Package {immundata}

Title:	A Unified Data Layer for Large-Scale Single-Cell, Spatial and Bulk Immunomics
Version:	0.1.0
Contact:	support@immunomind.com
Description:	Provides a unified data layer for single-cell, spatial and bulk T-cell and B-cell immune receptor repertoire data. Think AnnData or SeuratObject, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or VDJ sequencing data.
License:	Apache License (≥ 2)
URL:	https://immunomind.github.io/docs/, https://github.com/immunomind/immundata
BugReports:	https://github.com/immunomind/immundata/issues
Encoding:	UTF-8
Depends:	R (≥ 4.1.0), dplyr (≥ 1.2.1), duckplyr (≥ 1.2.1)
Imports:	checkmate, cli, dbplyr, glue, jsonlite, lifecycle, R6, readr, rlang, tibble, tools, utils
Suggests:	duckdb, rmarkdown, testthat (≥ 3.0.0), Seurat
Config/Needs/website:	altdoc
Config/testthat/edition:	3
Config/testthat/parallel:	true
Config/roxygen2/version:	8.1.0
NeedsCompilation:	no
Packaged:	2026-08-26 09:16:02 UTC; vdn
Author:	Vadim I. Nazarov [aut, cre]
Maintainer:	Vadim I. Nazarov <support@immunomind.com>
Repository:	CRAN
Date/Publication:	2026-08-26 10:00:02 UTC

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

Description

Provides a unified data layer for single-cell, spatial and bulk T-cell and B-cell immune receptor repertoire data. Think AnnData or SeuratObject, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or VDJ sequencing data.

Author(s)

Maintainer: Vadim I. Nazarov support@immunomind.com (ORCID)

Authors:

• Vadim I. Nazarov support@immunomind.com (ORCID)

See Also

Useful links:

• https://immunomind.github.io/docs/

• https://github.com/immunomind/immundata

• Report bugs at https://github.com/immunomind/immundata/issues

Internal Immundata Global Configuration

Description

IMD_GLOBALS is an internal list that stores globally used constants across the Immundata system. It is not intended for direct use by package users, but rather to ensure consistency in schema field names, default file names, and internal error messages.

Usage

IMD_GLOBALS

Components

• messages: Named list of default messages and error texts (e.g., "NotImpl").

• schema: Standardized column names for internal schema usage. These include:

  • cell: Column name for cell barcode IDs.

  • receptor: Column name for receptor unique identifiers.

  • repertoire: Column name for repertoire group IDs.

  • manifest_filename: Column name for manifest file paths (internal).

  • count: Column name for receptor count per group.

• files: Default file names used to store structured Immundata:

  • metadata: File name for schemas and small summary tables (metadata.json).

  • annotations: File name for chain-level data (annotations.parquet).

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

Active bindings

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

ImmunData$clone()

The objects of this class are cloneable with this method.

Usage

ImmunData$clone(deep = FALSE)

Arguments

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.

Group AIRR sequence rows into receptors

Description

agg_receptors() is a low-level function used during AIRR data ingestion. It decides which sequence rows represent the same biological receptor and adds package-standard identifiers and counts to the input table.

A receptor can be one chain or a pair of chains from the same cell. The schema argument defines which sequence features and loci make two receptors identical.

This function works with a prepared duckplyr table and returns a duckplyr table. It does not accept or return an ImmunData object. Most analysis workflows should provide the same arguments to read_repertoires(), which calls agg_receptors() during import.

Usage

agg_receptors(
  dataset,
  schema,
  barcode_col = NULL,
  count_col = NULL,
  locus_col = NULL,
  umi_col = NULL,
  verbose = getOption("immundata.verbose", TRUE)
)

Arguments

Details

The receptor features are the columns that define the identity of one chain. Two chains with the same values in all feature columns receive the same receptor identity in a single-chain analysis. Common features include V gene, J gene, and CDR3 amino acid sequence.

The function supports three input modes:

• Uncounted sequence table: If neither barcode_col nor count_col is supplied, every input row is treated as one observed chain. A synthetic barcode is created for each row, and imd_n_chains is set to 1.

• Bulk repertoire: If count_col is supplied, every input row receives a synthetic barcode and its abundance is copied to imd_n_chains.

• Single-cell repertoire: If barcode_col is supplied, rows are grouped by cell. umi_col is required and imd_n_chains is set to 1 for every retained cell-chain observation.

When one chain is specified in schema, only that locus is retained. If a cell contains several chains from that locus, the row with the highest value in umi_col is retained. If the highest values are tied, the first row is retained.

When two chains are specified, only cells containing both requested loci are retained. The selected chains are paired by barcode, and both rows receive the same imd_receptor_id. Cells with incomplete pairs are excluded.

A relaxed pair such as c("IGH", "IGL|IGK") requires IGH and exactly one of the two alternative light-chain loci. Cells containing both IGL and IGK are excluded.

Numeric imd_receptor_id values identify receptors within the returned table. The particular number assigned to a receptor is not a biological identifier and may change when the data are aggregated again.

Value

A duckplyr table containing the retained input rows and these package-standard columns:

• imd_receptor_id: links rows that belong to the same receptor.

• imd_barcode: contains the input cell barcode, or a synthetic row-level barcode for uncounted and bulk data.

• imd_chain_id: identifies an individual retained chain row.

• imd_n_chains: contains 1 for uncounted and single-cell data, or the value from count_col for bulk data.

• imd_count: initialized to 0; receptor counts are calculated later by agg_repertoires().

See Also

read_repertoires(), make_receptor_schema(), agg_repertoires(), ImmunData

Define biological repertoires and calculate receptor abundance

Description

Use agg_repertoires() to define which receptor observations belong to the same biological repertoire and calculate receptor abundance within each repertoire.

Use this function after importing data without repertoire definitions, or when you want to redefine repertoires using sample information. One repertoire usually represents one biological sample. It can also represent one sample and time-point combination. The columns in schema define these groups.

The unit being defined is the repertoire. The function does not remove chain rows or redefine cells or receptors. It returns a new ImmunData object. The original object is not changed.

Usage

agg_repertoires(
  idata,
  schema = "repertoire_id",
  verbose = getOption("immundata.verbose", TRUE)
)

Arguments

Details

The function calculates summaries at repertoire and receptor levels while keeping the original chain rows.

Value

A new ImmunData object with repertoire definitions and abundance statistics. Its repertoire summary contains the schema columns, imd_repertoire_id, n_barcodes, and n_receptors. Its chain rows also contain imd_repertoire_id, imd_count, imd_proportion, and n_repertoires.

What the function calculates

The returned repertoire summary contains one row for each repertoire:

• imd_repertoire_id: a new integer identifier for the repertoire.

• n_barcodes: the number of observed cells for single-cell data, or the total abundance for bulk data.

• n_receptors: the number of distinct receptors in the repertoire.

The function also adds these values to each chain row:

• imd_repertoire_id: the repertoire containing the row.

• imd_count: the number of cells carrying that receptor in single-cell data, or its summed abundance in bulk data, within the repertoire.

• imd_proportion: the receptor's fraction of the repertoire, calculated as imd_count / n_barcodes.

• n_repertoires: the number of repertoires in which the receptor occurs.

Values calculated for a receptor are repeated on all chain rows belonging to that receptor in the same repertoire.

Calling agg_repertoires() again replaces previous repertoire definitions, receptor counts, proportions, and related strata summaries.

Backend and storage

Large-table calculations run on the duckplyr annotation table. The annotation data remain lazy when the input is lazy. The small repertoire summary is collected and stored in the returned object.

Aggregation can be expensive for a large dataset. After checking the result, consider saving it so later analyses do not repeat the calculation. Use write_immundata(idata, tag = "by-sample") to create a managed snapshot in the object's project home. Managed snapshots are versioned, so another write with the same tag creates a new version and keeps the earlier version.

Use write_immundata(idata, output_folder = "path/to/result") when you need a standalone saved state in a specific folder, for example to share it or to choose a new storage location. Unlike a managed snapshot, writing to an existing explicit folder replaces the ImmunData files in that folder. Both forms materialize pending duckplyr calculations and return a disk-backed object that can be reopened with read_immundata().

See Also

read_repertoires(), agg_strata(), write_immundata(), ImmunData

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Create a small bulk T-cell receptor dataset from two biological samples
bulk_data <- tibble(
  Sample = c("Tumor", "Tumor", "Blood", "Blood"),
  cdr3_aa = c("CASSA", "CASSB", "CASSA", "CASSC"),
  v_call = c("TRBV1", "TRBV2", "TRBV1", "TRBV3"),
  abundance = c(20L, 5L, 4L, 6L)
)

bulk_file <- tempfile(fileext = ".tsv")
readr::write_tsv(bulk_data, bulk_file)

# Import receptors without defining repertoires
idata <- read_repertoires(
  path = bulk_file,
  schema = c("cdr3_aa", "v_call"),
  count_col = "abundance",
  repertoire_schema = NULL,
  output_folder = tempfile("immundata-example-")
)

# Define one repertoire for each biological sample
sample_repertoires <- idata |>
  agg_repertoires(schema = "Sample")

sample_repertoires$repertoires |>
  select(Sample, n_barcodes, n_receptors) |>
  arrange(Sample)
# Expected result:
#   Sample n_barcodes n_receptors
#   Blood          10           2
#   Tumor          25           2

# For example, CASSA forms 80% of the Tumor repertoire and 40% of the
# Blood repertoire. It occurs in two repertoires.

# For a large dataset, save the result as a managed snapshot so this
# aggregation does not need to run again.
saved_repertoires <- write_immundata(
  sample_repertoires,
  tag = "by-sample"
)

Group repertoires into biological strata

Description

Use agg_strata() to place sample repertoires into biological comparison groups, such as treatment arms, tissues, or disease groups.

Use this function after agg_repertoires() when several repertoires should be analysed as one group. A stratum contains every repertoire with the same value, or the same combination of values, in schema.

The unit being grouped is a whole repertoire. The function returns a new ImmunData object. The original object is not changed.

Usage

agg_strata(idata, schema, prefix = "Strata")

Arguments

Details

If schema contains several columns, a separate stratum is created for each observed combination. For example, c("Tissue", "Therapy") can define separate blood and tumour strata within each treatment arm.

Calling agg_strata() again replaces the existing strata with groups defined by the new schema.

Value

A new ImmunData object in which every repertoire belongs to one stratum. The ⁠$strata⁠ table lists the strata, their defining biological values, and their automatic labels. Repertoire definitions and summary statistics are preserved.

Identifiers and storage

imd_strata_id is an internal identifier and can change when strata are rebuilt. It is added to the repertoire table and to the underlying chain annotations. strata_name is stored only in the smaller repertoire and strata tables.

Calling agg_repertoires() again rebuilds the repertoires, so it removes the existing strata. Call agg_strata() again after redefining repertoires.

See Also

agg_repertoires(), rename_strata(), ImmunData

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Define sample repertoires using the biological metadata in the test data
idata <- get_test_idata() |>
  agg_repertoires(c("Response", "Therapy"))

# Group the sample repertoires into treatment arms
treatment_groups <- idata |>
  agg_strata(schema = "Therapy")

treatment_groups$repertoires |>
  select(Therapy, Response, imd_strata_id, strata_name) |>
  arrange(imd_strata_id)
# Expected result:
#   Therapy Response imd_strata_id strata_name
#   CAR-T   PR                   1 Strata1
#   ICI     FR                   2 Strata2

# Each repertoire is now assigned to its treatment stratum. Any additional
# repertoire with the same Therapy value would receive the same stratum ID.

Annotate an AnnData object from ImmunData (by barcode)

Description

Copy selected columns from idata$annotations to adata$obs, matching by cell barcode (adata$obs_names).

Usage

annotate_anndata(idata, adata, cols)

Arguments

Value

The updated AnnData object.

Add external information to ImmunData

Description

Use the ⁠annotate_*()⁠ functions to add information stored in another data frame to an ImmunData object. For example, you can add cell types from a single-cell analysis, antigen labels for receptors, or clinical information for samples.

When matching identifiers are unique, the functions keep every row in idata. When a row has no match in annotations, the new columns contain NA. Each function returns a new ImmunData object. The original object is not changed.

Usage

annotate_immundata(
  idata,
  annotations,
  by,
  keep_repertoires = TRUE,
  remove_limit = FALSE,
  conflicts = c("error", "replace")
)

annotate(
  idata,
  annotations,
  by,
  keep_repertoires = TRUE,
  remove_limit = FALSE,
  conflicts = c("error", "replace")
)

annotate_receptors(
  idata,
  annotations,
  annot_col = imd_schema("receptor"),
  keep_repertoires = TRUE,
  remove_limit = FALSE,
  conflicts = c("error", "replace")
)

annotate_barcodes(
  idata,
  annotations,
  annot_col = "<rownames>",
  keep_repertoires = TRUE,
  remove_limit = FALSE,
  conflicts = c("error", "replace")
)

annotate_chains(
  idata,
  annotations,
  annot_col = imd_schema("chain"),
  keep_repertoires = TRUE,
  remove_limit = FALSE,
  conflicts = c("error", "replace")
)

Arguments

Details

The functions differ in how they select the columns used for matching. The rules for duplicate identifiers, column conflicts, and preserved summaries are the same for all functions.

Value

A new ImmunData object containing the added annotation columns. Existing repertoire and strata summaries are preserved when keep_repertoires = TRUE.

Choose a function

Use the function that matches the type of information you want to add:

• annotate_barcodes() matches cell or barcode identifiers.

• annotate_receptors() matches receptor identifiers. All rows belonging to a matched receptor receive the new information.

• annotate_chains() matches chain identifiers.

• annotate() matches any one or more columns that you specify in by.

The first three functions select the correct ImmunData identifier for you. annotate_immundata() is an alternative name for annotate().

How matching works

For annotate_barcodes(), annotate_receptors(), and annotate_chains(), annot_col names the identifier column in annotations. For example, annot_col = "barcode" matches the barcode column in annotations with the barcode identifier in idata.

For general matching, supply by in the form c(immundata_column = "annotation_column"). For example, by = c(Response = "response_code") matches the Response column in idata with the response_code column in annotations. To match columns with the same name, use a value such as by = c(Response = "Response"). You can include more than one pair of columns in by.

Columns from annotations that are not used for matching are added to the result. Rows in idata without a match receive NA. Rows in annotations without a match are ignored.

Annotation identifiers must be unique

annotations must contain at most one row for each identifier, or each combination of identifiers when matching several columns. For example, a barcode annotation table must contain at most one row per barcode.

The function does not check this rule because the annotation table may be very large. If an identifier occurs several times, the corresponding rows in idata are repeated. This can make receptor counts, proportions, and other summaries incorrect.

Existing annotation columns

By default, the function stops if a new annotation column has the same name as a column already present in idata. This prevents accidental replacement.

Use conflicts = "replace" to replace existing annotation columns. Columns that define receptors, repertoires, strata, or other ImmunData state are protected and cannot be replaced. The old column is removed before matching, so rows without a new match receive NA.

Repertoire and strata summaries

With the default keep_repertoires = TRUE, existing repertoire and strata summaries are copied to the new object without recalculation. Use this option when you are only adding information and the matching identifiers in annotations are unique.

Set keep_repertoires = FALSE when you plan to filter rows or define new repertoires using the added information. This removes existing repertoire and strata summaries and their derived columns. After annotation and filtering, use agg_repertoires() to define the new repertoires.

Very wide annotation tables

By default, the function stops when annotations contains 100 or more columns. Adding a very wide table, such as a complete gene-expression matrix, can be slow and require a large amount of memory. If you understand this cost, set remove_limit = TRUE to allow the operation.

See Also

dplyr::left_join(), agg_repertoires(), filter_immundata(), mutate_immundata(), ImmunData

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Load data included with immundata
idata <- get_test_idata()

# Add cell types by matching barcode identifiers
cell_labels <- tibble(
  barcode = c("S1_1", "S1_2"),
  cell_type = c("CD8 T cell", "CD4 T cell")
)

idata_with_cells <- idata |>
  annotate_barcodes(
    annotations = cell_labels,
    annot_col = "barcode"
  )

idata_with_cells |>
  collect() |>
  filter(imd_barcode %in% c("S1_1", "S1_2", "S1_3")) |>
  select(imd_barcode, cell_type) |>
  arrange(imd_barcode)
# Expected result:
#   imd_barcode cell_type
#   S1_1        CD8 T cell
#   S1_2        CD4 T cell
#   S1_3        NA

# Add antigen labels to selected receptors
receptor_labels <- tibble(
  receptor_id = c(738L, 1567L),
  antigen = c("CMV", "CMV")
)

idata_with_antigens <- idata |>
  annotate_receptors(
    annotations = receptor_labels,
    annot_col = "receptor_id"
  )

idata_with_antigens |>
  collect() |>
  filter(!is.na(antigen)) |>
  distinct(imd_receptor_id, cdr3_aa, antigen) |>
  arrange(imd_receptor_id)
# Expected result:
#   imd_receptor_id cdr3_aa       antigen
#               738 ASRAGAGTGELF  CMV
#              1567 ASFPVLSPYNEQF CMV

# Match columns with different names
response_info <- tibble(
  response_code = c("FR", "PR"),
  response_label = c("Full response", "Partial response")
)

idata_with_response <- idata |>
  annotate(
    annotations = response_info,
    by = c(Response = "response_code")
  )

idata_with_response |>
  collect() |>
  distinct(Response, response_label) |>
  arrange(Response)
# Expected result:
#   Response response_label
#   FR       Full response
#   PR       Partial response

# Replace an annotation column intentionally
revised_cell_labels <- tibble(
  barcode = c("S1_1", "S1_2"),
  cell_type = c("Cytotoxic T cell", "Helper T cell")
)

idata_with_revised_cells <- idata_with_cells |>
  annotate_barcodes(
    annotations = revised_cell_labels,
    annot_col = "barcode",
    conflicts = "replace"
  )

# Remove old repertoire summaries before defining repertoires by cell type
cell_repertoires <- idata |>
  annotate_barcodes(
    annotations = cell_labels,
    annot_col = "barcode",
    keep_repertoires = FALSE
  ) |>
  filter(!is.na(cell_type)) |>
  agg_repertoires(schema = "cell_type")

cell_repertoires$repertoires |>
  arrange(cell_type)
# Expected result:
#   imd_repertoire_id cell_type  n_barcodes n_receptors
#                   1 CD4 T cell          1           1
#                   2 CD8 T cell          1           1

Annotate a Seurat object from ImmunData (by barcode)

Description

Copy selected columns from idata$annotations to Seurat metadata using the cell barcode. This is the simplest way to transfer data from immundata to Seurat object, e.g., for plotting data on UMAP.

Usage

annotate_seurat(idata, sdata, cols)

Arguments

Details

See functions annotate_clonality_rank and annotate_clonality_prop in immunarch package.

Value

The updated Seurat object with new metadata columns.

See Also

ImmunData, SeuratObject::AddMetaData

Examples

## Not run: 
# After annotating receptors:
idata <- annotate_clonality_prop(idata)

# Transfer the clonality bin to Seurat and plot:
sdata <- annotate_seurat(idata, sdata, cols = "clonal_prop_bin")
Seurat::DimPlot(sdata, reduction = "umap", group.by = "clonal_prop_bin", shuffle = TRUE)

# Alternative: rank bins
idata <- annotate_clonality_rank(idata, bins = c(10, 100))
sdata <- annotate_seurat(idata, sdata, cols = "clonal_rank_bin")

## End(Not run)

Collect ImmunData annotations

Description

Collects annotations from an ImmunData object and returns them as a tibble. Factor columns are converted to character.

Usage

## S3 method for class 'ImmunData'
collect(x, ...)

Arguments

Value

A tibble with collected annotations.

Compute ImmunData annotations

Description

Materializes the annotation table of an ImmunData object via dplyr::compute() and returns a new ImmunData.

Usage

## S3 method for class 'ImmunData'
compute(x, ...)

Arguments

Value

A new ImmunData object with computed annotations and the input repertoire, strata, schema, and provenance state preserved.

Count chain rows in ImmunData

Description

Use count() to find how many chain rows are stored in an ImmunData object.

Use this method for a quick check of dataset size. The unit counted is one retained chain row. Each retained cell with a paired receptor usually contributes two rows, one for each chain. The same receptor can therefore contribute two rows for every cell carrying it. For bulk data with an abundance column, this method counts table rows rather than the summed sequence abundance.

The function returns a one-row duckplyr table. The original object is not changed.

Usage

## S3 method for class 'ImmunData'
count(x, ..., wt = NULL, sort = FALSE, name = NULL)

Arguments

Details

This method currently provides only the total row count. The grouping, weighting, sorting, and result-name arguments of dplyr::count() are accepted for method compatibility but are not applied.

The calculation runs on the duckplyr annotation table and can remain in DuckDB. Use dplyr::pull() or dplyr::collect() to bring the small result into R.

Value

A one-row duckplyr table with an integer column named n. This value is the number of rows in the chain-level annotation table.

See Also

dplyr::count(), dplyr::collect(), ImmunData

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)
idata <- get_test_idata()

idata |> count()
# Expected result:
#      n
#   1902

# The result means that the object contains 1,902 retained chain rows.
# It does not mean that it contains 1,902 unique receptors.

Prevent Renaming ImmunData via dimnames

Description

Disallows replacing dimension names on ImmunData.

Usage

## S3 replacement method for class 'ImmunData'
dimnames(x) <- value

Arguments

Get Annotation Dimnames from ImmunData

Description

Returns dimension names for an ImmunData object so that colnames(idata) maps to annotation column names.

Usage

## S3 method for class 'ImmunData'
dimnames(x)

Arguments

Value

A list with NULL row names and annotation column names.

Reduce repertoires to a common sampling depth

Description

Use downsample_immundata() to reduce every repertoire to the same number or fraction of observed cells or bulk sequence counts before comparing repertoires. So, it is just a downsampling.

Use this function when different sequencing depths could affect a comparison of repertoire diversity or composition. In single-cell data, the sampling unit is a cell barcode and all selected chains from that cell stay together. In bulk data with abundance values, the sampling unit is one sequence count.

The function returns a new ImmunData object. The original object is not changed.

Usage

downsample_immundata(idata, n, seed = NULL)

Arguments

Value

A new ImmunData object containing the sampled chain observations. If the input has repertoires or strata, their summaries are recalculated for the sampled data.

Meaning of n for single-cell data

• ⁠0 < n < 1⁠ keeps ⁠floor(n * number of cells)⁠ cells from each repertoire.

• n >= 1 keeps n cells from each repertoire.

Cell barcodes are sampled without replacement. For paired receptors, all retained chains belonging to a selected cell stay together.

Meaning of n for bulk data

• ⁠0 < n < 1⁠ keeps ⁠floor(n * total abundance)⁠ sequence counts from each repertoire.

• n >= 1 keeps a total abundance of n from each repertoire.

Counts are sampled without replacement according to their observed abundance. A retained receptor can therefore have a smaller abundance than it had before downsampling. For example, n = 1000 makes the total retained abundance equal to 1000 in every repertoire that originally contained at least 1000 counts.

If a requested whole-number n is larger than a repertoire, that repertoire is returned unchanged and the function gives a warning. If a fraction is so small that it selects zero units in any repertoire, the function stops and asks for a larger value.

Repertoire and strata summaries

When repertoires are defined, the function recalculates receptor counts, proportions, repertoire sizes, and the number of repertoires containing each receptor. Existing strata are also rebuilt, and their labels are retained. When repertoires are not defined, the complete dataset is treated as one sampling group and no repertoire summary is added.

Backend and storage

Chain-level selection and reconstruction use the duckplyr annotation table. The small table of sampling units is collected into R for random sampling. The function does not overwrite the stored input object. Use write_immundata() to save the returned object.

See Also

agg_repertoires(), filter_immundata(), write_immundata()

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Create two small bulk T-cell repertoires with different total abundances.
bulk_data <- tibble(
  Sample = c("Tumor", "Tumor", "Blood", "Blood"),
  cdr3_aa = c("CASSA", "CASSB", "CASSA", "CASSC"),
  v_call = c("TRBV1", "TRBV2", "TRBV1", "TRBV3"),
  abundance = c(20L, 5L, 4L, 6L)
)
bulk_file <- tempfile(fileext = ".tsv")
readr::write_tsv(bulk_data, bulk_file)

idata <- read_repertoires(
  path = bulk_file,
  schema = c("cdr3_aa", "v_call"),
  count_col = "abundance",
  repertoire_schema = "Sample",
  preprocess = NULL,
  postprocess = NULL,
  rename_columns = NULL,
  output_folder = tempfile("immundata-downsample-")
)

before <- idata$repertoires |>
  select(Sample, n_barcodes) |>
  rename(before = n_barcodes)

sampled <- downsample_immundata(idata, n = 5, seed = 42)

before |>
  left_join(
    sampled$repertoires |>
      select(Sample, n_barcodes) |>
      rename(after = n_barcodes),
    by = "Sample"
  ) |>
  arrange(Sample)
# Expected result:
#   Sample before after
#   Blood      10     5
#   Tumor      25     5

# Each returned repertoire has five sequence counts. `idata` still has its
# original repertoire sizes of 10 and 25.

Keep selected rows or receptors in ImmunData

Description

Use filter() to keep selected rows in an ImmunData object. For example, you can keep rows from one response group, rows using a selected V gene, or receptors containing a CDR3 sequence similar to a reference sequence.

The function returns a new ImmunData object. The original object is not changed.

This function is a direct implementation of dplyr::filter. Alternative function name is filter_immundata.

Use filter_barcodes() to keep selected cell barcodes and filter_receptors() to keep selected receptor identifiers.

Usage

filter_immundata(idata, ..., seq_options = NULL, keep_repertoires = TRUE)

## S3 method for class 'ImmunData'
filter(
  .data,
  ...,
  .by = NULL,
  .preserve = FALSE,
  seq_options = NULL,
  keep_repertoires = TRUE
)

filter_barcodes(idata, barcodes, keep_repertoires = TRUE)

filter_receptors(idata, receptors, keep_repertoires = TRUE)

Arguments

Details

You can filter an ImmunData object in three ways:

• Supply conditions in ... to filter using annotation columns. Refer to columns directly by name. For example, Response == "FR" keeps rows from the FR response group.

• Supply seq_options, created with make_seq_options(), to find receptors containing a sequence that matches one or more reference sequences or patterns.

• Use filter_barcodes() or filter_receptors() when you already have the identifiers that you want to keep.

Conditions in ... are applied before sequence matching. Sequence matching then identifies receptors from the remaining rows. When one chain matches, all remaining chains belonging to the same receptor are kept. A chain removed by a condition in ... is not added back by sequence matching.

Sequence matching methods are:

• "exact": the sequence must be identical to one of the references.

• "regex": the sequence must match a regular-expression pattern. This is an advanced option for matching text patterns.

• "lev": the Levenshtein distance counts the substitutions, insertions, or deletions needed to change one sequence into the other.

• "hamm": the Hamming distance counts different positions between sequences of the same length. Sequences of different lengths do not match.

For "lev" and "hamm", provide max_dist. A sequence is accepted when its distance from at least one reference is less than or equal to this value. A distance of 0 means an exact match, and smaller values mean more similar sequences.

By default, existing repertoire summaries are recalculated from the filtered data. Existing strata are also rebuilt, and their labels are retained. Set keep_repertoires = FALSE to return an object without repertoire or strata summaries.

Value

A new ImmunData object containing the selected rows and receptors. If requested, repertoire and strata summaries are recalculated for the selected data.

See Also

dplyr::filter(), make_seq_options(), mutate_immundata(), agg_repertoires(), ImmunData

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Load data included with immundata
idata <- get_test_idata()

# Keep rows from one response group
fr_response <- idata |>
  filter(Response == "FR")

fr_response |>
  collect() |>
  summarise(
    n_rows = n(),
    n_receptors = n_distinct(imd_receptor_id)
  )
# Expected result:
#   n_rows n_receptors
#      955         871

# Keep receptors containing one reference CDR3 sequence
reference_cdr3 <- "ASFPVLSPYNEQF"

exact_match <- idata |>
  filter(
    seq_options = make_seq_options(
      query_col = "cdr3_aa",
      patterns = reference_cdr3,
      method = "exact"
    )
  )

exact_match |>
  collect() |>
  select(cdr3_aa, v_call, Response)
# Expected result:
#   cdr3_aa       v_call    Response
#   ASFPVLSPYNEQF TRBV28*01 FR

# Keep receptors within four sequence changes of the reference
similar_sequences <- idata |>
  filter(
    seq_options = make_seq_options(
      query_col = "cdr3_aa",
      patterns = reference_cdr3,
      method = "lev",
      max_dist = 4
    )
  )

similar_sequences |>
  collect() |>
  distinct(cdr3_aa) |>
  arrange(cdr3_aa)
# Expected result:
#   cdr3_aa
#   ASFPVLSPYNEQF
#   ASSPDSPSYNEQF
#   ASSPGLAAYNEQF
#   ASSPTLYNEQF

# Keep two selected cell barcodes
selected_barcodes <- c("S1_1", "S1_2")

selected_cells <- idata |>
  filter_barcodes(selected_barcodes)

selected_cells |>
  collect() |>
  distinct(imd_barcode)
# Expected result:
#   imd_barcode
#   S1_1
#   S1_2

# The same approach can keep selected receptor identifiers
selected_receptors <- idata |>
  collect() |>
  distinct(imd_receptor_id) |>
  slice_head(n = 2) |>
  pull(imd_receptor_id)

selected_receptors_data <- idata |>
  filter_receptors(selected_receptors)

Convert an immunarch Object into an ImmunData Dataset

Description

The from_immunarch() function takes an immunarch object (as returned by immunarch::repLoad()), writes each repertoire to a TSV file with an added internal filename column in a specified folder, and then imports those files into an ImmunData object via read_repertoires().

Usage

from_immunarch(
  imm,
  output_folder,
  schema = c("CDR3.aa", "V.name"),
  temp_folder = file.path(tempdir(), "temp_folder")
)

Arguments

Value

An ImmunData object containing all repertoires from the input immunarch object, with data saved under output_folder.

See Also

read_repertoires(), read_immundata(), ImmunData

Examples

## Not run: 
imm <- immunarch::repLoad("/path/to/your/files")
idata <- from_immunarch(imm,
  schema = c("CDR3.aa", "V.name"),
  temp_folder = tempdir(),
  output_folder = "/path/to/immundata_out"
)

## End(Not run)

Get test datasets from immundata

Description

Get test datasets from immundata

Usage

get_test_idata()

Get input-column presets

Description

Use these helpers to inspect or customize the column renaming and removal presets used by read_repertoires().

imd_rename_cols() returns mappings from standard output names to source names. imd_drop_cols() returns technical columns that can usually be removed before receptors are defined. These functions return definitions only; they do not change input files or an ImmunData object.

Usage

imd_rename_cols(format = "default")

imd_drop_cols(format = "airr")

Arguments

Value

imd_rename_cols() returns a named character vector in the form c(new_name = "source_name"). imd_drop_cols() returns a character vector of source columns to remove.

See Also

read_repertoires(), make_default_preprocessing(), imd_schema()

Examples

imd_rename_cols("10x")
# Includes c(v_call = "v_gene", locus = "chain").

head(imd_drop_cols("10x"), 3)
# Expected result:
#   "full_length" "is_cell" "contig_id"

# Keep the 10x `contig_id` column while dropping the other default columns.
columns_to_drop <- setdiff(imd_drop_cols("10x"), "contig_id")
custom_preprocessing <- list(
  exclude_columns = make_exclude_columns(columns_to_drop),
  filter_nonproductive = make_productive_filter(
    truthy = c("TRUE", "true", "1")
  )
)

Get a standard ImmunData column name

Description

Use imd_schema() when code needs the standard column name for an ImmunData identifier or calculated value, such as the cell barcode, receptor identifier, repertoire identifier, count, or proportion.

Use this helper in reusable analysis code or package extensions instead of writing an internal name such as "imd_barcode" directly. It only returns names; it does not inspect or change an ImmunData object.

Usage

imd_schema(key = NULL)

Arguments

Value

If key is supplied, one character string containing the standard column name. If key = NULL, a named list of all schema keys and column names.

See Also

make_receptor_schema(), imd_rename_cols(), ImmunData

Examples

imd_schema("barcode")
# Expected result: "imd_barcode"

imd_schema("receptor")
# Expected result: "imd_receptor_id"

# Use a returned name for programmatic selection.
barcode_column <- imd_schema("barcode")
get_test_idata() |>
  dplyr::collect() |>
  dplyr::select(dplyr::all_of(barcode_column)) |>
  head(2)

Developer helpers for ImmunData schemas and storage

Description

These helpers expose package constants for extension developers. They are not needed for routine biological analysis. Use imd_schema() for a standard column name and make_receptor_schema() to define biological receptors.

The functions remain exported for compatibility with packages that extend immundata, but their values describe implementation details and may grow as the storage format develops.

Usage

imd_schema_sym(key = NULL)

imd_meta_schema()

imd_files()

imd_repertoire_schema(format = "airr")

imd_receptor_features(schema)

imd_receptor_chains(schema)

Arguments

Value

• imd_schema_sym() returns an rlang symbol for one standard column. With key = NULL, it returns the complete named schema list.

• imd_meta_schema() returns a named list of fields used in metadata.json.

• imd_files() returns a named list of standard snapshot file names.

• imd_repertoire_schema() returns the configured preset for format, or NULL when no preset is configured.

• imd_receptor_features() returns the character vector in schema$features.

• imd_receptor_chains() returns the character vector in schema$chains, or NULL for a chain-agnostic schema.

Examples

schema <- make_receptor_schema(
  features = c("junction_aa", "v_call"),
  chains = c("TRA", "TRB")
)

imd_receptor_features(schema)
# Expected result: c("junction_aa", "v_call")

imd_receptor_chains(schema)
# Expected result: c("TRA", "TRB")

imd_files()
# Lists the standard metadata and Parquet file names.

Process chain rows while reading repertoire files

Description

Use these functions to preprocess or postprocess rows of the input data before returning the final ImmunData object to the session. A couple of example use cases: keep productive receptor chains, remove technical columns, or make cell barcodes unique while importing repertoire files with read_repertoires().

The defaults provide steps for common AIRR or 10x inputs. Use an individual step when your files need only one operation or when you are building a custom preprocess or postprocess list.

Preprocessing changes chain rows before receptors are defined. Barcode prefixing changes the cell identifier after receptor and manifest information are combined. The input files and input table are not changed: every step returns a new duckplyr table.

Usage

make_default_preprocessing(format = c("default", "airr", "10x"))

make_default_postprocessing()

make_exclude_columns(cols = imd_drop_cols("airr"))

make_productive_filter(col_name = c("productive"), truthy = TRUE)

make_barcode_prefix(prefix_col = "Prefix")

Arguments

Value

make_default_preprocessing() and make_default_postprocessing() return named lists of processing functions. The other functions return one processing function. Each processing function accepts a duckplyr table as its first argument, accepts unused arguments through ..., and returns a new duckplyr table.

Choose processing steps

• make_default_preprocessing() returns two steps. The first removes common technical columns. The second keeps rows whose productive value indicates a productive chain. If the productive column is absent, the filtering step gives a warning and keeps all rows.

• make_default_postprocessing() returns one step that adds a sample-specific prefix to cell barcodes. If the prefix column is absent, the step gives a warning and leaves barcodes unchanged.

• make_exclude_columns() creates one step that removes the columns in cols. Column names that are not present are ignored.

• make_productive_filter() creates one step that keeps rows whose value in col_name matches any value in truthy.

• make_barcode_prefix() creates one step that joins a prefix, such as "Tumor_", to the start of each imd_barcode value.

read_repertoires() applies functions in list order. You can therefore add, remove, or reorder steps in a custom list.

Input formats

For make_default_preprocessing(), format = "default" removes the union of the standard AIRR and 10x technical columns. Use format = "airr" or format = "10x" to remove only the columns expected for that format. All three defaults recognize common text representations of a productive value, including "TRUE", "true", "yes", and "1".

See Also

read_repertoires(), imd_drop_cols(), imd_rename_cols()

Examples

library(immundata)
library(dplyr)

# Three 10x chain rows from two samples. One chain is non-productive.
chains <- duckplyr::duckdb_tibble(
  imd_barcode = c("AAAC-1", "AAAG-1", "AATT-1"),
  cdr3_aa = c("CASSA", "CASSB", "CASSC"),
  productive = c("TRUE", "FALSE", "TRUE"),
  full_length = c(TRUE, TRUE, TRUE),
  Prefix = c("Tumor_", "Tumor_", "Blood_")
)

# read_repertoires() performs these calls for you. They are shown here to
# make the effect of each list clear.
prepared <- Reduce(
  function(data, step) step(data),
  make_default_preprocessing("10x"),
  init = chains
)
prepared <- Reduce(
  function(data, step) step(data),
  make_default_postprocessing(),
  init = prepared
)

prepared |>
  collect() |>
  select(imd_barcode, cdr3_aa, productive)
# Expected result:
#   imd_barcode  cdr3_aa productive
#   Tumor_AAAC-1 CASSA   TRUE
#   Blood_AATT-1 CASSC   TRUE

# The non-productive chain was removed, `full_length` was dropped, and the
# sample prefixes made the retained cell barcodes unique.

Define which chain observations form the same receptor

Description

Use make_receptor_schema() to define a biological receptor from sequence features and one or two receptor chains.

Use this function when reading single-cell data with one selected chain, when pairing chains such as TRA-TRB, or when accepting alternative light chains such as IGK or IGL. The unit being defined is the receptor. Creating a schema does not change any data or an existing ImmunData object.

Usage

make_receptor_schema(features, chains = NULL)

assert_receptor_schema(schema)

test_receptor_schema(schema)

Arguments

Value

make_receptor_schema() returns a list with character elements features and chains; chains is NULL when loci are not selected. assert_receptor_schema() returns TRUE for accepted input and otherwise stops with an error. test_receptor_schema() returns one logical value.

When observations are the same receptor

features names the fields that define chain identity. Common choices are the CDR3 amino acid sequence, V gene, and J gene. Two observations represent the same receptor only when the relevant chain loci and every selected feature match.

• With one chain, such as chains = "TRB", only that locus is used. Two TRB observations are the same receptor when all their selected feature values match.

• With a strict pair, such as chains = c("TRA", "TRB"), chains are first paired within each cell barcode. Receptors from two cells are the same only when every selected TRA feature and every selected TRB feature match.

• With an alternative second chain, such as chains = c("IGH", "IGK|IGL"), each receptor must contain IGH and exactly one of IGK or IGL. Cells containing both IGK and IGL are excluded. The light-chain locus and all selected heavy- and light-chain features must match for two observations to be the same receptor.

A barcode determines which chains belong to the same cell; it does not by itself define receptor identity across cells. During single-cell import, read_repertoires() uses umi_col to choose one chain when a cell contains several observations from the same locus.

Use chains = NULL for chain-agnostic bulk or pre-filtered data. In that case, only the values in features define receptor identity.

Validate a schema

assert_receptor_schema() stops with an error if schema is not accepted. Use it inside another function when invalid input must stop the calculation. test_receptor_schema() returns one TRUE or FALSE value and is useful in conditional code.

Backend and storage

A receptor schema is a small R list containing features and chains. It stores no sequence data. read_repertoires() and agg_receptors() apply the schema to chain observations using duckplyr.

See Also

read_repertoires(), agg_receptors(), imd_schema()

Examples

# Single-chain TCR: compare TRB observations by CDR3, V gene, and J gene.
trb_schema <- make_receptor_schema(
  features = c("junction_aa", "v_call", "j_call"),
  chains = "TRB"
)
trb_schema
# Expected result:
#   $features: "junction_aa" "v_call" "j_call"
#   $chains:   "TRB"

# Paired alpha-beta TCR: all selected fields must match on both TRA and TRB.
ab_tcr_schema <- make_receptor_schema(
  features = c("junction_aa", "v_call", "j_call"),
  chains = c("TRA", "TRB")
)
ab_tcr_schema
# The result defines one receptor as a matched TRA-TRB pair from one cell.

# BCR: require IGH and accept either an IGK or IGL light chain.
bcr_schema <- make_receptor_schema(
  features = c("junction_aa", "v_call", "j_call"),
  chains = c("IGH", "IGK|IGL")
)
bcr_schema
# The result accepts IGH-IGK and IGH-IGL receptors, while keeping the two
# light-chain loci biologically distinct.

test_receptor_schema(bcr_schema)
# Expected result: TRUE

Create options for comparing receptor sequences

Description

Create sequence comparison options for the seq_options argument of filter_immundata() or mutate_immundata(). Use these options to compare a sequence column with one or more reference sequences or patterns.

Usage

make_seq_options(
  query_col,
  patterns,
  method = c("exact", "lev", "hamm", "regex"),
  max_dist = NA,
  name_type = c("index", "pattern")
)

Arguments

Value

A named list for the seq_options argument of filter_immundata() or mutate_immundata().

See Also

filter_immundata(), mutate_immundata(), annotate_receptors()

Add or change annotation columns in ImmunData

Description

Use mutate() to add information to each row of an ImmunData object. For example, you can calculate CDR3 length, mark sequences of interest, or compare receptor sequences with reference sequences.

The function returns a new ImmunData object. The original object is not changed.

This function is a direct implementation of dplyr::mutate. Alternative function name is mutate_immundata.

Usage

mutate_immundata(idata, ..., .by = NULL, seq_options = NULL)

## S3 method for class 'ImmunData'
mutate(.data, ..., .by = NULL, seq_options = NULL)

Arguments

Details

You can use mutate() in three ways:

• Supply named calculations in ... to create annotation columns from existing data. For example, cmv_specific = cdr3_aa %in% cmv_cdr3s adds a column containing TRUE or FALSE for each row.

• Supply .by to perform calculations separately for temporary groups. The number of rows does not change. A group statistic is repeated for all rows in that group.

• Supply seq_options, created with make_seq_options(), to compare a sequence column with one or more reference sequences or patterns. One result column is added for each reference.

Named calculations in ... are performed before sequence comparisons.

Most grouped calculations are translated directly to DuckDB. Some group statistics, such as n_distinct(), are not available as DuckDB window calculations when a large dataset must stay on disk. In that case, mutate() automatically calculates one summary row per group and joins the values back to the annotation rows. This remains lazy and does not load the full dataset into R memory.

The automatic fallback works when every calculation in the call produces one value per group. If a call combines a row-level calculation with a group statistic that needs the fallback, use two mutate() calls. Also use a second call when a later calculation refers to a group statistic created by the fallback. See the examples below.

Columns used to identify receptors or repertoires, and identifiers managed by ImmunData, are protected. This prevents accidental changes that would make the object inconsistent. You can add new columns and change other annotation columns.

Sequence comparison methods are:

• "exact": TRUE when the sequence is identical to the reference.

• "regex": TRUE when the sequence matches a regular-expression pattern. This is an advanced option for matching text patterns.

• "lev": the number of substitutions, insertions, or deletions needed to change one sequence into the other.

• "hamm": the number of different positions between sequences of the same length. Sequences with different lengths receive NA.

For the distance methods, 0 means an exact match and smaller values mean more similar sequences. With name_type = "index", the result columns have short names such as imd_sim_exact_1 or imd_sim_lev_1. With name_type = "pattern", each column name includes its reference pattern.

max_dist is used by filter_immundata() but has no effect here because mutate() reports every calculated distance.

Existing repertoire and strata summaries are carried to the new object without modification.

Value

A new ImmunData object containing the added or changed annotation columns. Existing repertoire and strata summaries are preserved.

See Also

dplyr::mutate(), make_seq_options(), filter_immundata(), annotate_receptors(), agg_repertoires(), ImmunData

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Load data included with immundata
idata <- get_test_idata()

# Add the length of each CDR3 amino acid sequence
idata_with_length <- idata |>
  mutate(cdr3_length = dd$length(cdr3_aa))

idata_with_length |>
  collect() |>
  select(cdr3_aa, cdr3_length) |>
  slice_head(n = 3)
# Expected result:
#   cdr3_aa       cdr3_length
#   ASFPVLSPYNEQF          13
#   ASRAGAGTGELF           12
#   ASSPGQGLDTQY           12

# Compare CDR3 sequences with one reference sequence
reference_cdr3 <- "ASFPVLSPYNEQF"

idata_with_matches <- idata |>
  mutate(
    seq_options = make_seq_options(
      query_col = "cdr3_aa",
      patterns = reference_cdr3,
      method = "exact"
    )
  )

idata_with_matches |>
  collect() |>
  count(imd_sim_exact_1)
# Expected result:
#   imd_sim_exact_1     n
#   FALSE            1901
#   TRUE                1

# Calculate Levenshtein distance from the reference sequence
idata_with_distance <- idata |>
  mutate(
    seq_options = make_seq_options(
      query_col = "cdr3_aa",
      patterns = reference_cdr3,
      method = "lev"
    )
  )

idata_with_distance |>
  collect() |>
  select(cdr3_aa, imd_sim_lev_1) |>
  arrange(imd_sim_lev_1, cdr3_aa) |>
  slice_head(n = 3)
# Expected result:
#   cdr3_aa       imd_sim_lev_1
#   ASFPVLSPYNEQF             0
#   ASSPDSPSYNEQF             4
#   ASSPGLAAYNEQF             4

# Mark selected sequences
cmv_cdr3s <- c(
  "ASFPVLSPYNEQF",
  "ASRAGAGTGELF"
)

marked_sequences <- idata |>
  mutate(
    cmv_specific = cdr3_aa %in% cmv_cdr3s
  )

marked_sequences |>
  collect() |>
  count(cmv_specific)
# Expected result:
#   cmv_specific     n
#   FALSE         1900
#   TRUE             2

# Mark selected receptor identities
cmv_hits <- tibble(
  imd_receptor_id = c(1L, 105L),
  cmv_specific = TRUE
)

marked_receptors <- idata |>
  annotate_receptors(cmv_hits) |>
  mutate(
    cmv_specific = coalesce(cmv_specific, FALSE)
  )

marked_receptors |>
  collect() |>
  count(cmv_specific)
# Expected result:
#   cmv_specific     n
#   FALSE         1898
#   TRUE             4

# Add response-level statistics to every annotation row
# `.by` means: calculate separately for each response.
response_stats <- idata |>
  mutate(
    response_n_rows = n(),
    response_n_receptors = n_distinct(imd_receptor_id),
    .by = Response
  )

response_stats |>
  collect() |>
  distinct(Response, response_n_rows, response_n_receptors) |>
  arrange(Response)
# Expected result:
#   Response response_n_rows response_n_receptors
#   FR                   955                  871
#   PR                   947                  867

# A grouped calculation can also produce a different value for every row.
response_centered <- idata |>
  mutate(
    centered_counts = counts - mean(counts, na.rm = TRUE),
    .by = Response
  )

# Do not combine that row-level calculation with a statistic that needs the
# automatic summary fallback in the same call:
# idata |>
#   mutate(
#     centered_counts = counts - mean(counts, na.rm = TRUE),
#     response_n_receptors = n_distinct(imd_receptor_id),
#     .by = Response
#   )

# Use two mutate calls instead. The work remains lazy in DuckDB.
response_details <- idata |>
  mutate(
    centered_counts = counts - mean(counts, na.rm = TRUE),
    .by = Response
  ) |>
  mutate(
    response_n_receptors = n_distinct(imd_receptor_id),
    .by = Response
  )

# Also use a second call when a new calculation uses a statistic created by
# the fallback.
response_details <- idata |>
  mutate(
    response_n_receptors = n_distinct(imd_receptor_id),
    .by = Response
  ) |>
  mutate(
    twice_response_n_receptors = response_n_receptors * 2
  )

Prevent Renaming ImmunData via names

Description

Disallows replacing names on ImmunData.

Usage

## S3 replacement method for class 'ImmunData'
names(x) <- value

Arguments

Display the contents and biological definitions of ImmunData

Description

Use print() to inspect the receptor table, chain annotations, and biological schemas stored in an ImmunData object.

Use this method for a quick overview after reading, filtering, or aggregating repertoire data. It displays the units available in the object: receptors, chain rows, repertoires, and strata. It also shows the feature and chain definitions used to construct receptors.

Printing is read-only. It does not collect the complete dataset into R and does not change the original object. The object is returned invisibly so it can still be assigned or used in a pipeline.

Usage

## S3 method for class 'ImmunData'
print(x, ...)

Arguments

Details

A section is shown only when that information is available. An object without repertoire definitions, for example, has no repertoire schema or repertoire summary section. Duckplyr prints a preview of large tables rather than every row.

Value

x, invisibly. The displayed output is a human-readable overview; no data are modified.

See Also

ImmunData, dplyr::collect(), dplyr::count()

Examples

library(immundata)

options(immundata.verbose = FALSE)
idata <- get_test_idata()

print(idata)
# Expected output contains these sections:
#   ImmunData
#   Receptors
#   Annotations
#   Receptor schema
#   Repertoire schema
#   List of repertoires

# `Receptors` previews distinct biological receptor definitions.
# `Annotations` previews the retained chain rows and sample information.
# The schema sections explain how receptors and repertoires were defined.

Load an ImmunData object from disk

Description

Continue an analysis later by reopening an ImmunData dataset saved on disk. Use read_immundata() after restarting R, in another script, or when another person gives you a dataset created by write_immundata() or read_repertoires(). It is that simple, just don't forget to save the ImmunData object first!

The unit restored retains all information: chain rows, cell and receptor identifiers, repertoire and stratum definitions, and provenance. The function does not change these biological units or the saved files. It returns a new ImmunData object.

Usage

read_immundata(
  path,
  tag = NULL,
  version = NULL,
  prudence = "stingy",
  verbose = getOption("immundata.verbose", TRUE)
)

Arguments

Details

Read either a dataset directory directly or a versioned snapshot within its project home.

Value

A new, disk-backed ImmunData object representing the selected saved state. Its provenance records the directory that was read.

Choose the saved state

To reopen a dataset saved directly in a folder, supply that folder as path and leave tag and version as NULL.

To reopen a managed snapshot, supply the project home as path and its tag. By default, the latest version for that tag is read. Supply version when you need an exact earlier state.

Backend and serialized data

annotations.parquet stores the retained chain-level annotation table. It is reopened as a lazy duckplyr table, so the complete table does not need to be loaded into R memory. metadata.json stores the format and package versions, receptor, repertoire, and stratum schemas, the repertoire table, the snapshot identifier, lineage events, and provenance paths.

Receptor and stratum views are reconstructed from this serialized state; they are not stored as separate files. Please also mind, that the saved files is an ImmunData-specific serialization, not an RDS file.

See Also

write_immundata() for saving an analysis, read_repertoires() for importing AIRR-seq files, ImmunData

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Create a project home and save a filtered biological state as a snapshot
idata <- get_test_idata()
project_dir <- tempfile("immundata-project-")

project_idata <- write_immundata(
  idata,
  output_folder = project_dir,
  rehome = TRUE
)

fr_response <- project_idata |>
  filter(Response == "FR")

write_immundata(fr_response, tag = "fr-response")

# Read the exact first version of this snapshot
continued_fr <- read_immundata(
  project_dir,
  tag = "fr-response",
  version = 1
)

continued_fr |>
  collect() |>
  summarise(
    n_chains = n(),
    n_receptors = n_distinct(imd_receptor_id)
  )
# Expected result: the snapshot contains the 955 chain rows and 871
# receptors from the FR response group.
#   n_chains n_receptors
#        955         871

list.files(file.path(project_dir, "snapshots", "fr-response"))
# Expected result: "v001"

unlink(project_dir, recursive = TRUE)

Load and Validate a Manifest for Immune Repertoire Files

Description

This function loads a manifest from either a file path or a data frame, validates the presence of a column with repertoire file paths, and converts all file paths to absolute paths. It is used to support flexible pipelines for loading bulk or single-cell immune repertoire data across samples.

If the input is a file path, the function reads it with readr::read_delim. If the input is a data frame, it checks whether file paths are absolute; relative paths are only allowed when the manifest is loaded from a file.

It warns the user if many of the files listed in the manifest are missing, and stops execution if none of the files exist.

The column with file paths is normalized into the internal filename schema.

Usage

read_manifest(
  manifest,
  file_col = "file",
  delim = NULL,
  ...,
  verbose = getOption("immundata.verbose", TRUE)
)

Arguments

Value

A validated and updated manifest data frame with absolute file paths and an additional internal column named imd_filename.

Read immune repertoire files into ImmunData

Description

read_repertoires() is the main function for importing AIRR-seq data. It reads one or more repertoire files, defines biological receptors, adds sample information from an optional manifest, and returns an ImmunData object.

The function saves the processed data in output_folder. This lets you work with large datasets without loading everything into memory and reopen the result later with read_immundata().

Usage

read_repertoires(
  path,
  schema,
  manifest = NULL,
  barcode_col = NULL,
  count_col = NULL,
  locus_col = NULL,
  umi_col = NULL,
  preprocess = make_default_preprocessing(),
  postprocess = make_default_postprocessing(),
  rename_columns = imd_rename_cols("10x"),
  enforce_schema = TRUE,
  manifest_file_col = "file",
  output_folder = NULL,
  repertoire_schema = "<auto>",
  verbose = getOption("immundata.verbose", TRUE),
  prematerialize = TRUE,
  prematerialize_folder = NULL
)

Arguments

Details

The required arguments depend on how receptor observations are represented in the input files.

Value

A disk-backed ImmunData object containing the retained chain rows, receptor definitions, manifest annotations, and ingestion provenance. If repertoire_schema is not NULL, it also contains repertoire definitions and summary statistics calculated by agg_repertoires().

Choose arguments for your data

• Uncounted repertoire table: Supply schema. Leave barcode_col and count_col as NULL. Each retained row represents one observed chain.

• Bulk repertoire with abundance: Supply schema and count_col. The abundance values are preserved for later repertoire statistics.

• Single-cell, one selected chain: Use make_receptor_schema() with one chain and supply barcode_col, locus_col, and umi_col.

• Single-cell, paired chains: Use make_receptor_schema() with two chains and supply barcode_col, locus_col, and umi_col. Only cells containing both requested chains are retained.

• Single-cell, relaxed paired chains: Use a schema such as chains = c("IGH", "IGL|IGK") with barcode_col, locus_col, and umi_col. This accepts either an IGH-IGL or IGH-IGK receptor.

In single-cell data, the chain with the highest umi_col value is retained when a cell contains several chains from the same locus.

What happens by default

Unless you override the relevant arguments, read_repertoires():

• temporarily combines text input into Parquet before processing;

• standardizes common 10x column names;

• removes selected technical columns;

• keeps productive sequences when productivity information is present;

• prefixes barcodes when a manifest Prefix column is present;

• creates repertoires automatically; and

• writes the completed dataset to disk.

Set rename_columns, preprocess, postprocess, or repertoire_schema to NULL to disable the corresponding behavior.

Processing order

The function:

1. finds and reads the input files as one duckplyr table;

2. temporarily combines non-Parquet input into one Parquet file when prematerialize = TRUE;

3. renames columns;

4. applies preprocessing;

5. defines receptors using schema;

6. adds manifest information;

7. applies postprocessing;

8. defines repertoires when requested; and

9. writes and reopens the completed ImmunData dataset.

Manifests and repertoires

A manifest annotates each input file with biological information. The repertoire_schema argument chooses which annotation columns define a repertoire and therefore determine receptor counts and proportions.

With path = "<manifest>" and the default repertoire_schema = "<auto>", all manifest columns are used and each manifest row becomes one repertoire. With an explicit file path or vector of paths, "<auto>" creates one repertoire per input file.

Output storage

The output folder is not a temporary cache. The returned object reads its receptor annotations from annotations.parquet, while metadata.json stores its schemas, repertoire summaries, and provenance. Keep this folder for as long as you need the object, or reopen it later with read_immundata().

Important: Reusing the same output_folder replaces the existing annotations.parquet and metadata.json without creating a new version.

See Also

read_manifest(), make_receptor_schema(), agg_receptors(), agg_repertoires(), make_default_preprocessing(), make_default_postprocessing(), read_immundata(), write_immundata(), ImmunData

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Read one bulk AIRR file and preserve its abundance column
bulk_file <- system.file(
  "extdata/tsv",
  "sample_0_1k.tsv",
  package = "immundata"
)

bulk_idata <- read_repertoires(
  path = bulk_file,
  schema = c("cdr3_aa", "v_call"),
  count_col = "counts",
  output_folder = tempfile("immundata-bulk-")
)

tibble(
  n_records = bulk_idata |> count() |> pull(n),
  n_receptors = bulk_idata$receptors |> count() |> collect() |> pull(n),
  n_repertoires = nrow(bulk_idata$repertoires)
)
# Expected result:
#   n_records n_receptors n_repertoires
#         955         871             1

# Read multiple files and their sample information from a manifest
manifest_path <- system.file(
  "extdata/tsv",
  "manifest.csv",
  package = "immundata"
)
manifest <- read_manifest(manifest_path)

manifest_idata <- read_repertoires(
  path = "<manifest>",
  manifest = manifest,
  schema = c("cdr3_aa", "v_call"),
  count_col = "counts",
  output_folder = tempfile("immundata-manifest-")
)

manifest_idata$repertoires |>
  select(Therapy, Response, n_barcodes, n_receptors) |>
  arrange(Response)
# Expected result:
#   Therapy Response n_barcodes n_receptors
#   ICI     FR             4725         871
#   CAR-T   PR             4758         867

# Read paired TRA-TRB receptors from a small single-cell table
paired_input <- tibble(
  cell_id = c("cell1", "cell1", "cell2", "cell2", "cell3"),
  locus = c("TRA", "TRB", "TRA", "TRB", "TRA"),
  v_call = c("TRAV1", "TRBV1", "TRAV1", "TRBV1", "TRAV2"),
  j_call = c("TRAJ1", "TRBJ1", "TRAJ1", "TRBJ1", "TRAJ2"),
  junction_aa = c("CAVA", "CASSB", "CAVA", "CASSB", "CAVC"),
  umi_count = c(10L, 8L, 12L, 9L, 7L)
)
paired_file <- tempfile(fileext = ".tsv")
readr::write_tsv(paired_input, paired_file)

paired_idata <- read_repertoires(
  path = paired_file,
  schema = make_receptor_schema(
    features = c("v_call", "j_call", "junction_aa"),
    chains = c("TRA", "TRB")
  ),
  barcode_col = "cell_id",
  locus_col = "locus",
  umi_col = "umi_count",
  repertoire_schema = NULL,
  output_folder = tempfile("immundata-paired-")
)

tibble(
  n_chains = paired_idata |> count() |> pull(n),
  n_cells = paired_idata |> collect() |> distinct(imd_barcode) |> nrow(),
  n_receptors = paired_idata$receptors |> count() |> collect() |> pull(n)
)
# Expected result:
#   n_chains n_cells n_receptors
#          4       2           1

Give biological strata readable labels

Description

Use rename_strata() to replace automatic stratum labels with names that are clear in figures and result tables, such as "Control", "Treated", or "Tumour tissue".

Use this function after agg_strata() when labels such as "Strata1" do not describe the biological groups. The unit being changed is the stratum label. Stratum membership and the repertoires, receptors, cells, and chains remain unchanged.

The function returns a new ImmunData object. The original object is not changed.

Usage

rename_strata(
  idata,
  names,
  unnamed = c("error", "auto", "keep"),
  auto_prefix = "Strata"
)

Arguments

Details

The names of a named character vector are the stratum IDs, not the current labels. Inspect idata$strata to find the ID for each biological group.

The mapping cannot contain unknown or repeated IDs, and the resulting labels must be unique across strata.

Value

A new ImmunData object with the requested labels in its ⁠$strata⁠ and ⁠$repertoires⁠ tables. All biological group assignments and repertoire summaries are preserved.

Storage details

The readable strata_name is stored in the repertoire and strata tables. The underlying chain annotations keep only imd_strata_id, so renaming a stratum does not rewrite or regroup chain-level data.

See Also

agg_strata(), agg_repertoires()

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Create treatment strata for the sample repertoires in the test data
treatment_groups <- get_test_idata() |>
  agg_repertoires(c("Response", "Therapy")) |>
  agg_strata(schema = "Therapy")

# Replace automatic labels with names suitable for a figure
labeled_groups <- treatment_groups |>
  rename_strata(
    names = c("1" = "CAR-T arm", "2" = "ICI arm")
  )

labeled_groups$strata |>
  select(Therapy, imd_strata_id, strata_name) |>
  arrange(imd_strata_id)
# Expected result:
#   Therapy imd_strata_id strata_name
#   CAR-T               1 CAR-T arm
#   ICI                 2 ICI arm

# Only the labels changed. Each sample repertoire remains in the same
# treatment stratum.

Save an ImmunData object to disk

Description

Save ImmunData to disk so you can close R and continue the work later (I cannot believe it, but it works, I tried it). Use write_immundata() after importing or transforming repertoire data, or when you want a named snapshot before the next analysis step.

The unit saved is the complete ImmunData object. This includes retained chain rows, cell and receptor identifiers, repertoire and stratum definitions, and provenance. Saving does not add, remove, or change any biological unit.

Usage

write_immundata(
  idata,
  output_folder = NULL,
  tag = NULL,
  rehome = FALSE,
  compression = "zstd",
  compression_level = 9,
  verbose = getOption("immundata.verbose", TRUE)
)

Arguments

Details

Save to an explicit folder for a direct saved state, or use the object's home to create a versioned managed snapshot.

Value

Invisibly returns a newly reopened, disk-backed ImmunData object with provenance for the new save. The input idata remains unchanged.

Choose how to save

Supply output_folder to save a standalone state in a specific directory. This is useful when sharing a dataset or choosing its first project home. If the directory already contains an ImmunData dataset, its annotations.parquet and metadata.json are replaced.

Leave output_folder = NULL to create a managed snapshot. The function uses the object's home path and writes the next version under ⁠snapshots/<tag>/vNNN⁠, for example snapshots/baseline/v001. Later writes with the same tag create v002, v003, and so on; earlier versions remain available. Use read_immundata() with tag and version to reopen one.

Every save receives a new snapshot identifier and appends a provenance event. The returned object records the new saved directory as its current path.

Backend and serialization

The retained chain-level annotation table is materialized as compressed annotations.parquet. Materialization executes any pending lazy duckplyr calculations. metadata.json serializes format and package versions, receptor, repertoire, and stratum schemas, the small repertoire table, the snapshot identifier, lineage events, and provenance paths.

Receptor and stratum views are not written as separate files; they can be reconstructed from the annotation table and metadata. This Parquet and JSON pair is an ImmunData-specific serialization, not an RDS file.

See Also

read_immundata() for continuing a saved analysis, read_repertoires() for importing AIRR-seq files, ImmunData

Examples

library(immundata)
library(dplyr)

options(immundata.verbose = FALSE)

# Save a small immune-repertoire analysis
idata <- get_test_idata()
save_dir <- tempfile("saved-immundata-")

saved_idata <- write_immundata(idata, save_dir)

list.files(save_dir)
# Expected result: the analysis is serialized as two files.
# [1] "annotations.parquet" "metadata.json"

# Continue the analysis from the saved files
continued_idata <- read_immundata(save_dir)

continued_idata |>
  collect() |>
  summarise(
    n_chains = n(),
    n_receptors = n_distinct(imd_receptor_id)
  )
# Expected result: all 1,902 chain rows and 1,668 receptors are restored.
#   n_chains n_receptors
#       1902        1668

unlink(save_dir, recursive = TRUE)