- Load data
- Session info
Integrate all cells
Jovana Maksimovic
October 07, 2024
Last updated: 2024-10-07
Checks: 7 0
Knit directory: paed-inflammation-CITEseq/
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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)
})Load data
files <- list.files(here("data/C133_Neeland_merged"),
pattern = "C133_Neeland_full_clean.*(macrophages|t_cells|other_cells)_annotated_full.SEU.rds",
full.names = TRUE)
seuLst <- lapply(files[2:4], function(f) readRDS(f))
adt_names <- rownames(seuLst[[1]][["ADT"]]@counts)
seuLst <- lapply(seuLst, function(s){
DefaultAssay(s) <- "ADT"
if(!all(rownames(s) == adt_names)){
adt_counts <- s[["ADT"]]@counts
rownames(adt_counts) <- adt_names
CreateSeuratObject(counts = adt_counts,
assay = "ADT",
meta.data = s@meta.data)
} else {
DietSeurat(s, assays = "ADT", dimreducs = NULL)
}
})
seuADT <- merge(seuLst[[1]],
y = c(seuLst[[2]],
seuLst[[3]]))
seuADT <- seuADT[, seuADT$Batch != 0]
seuADTAn object of class Seurat
163 features across 168859 samples within 1 assay
Active assay: ADT (163 features, 0 variable features)Visualise ADTs Make data frame of proteins, clusters, expression levels.
out <- here("data",
"C133_Neeland_merged",
glue("C133_Neeland_full_clean_all_cells_dsb.ADT.rds"))
read_csv(file = here("data",
"C133_Neeland_batch1",
"data",
"sample_sheets",
"ADT_features.csv")) -> adt_data
pattern <- "anti-human/mouse |anti-human/mouse/rat |anti-mouse/human "
adt_data$name <- gsub(pattern, "", adt_data$name)
if(!file.exists(out)){
adt_data %>%
dplyr::filter(grepl("[Ii]sotype", name)) %>%
pull(name) -> isotype_controls
# normalise ADT using DSB normalisation
adt_dsb <- ModelNegativeADTnorm(cell_protein_matrix = seuADT[["ADT"]]@counts,
denoise.counts = TRUE,
use.isotype.control = TRUE,
isotype.control.name.vec = isotype_controls)
saveRDS(adt_dsb, file = out)
} else {
adt_dsb <- readRDS(out)
}
seuADT[["ADT"]]@data <- adt_dsb
seuADTAn object of class Seurat
163 features across 168859 samples within 1 assay
Active assay: ADT (163 features, 0 variable features)ADTs <- read_csv(file = here("data",
"Proteins_macs_22.04.22.csv"))
pattern <- "anti-human/mouse |anti-human/mouse/rat |anti-mouse/human |anti-human "
ADTs$Description <- gsub(pattern, "", ADTs$Description)
seuADT@meta.data %>%
dplyr::filter(ann_level_1 == "macrophages") %>%
dplyr::select(ann_level_3) %>%
rownames_to_column(var = "cell") %>%
inner_join(as.data.frame(t(seuADT[["ADT"]]@data)) %>%
rownames_to_column(var = "cell")) %>%
pivot_longer(c(-cell, -ann_level_3),
names_to = "ADT",
values_to = "Expression") %>%
dplyr::group_by(ann_level_3, ADT) %>%
dplyr::summarize(Expression = mean(Expression)) %>%
ungroup() %>%
dplyr::filter(ADT %in% ADTs$Description) -> dat
plot(density(dat$Expression))
dat %>%
dplyr::rename("Protein" = "ADT",
"DSB Exp." = "Expression",
"Cell type" = "ann_level_3") %>%
tidyHeatmap::heatmap(
.column = Protein,
.row = `Cell type`,
.value = `DSB Exp.`,
scale = "none",
rect_gp = grid::gpar(col = "white", lwd = 1),
show_row_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE,
column_names_gp = grid::gpar(fontsize = 8, fontfamily = "arial"),
column_title_gp = grid::gpar(fontsize = 10, fontfamily = "arial"),
row_names_gp = grid::gpar(fontsize = 8, fontfamily = "arial"),
row_title_gp = grid::gpar(fontsize = 10, fontfamily = "arial"),
column_title_side = "top",
palette_value = circlize::colorRamp2(seq(-0.5, 2.5, length.out = 256),
viridis::magma(256)),
heatmap_legend_param = list(direction = "vertical")) %>%
add_tile(`Cell type`, show_legend = FALSE,
show_annotation_name = FALSE,
palette = paletteer_d("ggsci::category20_d3",
length(unique(dat$ann_level_3)))) %>%
as_ComplexHeatmap() -> sf1a
sf1a
ADTs <- read_csv(file = here("data",
"Proteins_T-NK_22.04.22.csv"))
pattern <- "anti-human/mouse |anti-human/mouse/rat |anti-mouse/human |anti-human "
ADTs$Description <- gsub(pattern, "", ADTs$Description)
seuADT@meta.data %>%
dplyr::filter(ann_level_1 %in% unique(seuLst[[2]]$ann_level_1)) %>%
dplyr::select(ann_level_3) %>%
rownames_to_column(var = "cell") %>%
inner_join(as.data.frame(t(seuADT[["ADT"]]@data)) %>%
rownames_to_column(var = "cell")) %>%
pivot_longer(c(-cell, -ann_level_3),
names_to = "ADT",
values_to = "Expression") %>%
dplyr::group_by(ann_level_3, ADT) %>%
dplyr::summarize(Expression = mean(Expression)) %>%
ungroup() %>%
dplyr::filter(ADT %in% ADTs$Description) -> dat
plot(density(dat$Expression))
dat %>%
dplyr::rename("Protein" = "ADT",
"DSB Exp." = "Expression",
"Cell type" = "ann_level_3") %>%
tidyHeatmap::heatmap(
.column = Protein,
.row = `Cell type`,
.value = `DSB Exp.`,
scale = "none",
rect_gp = grid::gpar(col = "white", lwd = 1),
show_row_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE,
column_names_gp = grid::gpar(fontsize = 8, fontfamily = "arial"),
column_title_gp = grid::gpar(fontsize = 10, fontfamily = "arial"),
row_names_gp = grid::gpar(fontsize = 8, fontfamily = "arial"),
row_title_gp = grid::gpar(fontsize = 10, fontfamily = "arial"),
column_title_side = "top",
palette_value = circlize::colorRamp2(seq(-0, 1.5, length.out = 256),
viridis::magma(256)),
heatmap_legend_param = list(direction = "vertical")) %>%
add_tile(`Cell type`, show_legend = FALSE,
show_annotation_name = FALSE,
palette = paletteer_d("ggsci::category20b_d3",
length(unique(dat$ann_level_3)))) %>%
as_ComplexHeatmap() -> sf1b
sf1b
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)
seuADT@meta.data %>%
dplyr::filter(ann_level_1 %in% unique(seuLst[[1]]$ann_level_1)) %>%
dplyr::select(ann_level_3) %>%
rownames_to_column(var = "cell") %>%
inner_join(as.data.frame(t(seuADT[["ADT"]]@data)) %>%
rownames_to_column(var = "cell")) %>%
pivot_longer(c(-cell, -ann_level_3),
names_to = "ADT",
values_to = "Expression") %>%
dplyr::group_by(ann_level_3, ADT) %>%
dplyr::summarize(Expression = mean(Expression)) %>%
ungroup() %>%
dplyr::filter(ADT %in% ADTs$Description) -> dat
plot(density(dat$Expression))
dat %>%
dplyr::rename("Protein" = "ADT",
"DSB Exp." = "Expression",
"Cell type" = "ann_level_3") %>%
tidyHeatmap::heatmap(
.column = Protein,
.row = `Cell type`,
.value = `DSB Exp.`,
scale = "none",
rect_gp = grid::gpar(col = "white", lwd = 1),
show_row_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE,
column_names_gp = grid::gpar(fontsize = 8, fontfamily = "arial"),
column_title_gp = grid::gpar(fontsize = 10, fontfamily = "arial"),
row_names_gp = grid::gpar(fontsize = 8, fontfamily = "arial"),
row_title_gp = grid::gpar(fontsize = 10, fontfamily = "arial"),
column_title_side = "top",
palette_value = circlize::colorRamp2(seq(-0.1, 2, length.out = 256),
viridis::magma(256)),
heatmap_legend_param = list(direction = "vertical")) %>%
add_tile(`Cell type`, show_legend = FALSE,
show_annotation_name = FALSE,
palette = paletteer_d("ggsci::category20c_d3",
length(unique(dat$ann_level_3)))) %>%
as_ComplexHeatmap() -> sf1c
sf1c
layout = "
A
B
C
"
(wrap_plots(list(sf1a %>%
ComplexHeatmap::draw(heatmap_legend_side = "right") %>%
grid::grid.grabExpr())) +
wrap_plots(list(sf1b %>%
ComplexHeatmap::draw(heatmap_legend_side = "right") %>%
grid::grid.grabExpr())) +
wrap_plots(list(sf1c %>%
ComplexHeatmap::draw(heatmap_legend_side = "right") %>%
grid::grid.grabExpr()))) +
plot_layout(design = layout) +
plot_annotation(tag_levels = list(c("A","B","C"))) &
theme(plot.tag = element_text(size = 16,
face = "bold",
family = "arial"))
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] ggh4x_0.2.8 dsb_1.0.3
[3] paletteer_1.6.0 tidyHeatmap_1.8.1
[5] speckle_1.2.0 glue_1.7.0
[7] org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1
[9] patchwork_1.2.0 clustree_0.5.1
[11] ggraph_2.2.0 here_1.0.1
[13] dittoSeq_1.14.2 glmGamPoi_1.14.3
[15] SeuratObject_4.1.4 Seurat_4.4.0
[17] lubridate_1.9.3 forcats_1.0.0
[19] stringr_1.5.1 dplyr_1.1.4
[21] purrr_1.0.2 readr_2.1.5
[23] tidyr_1.3.1 tibble_3.2.1
[25] ggplot2_3.5.0 tidyverse_2.0.0
[27] edgeR_4.0.15 limma_3.58.1
[29] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
[31] Biobase_2.62.0 GenomicRanges_1.54.1
[33] GenomeInfoDb_1.38.6 IRanges_2.36.0
[35] S4Vectors_0.40.2 BiocGenerics_0.48.1
[37] MatrixGenerics_1.14.0 matrixStats_1.2.0
[39] 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] sass_0.4.8 spatstat.data_3.0-4 ggridges_0.5.6
[25] pbapply_1.7-2 parallelly_1.37.0 rstudioapi_0.15.0
[28] RSQLite_2.3.5 generics_0.1.3 shape_1.4.6
[31] ica_1.0-3 spatstat.random_3.2-2 vroom_1.6.5
[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 SparseArray_1.2.4 Rtsne_0.17
[43] grid_4.3.3 blob_1.2.4 promises_1.2.1
[46] crayon_1.5.2 miniUI_0.1.1.1 lattice_0.22-5
[49] cowplot_1.1.3 KEGGREST_1.42.0 pillar_1.9.0
[52] knitr_1.45 ComplexHeatmap_2.18.0 rjson_0.2.21
[55] future.apply_1.11.1 codetools_0.2-19 leiden_0.4.3.1
[58] getPass_0.2-4 data.table_1.15.0 vctrs_0.6.5
[61] png_0.1-8 gtable_0.3.4 rematch2_2.1.2
[64] cachem_1.0.8 xfun_0.42 S4Arrays_1.2.0
[67] mime_0.12 tidygraph_1.3.1 survival_3.7-0
[70] pheatmap_1.0.12 iterators_1.0.14 statmod_1.5.0
[73] ellipsis_0.3.2 fitdistrplus_1.1-11 ROCR_1.0-11
[76] nlme_3.1-164 bit64_4.0.5 RcppAnnoy_0.0.22
[79] rprojroot_2.0.4 bslib_0.6.1 irlba_2.3.5.1
[82] KernSmooth_2.23-24 colorspace_2.1-0 DBI_1.2.1
[85] tidyselect_1.2.0 processx_3.8.3 bit_4.0.5
[88] compiler_4.3.3 git2r_0.33.0 DelayedArray_0.28.0
[91] plotly_4.10.4 scales_1.3.0 lmtest_0.9-40
[94] callr_3.7.3 digest_0.6.34 goftest_1.2-3
[97] spatstat.utils_3.0-4 rmarkdown_2.25 XVector_0.42.0
[100] htmltools_0.5.7 pkgconfig_2.0.3 highr_0.10
[103] fastmap_1.1.1 rlang_1.1.3 GlobalOptions_0.1.2
[106] htmlwidgets_1.6.4 shiny_1.8.0 farver_2.1.1
[109] jquerylib_0.1.4 zoo_1.8-12 jsonlite_1.8.8
[112] mclust_6.1 RCurl_1.98-1.14 magrittr_2.0.3
[115] GenomeInfoDbData_1.2.11 munsell_0.5.0 Rcpp_1.0.12
[118] viridis_0.6.5 reticulate_1.35.0 stringi_1.8.3
[121] zlibbioc_1.48.0 MASS_7.3-60.0.1 plyr_1.8.9
[124] parallel_4.3.3 listenv_0.9.1 ggrepel_0.9.5
[127] deldir_2.0-2 Biostrings_2.70.2 graphlayouts_1.1.0
[130] splines_4.3.3 tensor_1.5 hms_1.1.3
[133] circlize_0.4.15 locfit_1.5-9.8 ps_1.7.6
[136] igraph_2.0.1.1 spatstat.geom_3.2-8 reshape2_1.4.4
[139] evaluate_0.23 renv_1.0.3 BiocManager_1.30.22
[142] tzdb_0.4.0 foreach_1.5.2 tweenr_2.0.3
[145] httpuv_1.6.14 RANN_2.6.1 polyclip_1.10-6
[148] future_1.33.1 clue_0.3-65 scattermore_1.2
[151] ggforce_0.4.2 xtable_1.8-4 later_1.3.2
[154] viridisLite_0.4.2 memoise_2.0.1 cluster_2.1.6
[157] timechange_0.3.0 globals_0.16.2