AnnData Stores

AnnData is the convention single-cell and spatial biology tools use to keep a measurement matrix together with its annotations. Written to Zarr, it becomes a group hierarchy with fixed member names: X holds the main matrix, obs describes the observations (cells or spots), var describes the variables (genes), and obsm holds alternative per-observation representations such as embeddings.

Reading one is a matter of knowing those names. The example here is a 10x Visium human lymph node section published by Vitessce, read over HTTPS — see vignette("remote-stores") for connection details.

Opening the store

This store publishes no consolidated metadata, so pizzarr cannot list what it contains — an HTTP server offers no equivalent of ls. That is the normal case for AnnData stores, and it is workable only because the member names are fixed by the convention: you address obs, obsm, and X by name rather than discovering them.

g <- zarr_open_group(HttpStore$new(root))

Observation annotations

obs is a group, not an array. Which of its members holds the observation identifiers is recorded in its _index attribute rather than fixed by name, so the lookup goes through the attributes:

obs_attrs <- g$get_item("obs")$get_attrs()$to_list()
index_colname <- obs_attrs[["_index"]]
index_colname
#> [1] "_index"

index_arr <- g$get_item(paste0("obs/", index_colname))$get_item("...")$data

length(index_arr)
#> [1] 3861
head(index_arr, 3)
#> [1] "AAACAAGTATCTCCCA-1" "AAACAATCTACTAGCA-1" "AAACAGAGCGACTCCT-1"

Those are barcodes identifying the 3861 spots on the slide. Cluster assignments live alongside them under the same group:

cluster_arr <- g$get_item("obs/clusters")$get_item("...")$data

table(cluster_arr)
#> cluster_arr
#>   0   1   2   3   4   5   6   7   8 
#> 665 661 654 507 447 352 279 171 125

Embeddings

obsm holds per-observation matrices — one row per spot, as many columns as the representation needs. A UMAP embedding is two columns:

umap_arr <- g$get_item("obsm/X_umap")$get_item("...")$data

dim(umap_arr)
#> [1] 3861    2

Plotting the embedding coloured by cluster gives the standard view of this kind of dataset:

plot(umap_arr[, 1], umap_arr[, 2],
     col = as.integer(as.factor(cluster_arr)),
     pch = 19, cex = 0.4,
     xlab = "UMAP 1", ylab = "UMAP 2")