ImmunData: ImmunData: A data structure for storing adaptive immune...

In immundata: A Unified Data Layer for Large-Scale Single-Cell, Spatial and Bulk Immunomics

ImmunData: A data structure for storing adaptive immune receptor repertoire data

Description

ImmunData stores adaptive immune receptor repertoire (AIRR) data and the rules used to turn observed sequences or cells into data units for analysis. Think AnnData or SeuratObject, but for immune repertoires.

You work with an ImmunData object after importing bulk or single-cell AIRR-seq data. The major idea behind ImmunData is that because sequencing provides only information about sequences and, for single-cell data, cell barcodes, the your responsibility is to determine, which sequences you want to treat as the same receptor, repertoire, or stratum (group of repertoires). You define these analysis units with schemas. A schema is a stored set of column names and chain-selection rules that tells ImmunData how to group observations. Those definitions are kept inside ImmunData to ensure that downstream functions count, filter, and compare the same units consistently. Repertoire and strata schemas can be changed later to re-aggregate repertoires differently, e.g., merge receptors from different clusters into per-patient clusters. Receptor schema is fixed once and for all, so if you want to work with a different receptor definition, e.g., use "CDR3aa + V gene" instead of just "CDR3aa" as a definiton for a unique receptor, you will need to create a separate ImmunData object.

ImmunData is immutable, meaning that functions that transform an ImmunData object return a new object, and the original object is not changed. Due to multiple optimisations on the backend, it does not mean that you re-create the whole dataset each time you run a, let's stay, a filter. However, it does affect analysis workflow significantly. You can read about it more on the website and in tutorials.

From observed data to analysis units

ImmunData connects observed records to user-defined analysis units:

• A chain observation is an observed receptor-chain sequence, such as a TRA, TRB, or IGH sequence. Chain observations form the main table.

• A barcode is an observed identifier for a cell in single-cell data. It links chains found in the same cell.

• A receptor is a virtual analysis unit that you define. For example, you may define it by CDR3 sequence alone, by CDR3 and V gene, or as a paired TRA-TRB receptor. The receptor schema records which chain features and loci must match for observations to receive the same receptor identifier.

• A repertoire is a virtual collection of receptors that you define from annotation columns. For example, one repertoire may contain all receptors from one sample, or from one donor at one time point.

• A stratum is a virtual collection of repertoires for a comparison. For example, one stratum may contain all repertoires from one treatment arm.

These definitions do not change the observed sequences. They determine how observations are grouped and counted during analysis. The resulting hierarchy is ⁠chain observations and barcodes -> receptors -> repertoires -> strata⁠.

Inspect and transform an object

Print an object for a compact overview. Use ⁠$receptors⁠ for the receptor table, ⁠$repertoires⁠ for one summary row per repertoire, and ⁠$strata⁠ for one row per stratum. Most analysis functions accept the complete ImmunData object directly.

Common transformations include:

• filter_immundata() to keep selected chains, cells, or receptors;

• mutate_immundata() to calculate annotation columns;

• annotate() to add external biological information;

• agg_repertoires() to define repertoires; and

• agg_strata() to group repertoires into strata.

Create an object

Create an ImmunData object with read_repertoires(), or reopen a saved object with read_immundata(). Do not call the ⁠$new()⁠ constructor in analysis code. Direct construction is reserved for package developers.

Lazy data and storage

The chain-level table uses duckplyr and can remain on disk. Filtering, mutation, and aggregation stay lazy when possible, so large datasets do not need to be loaded fully into R memory. Downstream analysis functions in the immunarch package are designed to accept lazy ImmunData objects. Pass the object directly; you usually do not need to call dplyr::collect(). Collect data only when another function explicitly requires an in-memory data frame or when you want to inspect a small table in R.

Objects created by read_repertoires() are backed by files in their output folder. Keep that folder while you use the object. Use write_immundata() to save a transformed object and read_immundata() to reopen it.

Public fields

schema_receptor

A named list defining the virtual receptor unit. The features element names the chain columns used to group observations, such as CDR3 sequence and V gene. The chains element selects one chain or a paired set of chains.

schema_repertoire

A character vector naming annotation columns whose unique combinations define one repertoire, such as sample_id or c("donor_id", "timepoint"). It is NULL when repertoires have not been defined.

schema_strata

A character vector naming repertoire-level columns whose unique combinations define one stratum, such as treatment. It is NULL when strata have not been defined.

Active bindings

receptors

A derived duckplyr table of distinct receptors. For a paired receptor, the selected chain features are shown side by side.

annotations

The lazy duckplyr table of retained chain observations and their biological annotations. For most tasks, pass the complete ImmunData object to a transformation function or use collect(idata) to inspect this table in memory.

repertoires

A small table with one row per repertoire, the columns that define it, and summary statistics such as n_barcodes and n_receptors. It is NULL when repertoires have not been defined.

strata

A small table with one row per stratum, its label, and the repertoire-level columns that define it. It is NULL when strata have not been defined.

provenance

Read-only named list describing the snapshot origin and storage context carried by this object. Retrieve the complete list with idata$provenance, or one field with, for example, idata$provenance$current_path. The fields are:

• home_path: project home used for managed snapshots and artifacts. The original ingestion snapshot is stored directly in this folder; it is NULL for an object with no persisted home.

• current_path: exact folder of the most recently loaded or written snapshot. Transformations preserve this source path until the transformed object is written as another snapshot; it is NULL for an object that has never been loaded from or written to disk.

• snapshot_root: derived managed-snapshot root, home_path/snapshots, or NULL when home_path is NULL.

• artifacts_root: derived project-level root for optional external tool outputs, home_path/artifacts, or NULL when home_path is NULL.

• artifacts_path: derived namespace for artifacts associated with the most recently loaded or written snapshot. It is artifacts_root/root for the original ingestion, ⁠artifacts_root/<tag>/vNNN⁠ for a managed snapshot, and ⁠artifacts_root/by-id/<snapshot_id>⁠ for a detached explicit snapshot. External tools can append ⁠<tool>/<run>⁠ and create that directory; artifact contents are not part of ImmunData. Write a transformed object as a new snapshot before storing artifacts that should be associated with the transformed data.

• snapshot_id: unique identifier generated when the snapshot is written; NULL for an in-memory object that has never been written.

• lineage: ordered list of ingestion and snapshot events leading to the current snapshot.

The accessor is read-only; assigning to idata$provenance is an error.

Methods

Public methods

• ImmunData$new()

• ImmunData$clone()

---

ImmunData$new()

Low-level constructor for package developers. Analysis code must create an ImmunData object with read_repertoires() or reopen one with read_immundata().

Usage

ImmunData$new(
  schema,
  annotations,
  repertoires = NULL,
  provenance = NULL,
  strata = NULL
)

Arguments

schema

A character vector or named list. A character vector names the features used to define a chain-agnostic receptor. A named list is created by make_receptor_schema() and can also select receptor chains.

annotations

A duckplyr table. It contains retained chain observations, receptor identifiers, and biological annotations.

repertoires

A data frame or NULL. It contains one row per repertoire and its summary statistics and is usually created by agg_repertoires().

provenance

A list or NULL. It contains internal storage and snapshot history.

strata

A data frame or NULL. It contains one row per stratum, its label, and the repertoire-level columns that define it.

---

ImmunData$clone()

The objects of this class are cloneable with this method.

Usage

ImmunData$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

read_repertoires(), read_immundata(), write_immundata(), agg_repertoires(), agg_strata(), filter_immundata(), mutate_immundata(), annotate()

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Load the small dataset included with immundata, then define one repertoire
# for each treatment-response group.
idata <- get_test_idata() |>
  agg_repertoires(schema = "Response")

idata$repertoires |>
  select(Response, n_barcodes, n_receptors) |>
  arrange(Response)
# Expected result:
#   Response n_barcodes n_receptors
#   FR              955         871
#   PR              947         867

# Under the current receptor definition, the full-response (FR) repertoire
# contains 955 chain observations grouped into 871 receptor units.

# Keep only the full-response repertoire. filter() returns a new object.
fr_only <- idata |>
  filter(Response == "FR")

tibble(
  original_repertoires = nrow(idata$repertoires),
  filtered_repertoires = nrow(fr_only$repertoires)
)
# Expected result:
#   original_repertoires filtered_repertoires
#                      2                    1
# The original object still contains both repertoires.

immundata documentation built on Aug. 26, 2026, 5:08 p.m.