Azimuth annotation of C133_Neeland data
Batches 0-6 (Ambient RNA removed)
Jovana Maksimovic
March 14, 2024
Last updated: 2024-03-14
Checks: 7 0
Knit directory: paed-inflammation-CITEseq/
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| Rmd | 0018eb8 | Jovana Maksimovic | 2024-03-14 | wflow_publish("analysis/azimuth_annotation") |
| html | bc5128e | Jovana Maksimovic | 2024-03-04 | Build site. |
| Rmd | 23ddb68 | Jovana Maksimovic | 2024-03-04 | wflow_publish(c("analysis/06.0_azimuth_annotation.Rmd", "analysis/06.1_azimuth_annotation_decontx.Rmd")) |
| html | 775cf34 | Jovana Maksimovic | 2024-02-29 | Build site. |
| Rmd | a26da65 | Jovana Maksimovic | 2024-02-29 | wflow_publish("analysis/06.1_azimuth_annotation_decontx.Rmd") |
| html | 919e790 | Jovana Maksimovic | 2024-02-29 | Build site. |
| Rmd | 40e4391 | Jovana Maksimovic | 2024-02-29 | wflow_publish("analysis/06.1_azimuth_annotation_decontx.Rmd") |
Load libraries
suppressPackageStartupMessages({
library(BiocStyle)
library(tidyverse)
library(here)
library(glue)
library(patchwork)
library(scran)
library(scater)
library(scuttle)
library(scMerge)
library(ggupset)
library(Seurat)
library(SeuratData)
library(Azimuth)
})Load data
files <- list.files(here("data",
paste0("C133_Neeland_batch", 0:6),
"data",
"SCEs"),
pattern = "ambient_removed",
full.names = TRUE)
sceLst <- sapply(files, function(fn){
readRDS(file = fn)
})
sceLst$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch0/data/SCEs/C133_Neeland_batch0.ambient_removed.SCE.rds`
class: SingleCellExperiment
dim: 33538 25635
metadata(1): decontX
assays(2): counts decontXcounts
rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
ENSG00000268674
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(25635): 1_AAACCCAAGCTAGTTC-1 1_AAACCCACAGTCGCTG-1 ...
4_TTTGTTGTCTAGTACG-1 4_TTTGTTGTCTCGAACA-1
colData names(45): Barcode Capture ... decontX_contamination
decontX_clusters
reducedDimNames(4): decontX_A_UMAP decontX_B_UMAP decontX_C_UMAP
decontX_D_UMAP
mainExpName: NULL
altExpNames(0):
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch1/data/SCEs/C133_Neeland_batch1.ambient_removed.SCE.rds`
class: SingleCellExperiment
dim: 36601 19522
metadata(1): decontX
assays(2): counts decontXcounts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(19522): 1_AAACCCACACTTCCTG-1 1_AAACCCACAGACAAAT-1 ...
2_TTTGTTGTCATTGGTG-1 2_TTTGTTGTCGATGGAG-1
colData names(58): Barcode Capture ... decontX_contamination
decontX_clusters
reducedDimNames(1): decontX_UMAP
mainExpName: NULL
altExpNames(3): HTO ADT ADT.dsb
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch2/data/SCEs/C133_Neeland_batch2.ambient_removed.SCE.rds`
class: SingleCellExperiment
dim: 36601 22386
metadata(1): decontX
assays(2): counts decontXcounts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(22386): 1_AAACCCAAGACTGTTC-1 1_AAACCCAAGATGATTG-1 ...
2_TTTGTTGTCCAAGGGA-1 2_TTTGTTGTCCTTCTAA-1
colData names(58): Barcode Capture ... decontX_contamination
decontX_clusters
reducedDimNames(1): decontX_UMAP
mainExpName: NULL
altExpNames(3): HTO ADT ADT.dsb
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch3/data/SCEs/C133_Neeland_batch3.ambient_removed.SCE.rds`
class: SingleCellExperiment
dim: 36601 46052
metadata(1): decontX
assays(2): counts decontXcounts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(46052): 1_AAACCCAAGCAGCACA-1 1_AAACCCAAGCATCTTG-1 ...
2_TTTGTTGTCTAGGCCG-1 2_TTTGTTGTCTCGGCTT-1
colData names(58): Barcode Capture ... decontX_contamination
decontX_clusters
reducedDimNames(1): decontX_UMAP
mainExpName: NULL
altExpNames(3): HTO ADT ADT.dsb
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch4/data/SCEs/C133_Neeland_batch4.ambient_removed.SCE.rds`
class: SingleCellExperiment
dim: 36601 18858
metadata(1): decontX
assays(2): counts decontXcounts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(18858): 1_AAACCCAAGGATTTGA-1 1_AAACCCAAGTCTCTGA-1 ...
2_TTTGTTGCATGTGGCC-1 2_TTTGTTGGTCAACATC-1
colData names(58): Barcode Capture ... decontX_contamination
decontX_clusters
reducedDimNames(1): decontX_UMAP
mainExpName: NULL
altExpNames(3): HTO ADT ADT.dsb
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch5/data/SCEs/C133_Neeland_batch5.ambient_removed.SCE.rds`
class: SingleCellExperiment
dim: 36601 32959
metadata(1): decontX
assays(2): counts decontXcounts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(32959): 1_AAACCCAAGAAGATCT-1 1_AAACCCAAGGAGAGGC-1 ...
2_TTTGTTGTCGGATTAC-1 2_TTTGTTGTCTGAGAGG-1
colData names(58): Barcode Capture ... decontX_contamination
decontX_clusters
reducedDimNames(1): decontX_UMAP
mainExpName: NULL
altExpNames(3): HTO ADT ADT.dsb
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch6/data/SCEs/C133_Neeland_batch6.ambient_removed.SCE.rds`
class: SingleCellExperiment
dim: 36601 31275
metadata(1): decontX
assays(2): counts decontXcounts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(31275): 1_AAACCCAAGAAGCGCT-1 1_AAACCCAAGACTCATC-1 ...
2_TTTGTTGTCCCGAGTG-1 2_TTTGTTGTCGAGAATA-1
colData names(58): Barcode Capture ... decontX_contamination
decontX_clusters
reducedDimNames(1): decontX_UMAP
mainExpName: NULL
altExpNames(3): HTO ADT ADT.dsbConvert to Seurat objects
seuLst <- sapply(sceLst, function(sce){
# Azimuth relies on gene symbols for annotation to add these as row names
rownames(sce) <- rowData(sce)$Symbol
# use rounded, ambient removed counts from decontX
# sum any rows that have the same gene symbol
CAGEfightR::utilsAggregateRows(round(assay(sce, "decontXcounts")),
factor(rownames(sce)),
sparse = TRUE) -> count_matrix
seu <- Seurat::CreateSeuratObject(counts = count_matrix,
meta.data = data.frame(colData(sce)))
seu
})
seuLst$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch0/data/SCEs/C133_Neeland_batch0.ambient_removed.SCE.rds`
An object of class Seurat
33514 features across 25635 samples within 1 assay
Active assay: RNA (33514 features, 0 variable features)
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch1/data/SCEs/C133_Neeland_batch1.ambient_removed.SCE.rds`
An object of class Seurat
36591 features across 19522 samples within 1 assay
Active assay: RNA (36591 features, 0 variable features)
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch2/data/SCEs/C133_Neeland_batch2.ambient_removed.SCE.rds`
An object of class Seurat
36591 features across 22386 samples within 1 assay
Active assay: RNA (36591 features, 0 variable features)
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch3/data/SCEs/C133_Neeland_batch3.ambient_removed.SCE.rds`
An object of class Seurat
36591 features across 46052 samples within 1 assay
Active assay: RNA (36591 features, 0 variable features)
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch4/data/SCEs/C133_Neeland_batch4.ambient_removed.SCE.rds`
An object of class Seurat
36591 features across 18858 samples within 1 assay
Active assay: RNA (36591 features, 0 variable features)
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch5/data/SCEs/C133_Neeland_batch5.ambient_removed.SCE.rds`
An object of class Seurat
36591 features across 32959 samples within 1 assay
Active assay: RNA (36591 features, 0 variable features)
$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch6/data/SCEs/C133_Neeland_batch6.ambient_removed.SCE.rds`
An object of class Seurat
36591 features across 31275 samples within 1 assay
Active assay: RNA (36591 features, 0 variable features)Azimuth annotate
Annotate cells in each batch using the Human Lung Cell Atlas v2.0 reference.
options(timeout = max(1000000, getOption("timeout")))
batches <- str_extract(names(seuLst), "batch[0-6]")
seuLst <- sapply(1:length(seuLst), function(i){
out <- here("data",
paste0("C133_Neeland_", batches[i]),
"data",
"SCEs",
glue("C133_Neeland_{batches[i]}.azimuth_annotated_decontx.SEU.rds"))
if(!file.exists(out)){
tmp <- RunAzimuth(seuLst[[i]], reference = "lungref")
seu <- seuLst[[i]]
seu@meta.data <- tmp@meta.data
seu@reductions$ref.umap <- tmp@reductions$ref.umap
if(!is_empty(altExpNames(sceLst[[i]]))){
seu[["ADT"]] <- CreateAssayObject(counts = counts(altExp(sceLst[[i]],
"ADT")))
seu[["ADT.dsb"]] <- CreateAssayObject(counts = counts(altExp(sceLst[[i]],
"ADT.dsb")))
}
saveRDS(seu, out)
fs::file_chmod(out, "664")
if(any(str_detect(fs::group_ids()$group_name,
"oshlack_lab"))) fs::file_chown(out,
group_id = "oshlack_lab")
} else {
seu <- readRDS(out)
}
seu
})An AzimuthData object - reference version: 2.0.0
Contains 6 meta.data field(s) to transfer.
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An AzimuthData object - reference version: 2.0.0
Contains 6 meta.data field(s) to transfer.
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An AzimuthData object - reference version: 2.0.0
Contains 6 meta.data field(s) to transfer.
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An AzimuthData object - reference version: 2.0.0
Contains 6 meta.data field(s) to transfer.
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An AzimuthData object - reference version: 2.0.0
Contains 6 meta.data field(s) to transfer.
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An AzimuthData object - reference version: 2.0.0
Contains 6 meta.data field(s) to transfer.
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An AzimuthData object - reference version: 2.0.0
Contains 6 meta.data field(s) to transfer.
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|======================================================================| 100%seuLst[[1]]
An object of class Seurat
33514 features across 25635 samples within 1 assay
Active assay: RNA (33514 features, 0 variable features)
1 dimensional reduction calculated: ref.umap
[[2]]
An object of class Seurat
36917 features across 19522 samples within 3 assays
Active assay: RNA (36591 features, 0 variable features)
2 other assays present: ADT, ADT.dsb
1 dimensional reduction calculated: ref.umap
[[3]]
An object of class Seurat
36917 features across 22386 samples within 3 assays
Active assay: RNA (36591 features, 0 variable features)
2 other assays present: ADT, ADT.dsb
1 dimensional reduction calculated: ref.umap
[[4]]
An object of class Seurat
36917 features across 46052 samples within 3 assays
Active assay: RNA (36591 features, 0 variable features)
2 other assays present: ADT, ADT.dsb
1 dimensional reduction calculated: ref.umap
[[5]]
An object of class Seurat
36917 features across 18858 samples within 3 assays
Active assay: RNA (36591 features, 0 variable features)
2 other assays present: ADT, ADT.dsb
1 dimensional reduction calculated: ref.umap
[[6]]
An object of class Seurat
36917 features across 32959 samples within 3 assays
Active assay: RNA (36591 features, 0 variable features)
2 other assays present: ADT, ADT.dsb
1 dimensional reduction calculated: ref.umap
[[7]]
An object of class Seurat
36917 features across 31275 samples within 3 assays
Active assay: RNA (36591 features, 0 variable features)
2 other assays present: ADT, ADT.dsb
1 dimensional reduction calculated: ref.umapp <- lapply(seuLst, function(seu){
DimPlot(seu, reduction = "ref.umap", group.by = "predicted.ann_level_3",
label = TRUE, label.size = 2.75) +
NoLegend() +
paletteer::scale_color_paletteer_d("miscpalettes::pastel") -> p1
DimPlot(seu, reduction = "ref.umap", group.by = "predicted.ann_level_4",
label = TRUE, label.size = 2.5) + NoLegend() -> p2
p1 / p2
})
p[[1]]
| Version | Author | Date |
|---|---|---|
| 919e790 | Jovana Maksimovic | 2024-02-29 |
[[2]]
| Version | Author | Date |
|---|---|---|
| 919e790 | Jovana Maksimovic | 2024-02-29 |
[[3]]
| Version | Author | Date |
|---|---|---|
| 919e790 | Jovana Maksimovic | 2024-02-29 |
[[4]]
| Version | Author | Date |
|---|---|---|
| 919e790 | Jovana Maksimovic | 2024-02-29 |
[[5]]
| Version | Author | Date |
|---|---|---|
| 919e790 | Jovana Maksimovic | 2024-02-29 |
[[6]]
| Version | Author | Date |
|---|---|---|
| 919e790 | Jovana Maksimovic | 2024-02-29 |
[[7]]
| Version | Author | Date |
|---|---|---|
| 919e790 | Jovana Maksimovic | 2024-02-29 |
Merge batches and save object
Create a single Seurat object and clean-up the
metadata.
out <- here("data",
"C133_Neeland_merged",
"C133_Neeland_full_clean_decontx.SEU.rds")
if(!file.exists(out)){
seu <- merge(seuLst[[1]],
y = c(seuLst[2:7]))
rm(seuLst)
gc()
meta_regex <- "altexps|vireo|hashedDrops|refAssay|batch|Sample|sum|^detected|^dmmHTO|^Capture.|^total|mito_|percent.mt|orig|_call|_score|^scDblFinder|subsets"
keep <- !str_detect(colnames(seu@meta.data), meta_regex)
seu@meta.data <- seu@meta.data[, keep]
# extract RNA counts
rna <- seu[["RNA"]]@counts
# remove genes that are not expressed in any cells
rna <- rna[rowSums(rna) != 0, ]
# remove genes that are not expressed in >=50 cells across the full dataset
rna <- rna[nexprs(rna, byrow = TRUE) >= 50,]
seu[["RNA"]] <- CreateAssayObject(counts = rna)
saveRDS(seu, out)
fs::file_chmod(out, "664")
if(any(str_detect(fs::group_ids()$group_name,
"oshlack_lab"))) fs::file_chown(out,
group_id = "oshlack_lab")
} else {
rm(seuLst)
gc()
seu <- readRDS(out)
}
seuAn object of class Seurat
20299 features across 196687 samples within 3 assays
Active assay: RNA (19973 features, 0 variable features)
2 other assays present: ADT, ADT.dsbSave cell type group objects
Subset cell type groups.
cellsLst <- list(macrophages = seu[, seu$predicted.ann_level_3 == "Macrophages"],
t_cells = seu[, seu$predicted.ann_level_3 %in% c("T cell lineage",
"Innate lymphoid cell NK")],
other_cells = seu[, !seu$predicted.ann_level_3 %in% c("Macrophages",
"T cell lineage",
"Innate lymphoid cell NK")])
cellsLst$macrophages
An object of class Seurat
20299 features across 151349 samples within 3 assays
Active assay: RNA (19973 features, 0 variable features)
2 other assays present: ADT, ADT.dsb
$t_cells
An object of class Seurat
20299 features across 15511 samples within 3 assays
Active assay: RNA (19973 features, 0 variable features)
2 other assays present: ADT, ADT.dsb
$other_cells
An object of class Seurat
20299 features across 29827 samples within 3 assays
Active assay: RNA (19973 features, 0 variable features)
2 other assays present: ADT, ADT.dsbSave objects.
sapply(1:length(cellsLst), function(i){
out <- here("data",
"C133_Neeland_merged",
glue("C133_Neeland_full_clean_decontx_{names(cellsLst[i])}.SEU.rds"))
if(!file.exists(out)) saveRDS(cellsLst[[i]], out)
fs::file_chmod(out, "664")
if(any(str_detect(fs::group_ids()$group_name,
"oshlack_lab"))) fs::file_chown(out,
group_id = "oshlack_lab")
})[[1]]
NULL
[[2]]
NULL
[[3]]
NULLSession info
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Australia/Melbourne
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] Azimuth_0.4.6 shinyBS_0.61.1
[3] lungref.SeuratData_2.0.0 SeuratData_0.2.2
[5] SeuratObject_4.1.4 Seurat_4.4.0
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[235] spatstat.sparse_3.0-3 spatstat.explore_3.2-6
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