/intermediate_objects"),
            pattern = "macro.*CF_samples", 
            full.names = TRUE) 
 
cutoff <- 0.05 
cont_name <- "CF.NO_MOD.SvCF.NO_MOD.M"  
lfc_cutoff <- 0
suffix <- ".CF_samples.fit.rds"
  
get_deg_data(files, cont_name, cell_freq, treat_lfc = lfc_cutoff,
             suffix = suffix) -> dat

Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead.

genes <- c("ARHGEF5",
           "GPD1",
           "ITGA1",
           "SIRPB1",
           "TLCD4",
           "GBP3",
           "CLIC2",
           "LILRB2",
           "MMP14",
           "SLC28A3",
           "SLC46A1",
           "ARPIN",
           "CTSK",
           "VAMP5")

dat %>%
  dplyr::select(gene, cell, logFC) %>%
  distinct() %>%
  dplyr::filter(gene %in% sort(genes)) %>%
  pivot_wider(
    names_from = cell,        # Column whose values become new column names
    values_from = logFC,
    values_fill = list(logFC = NA)) %>%
   arrange(across(all_of(cell_freq$cell[cell_freq$cell %in% .$cell]))) %>%
  column_to_rownames(var = "gene") -> dat_lfc

pal_dt <- c(paletteer::paletteer_d("RColorBrewer::Set1")[2:1], "grey") 

col_fun <- circlize::colorRamp2(seq(0, 100000, length.out = 9), 
                                (RColorBrewer::brewer.pal(9, "PiYG")))
col_split <- c(rep("aggregate", 1), rep("sub-type", ncol(dat_lfc) - 1))

col_lfc_fun <- circlize::colorRamp2(seq(-2, 2, length.out = 3), 
                                    c(pal_dt[1], "white", pal_dt[2]))

ComplexHeatmap::HeatmapAnnotation(df = cell_freq %>%
                                    dplyr::filter(cell %in% colnames(dat_lfc)) %>%
                                    column_to_rownames(var = "cell") %>%
                                    dplyr::rename(`No. cells` = n),
                                  which = "column",
                                  show_annotation_name = FALSE,
                                  col = list(`No. cells` = col_fun),
                                  annotation_legend_param = list(
                                    `No. cells` = list(direction = "vertical",
                                                       labels_gp = grid::gpar(fontsize = 7)))) -> col_ann

ComplexHeatmap::Heatmap(dat_lfc[order(rowSums(dat_lfc, na.rm = TRUE),
                                      decreasing = TRUE),], 
                        name = "logFC",
                        column_split = col_split,
                        column_title = NULL,
                        cluster_rows = FALSE,
                        cluster_columns = FALSE,
                        rect_gp = grid::gpar(col = "white", lwd = 1),
                        row_names_gp = grid::gpar(fontsize = 7),
                        column_names_gp = grid::gpar(fontsize = 8),
                        column_names_rot = 90,
                        top_annotation = col_ann,
                        col = col_lfc_fun,
                        #right_annotation = row_ann,
                        heatmap_legend_param = list(direction = "vertical",
                                                    labels_gp = grid::gpar(fontsize = 7))) -> plot_lfc

ComplexHeatmap::draw(as(list(plot_lfc), "HeatmapList"), 
                     heatmap_legend_side = "right", 
                     annotation_legend_side = "right",
                     merge_legends = TRUE) -> plot_lfc

plot_lfc

bind_rows(lapply(files, function(f){
  deg_results <- readRDS(f)
  lrt <- glmLRT(deg_results$fit, 
                contrast = deg_results$contr[,cont_name])
  tmp <- cbind(summary(decideTests(lrt, p.value = 0.05)) %>% data.frame,
                    cell = unlist(str_split(str_remove(f, suffix), "/"))[8])
  tmp
})) -> dat_deg

bind_rows(lapply(files, function(f){
  deg_results <- readRDS(f)
  lrt <- glmLRT(deg_results$fit, 
                contrast = deg_results$contr[,cont_name])
  tmp <- cbind(summary(decideTests(lrt, p.value = cutoff)) %>% data.frame,
                    cell = unlist(str_split(str_remove(f, suffix), "/"))[8])
  tmp
})) -> dat_deg

dat_deg %>% 
  left_join(cell_freq) -> dat_deg

dat_deg %>%
  dplyr::filter(Var1 != "NotSig") %>%
  ggplot(aes(x = fct_reorder(cell, -n), y = Freq, fill = Var1)) +
  geom_col(position = "dodge") +
  scale_fill_manual(values = pal_dt) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 30,
                                   hjust = 1,
                                   vjust = 1,
                                   size = 7),
        legend.position = "top") +
  geom_text(aes(label = Freq), 
            position = position_dodge(width = 0.9),
            vjust = -0.5,
            size = 2.5) +
  labs(x = "Cell Type",
       y = "No. DEG (FDR < 0.05)",
       fill = "Direction") -> deg_barplot

deg_barplot

Figure 4

layout <- "
ABB
CCC
"

wrap_elements(p1 + theme(legend.direction = "vertical")) +
(wrap_elements(deg_barplot + theme(axis.title.x = element_blank(),
                                   legend.text = element_text(size = 8)))) +
  #wrap_elements(volc_plot + theme(strip.text = element_text(size = 7))) +
  wrap_elements(grid::grid.grabExpr(ComplexHeatmap::draw(plot_lfc))) +
  plot_layout(design = layout) +
  plot_annotation(tag_levels = "A")  &
  theme(plot.tag = element_text(size = 16,
                                face = "bold",
                                family = "arial"))

Supplementary Figure X

dat %>%
  dplyr::select(gene, cell, logFC) %>%
  distinct() %>%
  pivot_wider(
    names_from = cell,        # Column whose values become new column names
    values_from = logFC,
    values_fill = list(logFC = NA)) %>%
   arrange(across(all_of(cell_freq$cell[cell_freq$cell %in% .$cell]))) %>%
  column_to_rownames(var = "gene") -> dat_lfc_supp

col_fun <- circlize::colorRamp2(seq(0, 100000, length.out = 9), 
                                (RColorBrewer::brewer.pal(9, "PiYG")))
col_split <- c(rep("aggregate", 1), rep("sub-type", ncol(dat_lfc_supp) - 1))

col_lfc_fun <- circlize::colorRamp2(seq(-2, 2, length.out = 3), 
                                    c(pal_dt[1], "white", pal_dt[2]))

ComplexHeatmap::HeatmapAnnotation(df = cell_freq %>%
                                    dplyr::filter(cell %in% colnames(dat_lfc_supp)) %>%
                                    column_to_rownames(var = "cell") %>%
                                    dplyr::rename(`No. cells` = n),
                                  which = "column",
                                  show_annotation_name = FALSE,
                                  col = list(`No. cells` = col_fun),
                                  annotation_legend_param = list(
                                    `No. cells` = list(direction = "vertical",
                                                       labels_gp = grid::gpar(fontsize = 7)))) -> col_ann

ComplexHeatmap::HeatmapAnnotation(df = data.frame(multiple = (rowSums(!is.na(dat_lfc_supp)) > 1)),
                                  which = "row",
                                  col = list(multiple = c("FALSE" = "#fdcce5","TRUE" = "#8bd3c7")),
                                  annotation_legend_param = list(
                                    multiple = list(direction = "vertical",
                                                    ncol = 1))) -> row_ann

ComplexHeatmap::Heatmap(dat_lfc_supp, 
                        name = "logFC",
                        column_split = col_split,
                        column_title = NULL,
                        cluster_rows = FALSE,
                        cluster_columns = FALSE,
                        rect_gp = grid::gpar(col = "white", lwd = 1),
                        row_names_gp = grid::gpar(fontsize = 7),
                        column_names_gp = grid::gpar(fontsize = 8),
                        column_names_rot = 90,
                        top_annotation = col_ann,
                        col = col_lfc_fun,
                        right_annotation = row_ann,
                        heatmap_legend_param = list(direction = "vertical",
                                                    labels_gp = grid::gpar(fontsize = 7))) -> plot_lfc

ComplexHeatmap::draw(as(list(plot_lfc), "HeatmapList"), 
                     heatmap_legend_side = "right", 
                     annotation_legend_side = "right",
                     merge_legends = TRUE) -> plot_lfc_supp

plot_lfc_supp

get_deg_data(files, cont_name, cell_freq, treat_lfc = lfc_cutoff,
             suffix = suffix, cutoff = 1) -> dat_all

Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead. 
Zero log2-FC threshold detected. Switch to glmLRT() instead.

dat_all %>% 
  left_join(cell_freq) %>%
  mutate(Direction = as.factor(ifelse(sig == -1, "Down",
                                      ifelse(sig == 1, "Up", "N.S."))),
         cell = fct_reorder(cell, -n)) -> dat_all

ggplot(dat_all, aes(x = logFC, y = -log10(FDR), colour = Direction)) + 
  geom_point(size = 0.5) + 
  facet_wrap(~cell, ncol = 4) + 
  theme_classic() +
  scale_color_manual(values = pal_dt[c(1,3,2)]) +
  ggrepel::geom_text_repel(data = dat_all[dat_all$sig != 0,], 
                           aes(label = gene), size = 2) -> volc_plot

volc_plot

Session info

sessionInfo()

R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 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_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

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

other attached packages:
 [1] readxl_1.4.3                ggh4x_0.2.8                
 [3] dsb_1.0.3                   paletteer_1.6.0            
 [5] tidyHeatmap_1.8.1           speckle_1.2.0              
 [7] glue_1.7.0                  org.Hs.eg.db_3.18.0        
 [9] AnnotationDbi_1.64.1        patchwork_1.2.0            
[11] clustree_0.5.1              ggraph_2.2.0               
[13] here_1.0.1                  dittoSeq_1.14.2            
[15] glmGamPoi_1.14.3            SeuratObject_4.1.4         
[17] Seurat_4.4.0                lubridate_1.9.3            
[19] forcats_1.0.0               stringr_1.5.1              
[21] dplyr_1.1.4                 purrr_1.0.2                
[23] readr_2.1.5                 tidyr_1.3.1                
[25] tibble_3.2.1                ggplot2_3.5.0              
[27] tidyverse_2.0.0             edgeR_4.0.15               
[29] limma_3.58.1                SingleCellExperiment_1.24.0
[31] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[33] GenomicRanges_1.54.1        GenomeInfoDb_1.38.6        
[35] IRanges_2.36.0              S4Vectors_0.40.2           
[37] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[39] matrixStats_1.2.0           workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.3                spatstat.sparse_3.0-3   bitops_1.0-7           
  [4] httr_1.4.7              RColorBrewer_1.1-3      doParallel_1.0.17      
  [7] tools_4.3.3             sctransform_0.4.1       utf8_1.2.4             
 [10] R6_2.5.1                lazyeval_0.2.2          uwot_0.1.16            
 [13] GetoptLong_1.0.5        withr_3.0.0             sp_2.1-3               
 [16] gridExtra_2.3           progressr_0.14.0        cli_3.6.2              
 [19] Cairo_1.6-2             spatstat.explore_3.2-6  prismatic_1.1.1        
 [22] labeling_0.4.3          sass_0.4.8              spatstat.data_3.0-4    
 [25] ggridges_0.5.6          pbapply_1.7-2           parallelly_1.37.0      
 [28] rstudioapi_0.15.0       RSQLite_2.3.5           generics_0.1.3         
 [31] shape_1.4.6             ica_1.0-3               spatstat.random_3.2-2  
 [34] dendextend_1.17.1       Matrix_1.6-5            fansi_1.0.6            
 [37] abind_1.4-5             lifecycle_1.0.4         whisker_0.4.1          
 [40] yaml_2.3.8              snakecase_0.11.1        SparseArray_1.2.4      
 [43] Rtsne_0.17              grid_4.3.3              blob_1.2.4             
 [46] promises_1.2.1          crayon_1.5.2            miniUI_0.1.1.1         
 [49] lattice_0.22-5          cowplot_1.1.3           KEGGREST_1.42.0        
 [52] pillar_1.9.0            knitr_1.45              ComplexHeatmap_2.18.0  
 [55] rjson_0.2.21            future.apply_1.11.1     codetools_0.2-19       
 [58] leiden_0.4.3.1          getPass_0.2-4           data.table_1.15.0      
 [61] vctrs_0.6.5             png_0.1-8               cellranger_1.1.0       
 [64] gtable_0.3.4            rematch2_2.1.2          cachem_1.0.8           
 [67] xfun_0.42               S4Arrays_1.2.0          mime_0.12              
 [70] tidygraph_1.3.1         survival_3.7-0          pheatmap_1.0.12        
 [73] iterators_1.0.14        statmod_1.5.0           ellipsis_0.3.2         
 [76] fitdistrplus_1.1-11     ROCR_1.0-11             nlme_3.1-164           
 [79] bit64_4.0.5             RcppAnnoy_0.0.22        rprojroot_2.0.4        
 [82] bslib_0.6.1             irlba_2.3.5.1           KernSmooth_2.23-24     
 [85] colorspace_2.1-0        DBI_1.2.1               tidyselect_1.2.0       
 [88] processx_3.8.3          bit_4.0.5               compiler_4.3.3         
 [91] git2r_0.33.0            DelayedArray_0.28.0     plotly_4.10.4          
 [94] scales_1.3.0            lmtest_0.9-40           callr_3.7.3            
 [97] digest_0.6.34           goftest_1.2-3           spatstat.utils_3.0-4   
[100] rmarkdown_2.25          XVector_0.42.0          htmltools_0.5.7        
[103] pkgconfig_2.0.3         highr_0.10              fastmap_1.1.1          
[106] rlang_1.1.3             GlobalOptions_0.1.2     htmlwidgets_1.6.4      
[109] shiny_1.8.0             farver_2.1.1            jquerylib_0.1.4        
[112] zoo_1.8-12              jsonlite_1.8.8          mclust_6.1             
[115] RCurl_1.98-1.14         magrittr_2.0.3          GenomeInfoDbData_1.2.11
[118] munsell_0.5.0           Rcpp_1.0.12             viridis_0.6.5          
[121] reticulate_1.35.0       stringi_1.8.3           zlibbioc_1.48.0        
[124] MASS_7.3-60.0.1         plyr_1.8.9              parallel_4.3.3         
[127] listenv_0.9.1           ggrepel_0.9.5           deldir_2.0-2           
[130] Biostrings_2.70.2       graphlayouts_1.1.0      splines_4.3.3          
[133] tensor_1.5              hms_1.1.3               circlize_0.4.15        
[136] locfit_1.5-9.8          ps_1.7.6                igraph_2.0.1.1         
[139] spatstat.geom_3.2-8     reshape2_1.4.4          evaluate_0.23          
[142] renv_1.0.3              BiocManager_1.30.22     tzdb_0.4.0             
[145] foreach_1.5.2           tweenr_2.0.3            httpuv_1.6.14          
[148] RANN_2.6.1              polyclip_1.10-6         future_1.33.1          
[151] clue_0.3-65             scattermore_1.2         ggforce_0.4.2          
[154] janitor_2.2.0           xtable_1.8-4            later_1.3.2            
[157] viridisLite_0.4.2       memoise_2.0.1           cluster_2.1.6          
[160] timechange_0.3.0        globals_0.16.2

Figure 4

Jovana Maksimovic

January 06, 2025

Load data

Prepare figure panels

Figure 4

Supplementary Figure X

Session info