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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 43ca580 | Dave Tang | 2026-07-31 | Checking out an Azimuth model |
Azimuth provides a reference and a query cell is placed individually on this reference, and then labelled by a weighted vote using the reference cells it lands near.
Install as per the official instructions.
if (!requireNamespace('remotes', quietly = TRUE)) {
install.packages('remotes')
}
remotes::install_github('satijalab/azimuth', ref = 'master')We will examine the Human PBMC reference that has been downloaded using zenodo_get.
zenodo_get -r 4546839An Azimuth model holds two files:
ref.Rds, a Seurat object carrying the reference cells,
their labels and a precomputed dimensional
reductionidx.annoy, an approximate nearest neighbor index over
that reduction.azimuth_model <- paste0("data/azimuth_pbmc/")
stopifnot(dir.exists(azimuth_model))RunAzimuth() calls
Azimuth:::LoadReference() internally; we will call the same
function directly. It accepts a local directory or a URL, and returns a
list of two Seurat objects:
$map used for the annotation, and$plot a lighter copy used only for drawing the
reference UMAP.azimuth_obj <- Azimuth:::LoadReference(path = azimuth_model)
azimuth_obj$map
An object of class Seurat
5228 features across 36433 samples within 2 assays
Active assay: refAssay (5000 features, 0 variable features)
1 layer present: data
1 other assay present: ADT
2 dimensional reductions calculated: refUMAP, refDR
$plot
An object of class Seurat
1 features across 24760 samples within 1 assay
Active assay: RNA (1 features, 0 variable features)
2 layers present: counts, data
1 dimensional reduction calculated: refUMAPrefAssay is the default assay.
ref_assay_name <- SeuratObject::DefaultAssay(azimuth_obj$map)
ref_assay_name[1] "refAssay"We already saw above that there are 5,000 features.
all_features <- rownames(azimuth_obj$map)
length(all_features)[1] 5000refDR is the reduction the reference was built with. Its
feature loadings define the coordinate system query cells are projected
into, so its rownames are the genes that can influence where a query
cell lands.
refdr_loadings <- SeuratObject::Loadings(azimuth_obj$map[["refDR"]])
refdr_features <- rownames(refdr_loadings)
dim(refdr_loadings)[1] 5000 50SCTAssay.
assay_obj <- azimuth_obj$map[[ref_assay_name]]
assay_objSCTAssay data with 5000 features for 36433 cells, and 1 SCTModel(s)
First 10 features:
S100A9, GNLY, S100A8, LYZ, IGKC, NKG7, IGLC2, IGHM, PPBP, CCL5 all(row.names(assay_obj) == all_features)[1] TRUEThe labels Azimuth can transfer are simply the reference’s metadata columns.
head(azimuth_obj$map@meta.data) celltype.l1 celltype.l2 celltype.l3 ori.index
L1_AAACGAATCCTCACCA other T gdT gdT_3 27
L1_AAACGCTAGAGCATTA CD8 T CD8 TEM CD8 TEM_2 30
L1_AAACGCTCAACGATCT CD8 T CD8 TCM CD8 TCM_1 35
L1_AAACGCTGTGCTCGTG other T dnT dnT_2 40
L1_AAACGCTTCTTGGTCC B B intermediate B intermediate lambda 42
L1_AAAGAACCAAGCGGAT CD4 T CD4 TCM CD4 TCM_3 46
nCount_refAssay nFeature_refAssay
L1_AAACGAATCCTCACCA 0 0
L1_AAACGCTAGAGCATTA 0 0
L1_AAACGCTCAACGATCT 0 0
L1_AAACGCTGTGCTCGTG 0 0
L1_AAACGCTTCTTGGTCC 0 0
L1_AAAGAACCAAGCGGAT 0 0Neighbour names.
neighbour_names <- names(methods::slot(azimuth_obj$map, "neighbors"))
neighbour_names[1] "refdr.annoy.neighbors"
sessionInfo()R version 4.5.2 (2025-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 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-r0.3.26.so; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Azimuth_0.5.1 shinyBS_0.65.0 Seurat_5.5.1 SeuratObject_5.4.0
[5] sp_2.2-3 lubridate_1.9.5 forcats_1.0.1 stringr_1.6.0
[9] dplyr_1.2.1 purrr_1.2.2 readr_2.1.6 tidyr_1.3.2
[13] tibble_3.3.1 ggplot2_4.0.3 tidyverse_2.0.0 workflowr_1.7.2
loaded via a namespace (and not attached):
[1] fs_2.1.0 ProtGenerics_1.42.0
[3] matrixStats_1.5.0 spatstat.sparse_3.2-0
[5] bitops_1.0-9 DirichletMultinomial_1.52.0
[7] TFBSTools_1.48.0 httr_1.4.8
[9] RColorBrewer_1.1-3 tools_4.5.2
[11] sctransform_0.4.3 R6_2.6.1
[13] DT_0.34.0 lazyeval_0.2.3
[15] uwot_0.2.4 rhdf5filters_1.22.0
[17] withr_3.0.3 gridExtra_2.3.1
[19] progressr_1.0.0 cli_3.6.6
[21] Biobase_2.70.0 spatstat.explore_3.8-2
[23] fastDummies_1.7.6 EnsDb.Hsapiens.v86_2.99.0
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