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Load libraries

Load Data

ambient <- "_decontx"
out <- here("data",
            "C133_Neeland_merged",
            glue("C133_Neeland_full_clean{ambient}_integrated_clustered_mapped_other_cells.ADT.SEU.rds"))
seuInt <- readRDS(file = out)

seuInt

An object of class Seurat 
41729 features across 13687 samples within 4 assays 
Active assay: integrated (3000 features, 0 variable features)
 3 other assays present: RNA, ADT, SCT
 2 dimensional reductions calculated: pca, umap

Update group labels

seuInt@meta.data %>%
  data.frame %>%
  mutate(Group = ifelse(str_detect(Treatment, "ivacaftor"),
                        "CF.IVA",
                        ifelse(str_detect(Treatment, "orkambi"),
                               "CF.LUMA_IVA",
                               ifelse(Treatment == "untreated",
                                      "CF.NO_MOD",
                                      "NON_CF.CTRL"))),
         Group_severity = ifelse(!Group %in% "NON_CF.CTRL", 
                                 paste(Group, 
                                       toupper(substr(Severity, 1, 1)),
                                       sep = "."), 
                                 Group),
         Severity = tolower(Severity),
         Participant = strsplit2(sample.id, ".", fixed = TRUE)[,1]) -> seuInt@meta.data

Sub-cluster labelling

Load manual annotations

labels <- read_excel(here("data",
                          "cluster_annotations",
                          "others_ambientRNAremoval_21.03.24.xlsx"),
                     skip = 1)


# set selected cluster resolution
grp <- "wsnn_res.0.6"
seuInt@meta.data %>%
  rownames_to_column(var = "cell") %>%
  left_join(labels %>%
              mutate(Cluster = as.factor(Cluster),
                     ann_level_3 = as.factor(ann_level_3),
                     ann_level_2 = as.factor(ann_level_2),
                     ann_level_1 = as.factor(ann_level_1)),
            by = c("wsnn_res.0.6" = "Cluster")) %>%
  column_to_rownames(var = "cell") -> seuInt@meta.data

seuInt <- subset(seuInt, cells = which(seuInt$ann_level_3 != "unknown"))
seuInt$ann_level_3 <- fct_drop(seuInt$ann_level_3)
seuInt$ann_level_2 <- fct_drop(seuInt$ann_level_2)
seuInt$ann_level_1 <- fct_drop(seuInt$ann_level_1)
seuInt

An object of class Seurat 
41729 features across 13687 samples within 4 assays 
Active assay: integrated (3000 features, 0 variable features)
 3 other assays present: RNA, ADT, SCT
 2 dimensional reductions calculated: pca, umap

Visualise annotations

options(ggrepel.max.overlaps = Inf)
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE, 
        label.size = 3, group.by = grp) + 
  NoLegend() -> p1

cluster_pal <- "ggsci::category20_d3"
DimPlot(seuInt, reduction = 'umap', label = FALSE, group.by = "ann_level_1") + 
  scale_color_paletteer_d(cluster_pal) +
  theme(text = element_text(size = 9),
        axis.text = element_blank(),
        axis.ticks = element_blank()) +
  NoLegend() -> p2

DimPlot(seuInt, reduction = 'umap', label = FALSE, group.by = "ann_level_3") + 
  scale_color_paletteer_d(cluster_pal) +
  theme(text = element_text(size = 9),
        axis.text = element_blank(),
        axis.ticks = element_blank()) +
  NoLegend() -> p3

p1

LabelClusters(p2, id = "ann_level_1", repel = TRUE, 
              size = 2.5, box = TRUE, fontfamily = "arial")

LabelClusters(p3, id = "ann_level_3", repel = TRUE, 
              size = 2.5, box = TRUE, fontfamily = "arial")

No. cells per cluster

seuInt@meta.data %>%
  ggplot(aes(x = ann_level_1, fill = ann_level_1)) +
  geom_bar() +
  geom_text(aes(label = after_stat(count)), stat = "count",
            vjust = -0.5, colour = "black", size = 2) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  NoLegend() +
  scale_fill_paletteer_d(cluster_pal)

Version	Author	Date
0b24220	Jovana Maksimovic	2024-07-05

seuInt@meta.data %>%
  ggplot(aes(x = ann_level_3, fill = ann_level_3)) +
  geom_bar() +
  geom_text(aes(label = after_stat(count)), stat = "count",
            vjust = -0.5, colour = "black", size = 2) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  NoLegend() +
  scale_fill_paletteer_d(cluster_pal)

Version	Author	Date
0b24220	Jovana Maksimovic	2024-07-05

RNA marker gene analysis

Adapted from Dr. Belinda Phipson’s work for [@Sim2021-cg].

Test for marker genes using `limma`

# limma-trend for DE
Idents(seuInt) <- "ann_level_3"

out <- here("data",
            "C133_Neeland_merged",
            glue("C133_Neeland_full_clean{ambient}_other_cells_logcounts.SEU.rds"))

if(!file.exists(out)){
  logcounts <- normCounts(DGEList(as.matrix(seuInt[["RNA"]]@counts)),
                          log = TRUE, prior.count = 0.5)
  entrez <- AnnotationDbi::mapIds(org.Hs.eg.db,
                                  keys = rownames(logcounts),
                                  column = c("ENTREZID"),
                                  keytype = "SYMBOL",
                                  multiVals = "first")
  # remove genes without entrez IDs as these are difficult to interpret biologically
  logcounts <- logcounts[!is.na(entrez),]
  saveRDS(logcounts, file = out)
  
} else {
  logcounts <- readRDS(out)  
}

maxclust <- length(levels(Idents(seuInt))) - 1

clustgrp <- seuInt$ann_level_3
clustgrp <- factor(clustgrp)
donor <- factor(seuInt$sample.id)
batch <- factor(seuInt$Batch)

design <- model.matrix(~ 0 + clustgrp + donor)
colnames(design)[1:(length(levels(clustgrp)))] <- levels(clustgrp)

# Create contrast matrix
mycont <- matrix(NA, ncol = length(levels(clustgrp)),
                 nrow = length(levels(clustgrp)))
rownames(mycont) <- colnames(mycont) <- levels(clustgrp)
diag(mycont) <- 1
mycont[upper.tri(mycont)] <- -1/(length(levels(factor(clustgrp))) - 1)
mycont[lower.tri(mycont)] <- -1/(length(levels(factor(clustgrp))) - 1)

# Fill out remaining rows with 0s
zero.rows <- matrix(0, ncol = length(levels(clustgrp)),
                    nrow = (ncol(design) - length(levels(clustgrp))))
fullcont <- rbind(mycont, zero.rows)
rownames(fullcont) <- colnames(design)

fit <- lmFit(logcounts, design)

fit.cont <- contrasts.fit(fit, contrasts = fullcont)
fit.cont <- eBayes(fit.cont, trend = TRUE, robust = TRUE)

summary(decideTests(fit.cont))

       B cells  cDC1  cDC2 ciliated epithelial cells dividing innate cells
Down      6533  2976  6055                      2723                   835
NotSig    6761 11885  7404                      4657                 12155
Up        2516   949  2351                      8430                  2820
       HSP+ B cells mast cells migratory DC monocytes neutrophil-like
Down            837       1928         6138      6409            6299
NotSig        14271      12253         8254      6988            8170
Up              702       1629         1418      2413            1341
       plasma B cells plasmacytoid DC secretory epithelial cells
Down             1295            3136                       3063
NotSig          13871            9003                       9094
Up                644            3671                       3653

Test relative to a threshold (TREAT).

tr <- treat(fit.cont, lfc = 0.5)
dt <- decideTests(tr)
summary(dt)

       B cells  cDC1  cDC2 ciliated epithelial cells dividing innate cells
Down        81    16    10                       204                     8
NotSig   15602 15643 15658                     14666                 15657
Up         127   151   142                       940                   145
       HSP+ B cells mast cells migratory DC monocytes neutrophil-like
Down             27        186           32        14              23
NotSig        15703      15581        15575     15595           15625
Up               80         43          203       201             162
       plasma B cells plasmacytoid DC secretory epithelial cells
Down              105              51                        165
NotSig          15634           15577                      15494
Up                 71             182                        151

Mean-difference (MD) plots per cluster.

par(mfrow=c(4,3))
par(mar=c(2,3,1,2))

for(i in 1:ncol(mycont)){
  plotMD(tr, coef = i, status = dt[,i], hl.cex = 0.5)
  abline(h = 0, col = "lightgrey")
  lines(lowess(tr$Amean, tr$coefficients[,i]), lwd = 1.5, col = 4)
}

`limma` marker gene dotplot

DefaultAssay(seuInt) <- "RNA"
contnames <- colnames(mycont)
top_markers <- NULL
n_markers <- 5

for(i in 1:ncol(mycont)){
  top <- topTreat(tr, coef = i, n = Inf)
  top <- top[top$logFC > 0, ]
  top_markers <- c(top_markers, 
                   setNames(rownames(top)[1:n_markers], 
                            rep(contnames[i], n_markers)))
}

top_markers <- top_markers[!is.na(top_markers)]
d <- duplicated(top_markers)
top_markers <- top_markers[!d]
geneCols <- paletteer_d(cluster_pal)[factor(names(top_markers))]

strip <- strip_themed(background_x = elem_list_rect(fill = unique(geneCols)))
DotPlot(seuInt,
        features = unname(top_markers),
        group.by = "ann_level_3",
        cols = c("azure1", "blueviolet"),
        dot.scale = 2.5,
        assay = "SCT") +
  FontSize(x.text = 9, y.text = 9) +
  labs(y = element_blank(), x = element_blank()) +
  facet_grid2(~names(top_markers), 
              scales = "free_x", 
              space = "free_x", 
              strip = strip) +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   vjust = 0.5),
        legend.text = element_text(size = 8),
        legend.title = element_text(size = 9),
        strip.text = element_text(size = 0),
        text = element_text(family = "arial"),
        axis.ticks = element_blank(),
        axis.line = element_blank(),
        panel.spacing = unit(2, "mm"))

Test for marker genes using `Seurat`

DefaultAssay(seuInt) <- "RNA"
Idents(seuInt) <- "ann_level_3"

out <- here("data/cluster_annotations/seurat_markers_other_cells.rds")

if(!file.exists(out)){
  # restrict genes to same set as for limma analysis
  markers <- FindAllMarkers(seuInt, only.pos = TRUE, 
                            features = rownames(logcounts))
  saveRDS(markers, file = out)
  
} else {
  markers <- readRDS(out)
  
}

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

	avg_log2FC	pct.1	pct.2	cluster	gene
FCRL5	54.84147	0.409	0.016	B cells	FCRL5
CD48	41.57641	0.632	0.276	B cells	CD48
TTN	16.80242	0.214	0.013	B cells	TTN
FCRL2	14.15510	0.254	0.008	B cells	FCRL2
RALGPS2	11.58341	0.506	0.085	B cells	RALGPS2
FCRL3	11.14185	0.211	0.003	B cells	FCRL3

`Seurat` marker gene dotplot

DefaultAssay(seuInt) <- "RNA"

maxGenes <- 5
markers %>%
  group_by(cluster) %>%
  top_n(n = maxGenes, wt = avg_log2FC) -> top5

sig <- top5$gene
d <- duplicated(sig)
geneCols <- paletteer_d(cluster_pal)[top5$cluster][!d]

strip <- strip_themed(background_x = elem_list_rect(fill = unique(geneCols)))
DotPlot(seuInt,
        features = sig[!d],
        group.by = "ann_level_3",
        cols = c("azure1", "blueviolet"),
        dot.scale = 2.5,
        assay = "SCT") +
  FontSize(x.text = 9, y.text = 9) +
  labs(y = element_blank(), x = element_blank()) +
  facet_grid2(~top5$cluster[!d], 
              scales = "free_x", 
              space = "free_x", 
              strip = strip) +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   vjust = 0.5),
        legend.text = element_text(size = 8),
        legend.title = element_text(size = 9),
        strip.text = element_text(size = 0),
        text = element_text(family = "arial"),
        axis.ticks = element_blank(),
        axis.line = element_blank(),
        panel.spacing = unit(2, "mm"))

Version	Author	Date
0b24220	Jovana Maksimovic	2024-07-05

Visualise ADTs

Make data frame of proteins, clusters, expression levels.

out <- here("data",
            "C133_Neeland_merged",
            glue("C133_Neeland_full_clean{ambient}_other_cells_adt_dsb.SEU.rds"))

if(!file.exists(out)){
  read_csv(file = here("data",
                       "C133_Neeland_batch1",
                       "data",
                       "sample_sheets",
                       "ADT_features.csv")) -> adt_data
  
  pattern <- "anti-human/mouse |anti-human/mouse/rat |anti-mouse/human |anti-human "
  adt_data$name <- gsub(pattern, "", adt_data$name)
  adt <- seuInt[["ADT"]]@counts
  if(all(rownames(seuInt[["ADT"]]@counts) == adt_data$id)) rownames(adt) <- adt_data$name
  adt_data %>%
  dplyr::filter(grepl("[Ii]sotype", name)) %>%
  pull(name) -> isotype_controls
  
  # normalise ADT using DSB normalisation
  adt_dsb <- ModelNegativeADTnorm(cell_protein_matrix = adt,
                                  denoise.counts = TRUE,
                                  use.isotype.control = TRUE,
                                  isotype.control.name.vec = isotype_controls)
  saveRDS(adt_dsb, file = out)
  
} else {
  adt_dsb <- readRDS(out)
  
}

#seuInt[["ADT.dsb"]] <- NULL
m <- match(colnames(seuInt), colnames(adt_dsb)) # remove cells not present in Seurat obj
seuInt[["ADT.dsb"]] <- CreateAssayObject(data = adt_dsb[,m])

ADTs <- read_csv(file = here("data",
                       "Proteins_other_22.04.22.csv"))
pattern <- "anti-human/mouse |anti-human/mouse/rat |anti-mouse/human |anti-human "
ADTs$Description <- gsub(pattern, "", ADTs$Description)

DotPlot(seuInt,
        features = ADTs$Description,
        group.by = "ann_level_3",
        cols = c("azure1", "blueviolet"),
        dot.scale = 2.5,
        assay = "ADT.dsb") +
  FontSize(x.text = 9, y.text = 9) +
  labs(y = element_blank(), x = element_blank()) +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   vjust = 0.5),
        legend.text = element_text(size = 8),
        legend.title = element_text(size = 9),
        strip.text = element_text(size = 0),
        text = element_text(family = "arial"),
        axis.ticks = element_blank(),
        axis.line = element_blank(),
        panel.spacing = unit(2, "mm"))

Version	Author	Date
0b24220	Jovana Maksimovic	2024-07-05

Save data

out <- here("data",
            "C133_Neeland_merged",
            glue("C133_Neeland_full_clean{ambient}_other_cells_annotated_diet.SEU.rds"))
if(!file.exists(out)){
  DefaultAssay(seuInt) <- "RNA"
  saveRDS(DietSeurat(seuInt, assays = "RNA"), out)
}

out <- here("data",
            "C133_Neeland_merged",
            glue("C133_Neeland_full_clean{ambient}_other_cells_annotated_full.SEU.rds"))
if(!file.exists(out)){
  DefaultAssay(seuInt) <- "RNA"
  saveRDS(seuInt, out)
}

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

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22        splines_4.3.3           later_1.3.2            
  [4] prismatic_1.1.1         bitops_1.0-7            cellranger_1.1.0       
  [7] polyclip_1.10-6         lifecycle_1.0.4         doParallel_1.0.17      
 [10] rprojroot_2.0.4         vroom_1.6.5             globals_0.16.2         
 [13] processx_3.8.3          lattice_0.22-5          MASS_7.3-60.0.1        
 [16] dendextend_1.17.1       magrittr_2.0.3          plotly_4.10.4          
 [19] sass_0.4.8              rmarkdown_2.25          jquerylib_0.1.4        
 [22] yaml_2.3.8              httpuv_1.6.14           sctransform_0.4.1      
 [25] sp_2.1-3                spatstat.sparse_3.0-3   reticulate_1.35.0      
 [28] DBI_1.2.1               cowplot_1.1.3           pbapply_1.7-2          
 [31] RColorBrewer_1.1-3      abind_1.4-5             zlibbioc_1.48.0        
 [34] Rtsne_0.17              RCurl_1.98-1.14         git2r_0.33.0           
 [37] circlize_0.4.15         GenomeInfoDbData_1.2.11 ggrepel_0.9.5          
 [40] irlba_2.3.5.1           listenv_0.9.1           spatstat.utils_3.0-4   
 [43] pheatmap_1.0.12         goftest_1.2-3           spatstat.random_3.2-2  
 [46] fitdistrplus_1.1-11     parallelly_1.37.0       leiden_0.4.3.1         
 [49] codetools_0.2-19        DelayedArray_0.28.0     shape_1.4.6            
 [52] tidyselect_1.2.0        farver_2.1.1            viridis_0.6.5          
 [55] spatstat.explore_3.2-6  jsonlite_1.8.8          GetoptLong_1.0.5       
 [58] ellipsis_0.3.2          progressr_0.14.0        iterators_1.0.14       
 [61] ggridges_0.5.6          survival_3.7-0          foreach_1.5.2          
 [64] tools_4.3.3             ica_1.0-3               Rcpp_1.0.12            
 [67] gridExtra_2.3           SparseArray_1.2.4       xfun_0.42              
 [70] withr_3.0.0             BiocManager_1.30.22     fastmap_1.1.1          
 [73] fansi_1.0.6             callr_3.7.3             digest_0.6.34          
 [76] timechange_0.3.0        R6_2.5.1                mime_0.12              
 [79] colorspace_2.1-0        scattermore_1.2         tensor_1.5             
 [82] RSQLite_2.3.5           spatstat.data_3.0-4     utf8_1.2.4             
 [85] generics_0.1.3          renv_1.0.3              data.table_1.15.0      
 [88] httr_1.4.7              htmlwidgets_1.6.4       S4Arrays_1.2.0         
 [91] whisker_0.4.1           uwot_0.1.16             pkgconfig_2.0.3        
 [94] gtable_0.3.4            blob_1.2.4              ComplexHeatmap_2.18.0  
 [97] lmtest_0.9-40           XVector_0.42.0          htmltools_0.5.7        
[100] clue_0.3-65             scales_1.3.0            png_0.1-8              
[103] knitr_1.45              rstudioapi_0.15.0       rjson_0.2.21           
[106] tzdb_0.4.0              reshape2_1.4.4          nlme_3.1-164           
[109] GlobalOptions_0.1.2     cachem_1.0.8            zoo_1.8-12             
[112] KernSmooth_2.23-24      parallel_4.3.3          miniUI_0.1.1.1         
[115] pillar_1.9.0            grid_4.3.3              vctrs_0.6.5            
[118] RANN_2.6.1              promises_1.2.1          xtable_1.8-4           
[121] cluster_2.1.6           evaluate_0.23           cli_3.6.2              
[124] locfit_1.5-9.8          compiler_4.3.3          rlang_1.1.3            
[127] crayon_1.5.2            future.apply_1.11.1     labeling_0.4.3         
[130] mclust_6.1              rematch2_2.1.2          ps_1.7.6               
[133] getPass_0.2-4           plyr_1.8.9              fs_1.6.3               
[136] stringi_1.8.3           viridisLite_0.4.2       deldir_2.0-2           
[139] Biostrings_2.70.2       munsell_0.5.0           lazyeval_0.2.2         
[142] spatstat.geom_3.2-8     Matrix_1.6-5            hms_1.1.3              
[145] bit64_4.0.5             future_1.33.1           KEGGREST_1.42.0        
[148] statmod_1.5.0           shiny_1.8.0             highr_0.10             
[151] ROCR_1.0-11             memoise_2.0.1           igraph_2.0.1.1         
[154] bslib_0.6.1             bit_4.0.5

Annotate Other Cells clusters

Jovana Maksimovic

September 10, 2024

Load libraries

Load Data

Update group labels

Sub-cluster labelling

Load manual annotations

Visualise annotations

No. cells per cluster

RNA marker gene analysis

Test for marker genes using `limma`

`limma` marker gene dotplot

Test for marker genes using `Seurat`

`Seurat` marker gene dotplot

Visualise ADTs

Save data

Session info

Annotate Other Cells clusters

Jovana Maksimovic

September 10, 2024

Load libraries

Load Data

Update group labels

Sub-cluster labelling

Load manual annotations

Visualise annotations

No. cells per cluster

RNA marker gene analysis

Test for marker genes using limma

limma marker gene dotplot

Test for marker genes using Seurat

Seurat marker gene dotplot

Visualise ADTs

Save data

Session info

Test for marker genes using `limma`

`limma` marker gene dotplot

Test for marker genes using `Seurat`

`Seurat` marker gene dotplot