Package 'dandelionR' reference manual

Title:	Single-cell Immune Repertoire Trajectory Analysis in R
Description:	dandelionR is an R package for performing single-cell immune repertoire trajectory analysis, based on the original python implementation. It provides the necessary functions to interface with scRepertoire and a custom implementation of an absorbing Markov chain for pseudotime inference, inspired by the Palantir Python package.
Authors:	Jiawei Yu [aut] , Nicholas Borcherding [aut] , Kelvin Tuong [aut, cre]
Maintainer:	Kelvin Tuong <[email protected]>
License:	MIT + file LICENSE
Version:	0.99.10
Built:	2025-02-22 03:30:16 UTC
Source:	https://github.com/bioc/dandelionR

Example AIRR Dataset for V(D)J Analysis

Description

The demo_airr object is a list of AIRR data frames from a down-sampled demo dataset derived from Suo et al., 2024, Nature Biotechnology.
This dataset is used in vignettes to demonstrate workflows for V(D)J analysis.
For details, see the original publication at https://www.nature.com/articles/s41587-023-01734-7.
The original files are available at https://github.com/zktuong/dandelion-demo-files.

Usage

data(demo_airr)
data(demo_airr)

Format

A SingleCellExperiment object with the following slots:

list: List of DataFrames containing the standardised AIRR data for each sample.
For information of AIRR rearrangements, see the AIRR Community standards at https://docs.airr-community.org/.

Source

Suo et al., 2024, Nature Biotechnology.
https://www.nature.com/articles/s41587-023-01734-7.

Examples

data(demo_airr)
data(demo_airr)

Example SCE Dataset that does not contain V(D)J information

Description

The demo_sce object is a down-sampled demo dataset derived from Suo et al., 2024, Nature Biotechnology.
This dataset is used in vignettes to demonstrate workflows for V(D)J analysis.
For details, see the original publication at https://www.nature.com/articles/s41587-023-01734-7. The original Lymphoid cells data in h5ad format is available at https://developmental.cellatlas.io/fetal-immune.

Usage

data(demo_sce)
data(demo_sce)

Format

A SingleCellExperiment object with the following slots:

colData

A minimall DataFrame containing metadata about each sample, corresponding to obs in AnnData (Python). The following columns are relevant for vignette usage:

anno_lvl_2_final_clean: Cell type annotations.

int_colData

A DataFrame containing additional assay metadata important for further analysis. Includes:

X_scvi: A dimensionality reduction matrix from the scVI model.
UMAP: A UMAP reduction matrix.

Source

Suo et al., 2024, Nature Biotechnology.
https://www.nature.com/articles/s41587-023-01734-7.

Examples

data(demo_sce)
data(demo_sce)

Compute Branch Probabilities Using Markov Chain

Description

This function calculates branch probabilities for differentiation trajectories based on a Markov chain constructed from waypoint data and pseudotime ordering.

Usage

differentiationProbabilities(
  wp_data,
  terminal_states = NULL,
  knn = 30L,
  pseudotime,
  waypoints,
  verbose = TRUE
)
differentiationProbabilities(
  wp_data,
  terminal_states = NULL,
  knn = 30L,
  pseudotime,
  waypoints,
  verbose = TRUE
)

Arguments

`wp_data`	A multi-scale data matrix or data frame representing the waypoints.
`terminal_states`	Integer vector. Indices of the terminal states. Default is `NULL`.
`knn`	Integer. Number of nearest neighbors for graph construction. Default is `30L`.
`pseudotime`	Numeric vector. Pseudotime ordering of cells.
`waypoints`	Integer vector. Indices of selected waypoints used to construct the Markov chain.
`verbose`	Boolean, whether to print messages/warnings.

Value

A numeric matrix or data frame containing branch probabilities for each waypoint.

Markov Chain Construction and Probability Calculation

Description

This function preprocesses data, constructs a Markov chain, and calculates transition probabilities based on pseudotime information.

Usage

markovProbability(
  milo,
  diffusionmap,
  terminal_state = NULL,
  root_cell,
  knn = 30L,
  diffusiontime = NULL,
  pseudotime_key = "pseudotime",
  scale_components = TRUE,
  num_waypoints = 500,
  n_eigs = NULL,
  verbose = TRUE
)
markovProbability(
  milo,
  diffusionmap,
  terminal_state = NULL,
  root_cell,
  knn = 30L,
  diffusiontime = NULL,
  pseudotime_key = "pseudotime",
  scale_components = TRUE,
  num_waypoints = 500,
  n_eigs = NULL,
  verbose = TRUE
)

Arguments

`milo`	A `Milo` or `SingleCellExperiment` object. This object should have pseudotime stored in `colData`, which will be used to calculate probabilities. If pseudotime is available in `milo`, it takes precedence over the value provided through the `diffusiontime` parameter.
`diffusionmap`	A `DiffusionMap` object corresponding to the `milo` object. Used for Markov chain construction.
`terminal_state`	Integer. The index of the terminal state in the Markov chain.
`root_cell`	Integer. The index of the root state in the Markov chain.
`knn`	Integer. The number of nearest neighbors for graph construction. Default is `30L`.
`diffusiontime`	Numeric vector. If pseudotime is not stored in `milo`, this parameter can be used to provide pseudotime values to the function.
`pseudotime_key`	Character. The name of the column in `colData` that contains the inferred pseudotime.
`scale_components`	Logical. If `TRUE`, the components will be scaled before constructing the Markov chain. Default is `FALSE`.
`num_waypoints`	Integer. The number of waypoints to sample when constructing the Markov chain. Default is `500L`.
`n_eigs`	integer, default is NULL. Number of eigen vectors to use. If is not specified, the number of eigen vectors will be determined using the eigen gap.
`verbose`	Logical. If `TRUE`, print progress. Default is `TRUE`.

Value

milo or SinglCellExperiment object with pseudotime, probabilities in its colData

Examples

data(sce_vdj)
# downsample to first 2000 cells
sce_vdj <- sce_vdj[, 1:2000]
sce_vdj <- setupVdjPseudobulk(sce_vdj,
    already.productive = FALSE,
    allowed_chain_status = c("Single pair", "Extra pair")
)
# Build Milo Object
set.seed(100)
milo_object <- miloR::Milo(sce_vdj)
milo_object <- miloR::buildGraph(milo_object,
    k = 50, d = 20,
    reduced.dim = "X_scvi"
)
milo_object <- miloR::makeNhoods(milo_object,
    reduced_dims = "X_scvi",
    d = 20
)

# Construct Pseudobulked VDJ Feature Space
pb.milo <- vdjPseudobulk(milo_object, col_to_take = "anno_lvl_2_final_clean")
pb.milo <- scater::runPCA(pb.milo, assay.type = "Feature_space")

# Define root and branch tips
pca <- t(as.matrix(SingleCellExperiment::reducedDim(pb.milo, type = "PCA")))
branch.tips <- c(which.min(pca[, 2]), which.max(pca[, 2]))
names(branch.tips) <- c("CD8+T", "CD4+T")
root <- which.min(pca[, 1])

# Construct Diffusion Map
dm <- destiny::DiffusionMap(t(pca), n_pcs = 10, n_eigs = 5)
dif.pse <- destiny::DPT(dm, tips = c(root, branch.tips), w_width = 0.1)

# Markov Chain Construction
pb.milo <- markovProbability(
    milo = pb.milo,
    diffusionmap = dm,
    diffusiontime = dif.pse[[paste0("DPT", root)]],
    terminal_state = branch.tips,
    root_cell = root,
    pseudotime_key = "pseudotime"
)
data(sce_vdj)
# downsample to first 2000 cells
sce_vdj <- sce_vdj[, 1:2000]
sce_vdj <- setupVdjPseudobulk(sce_vdj,
    already.productive = FALSE,
    allowed_chain_status = c("Single pair", "Extra pair")
)
# Build Milo Object
set.seed(100)
milo_object <- miloR::Milo(sce_vdj)
milo_object <- miloR::buildGraph(milo_object,
    k = 50, d = 20,
    reduced.dim = "X_scvi"
)
milo_object <- miloR::makeNhoods(milo_object,
    reduced_dims = "X_scvi",
    d = 20
)

# Construct Pseudobulked VDJ Feature Space
pb.milo <- vdjPseudobulk(milo_object, col_to_take = "anno_lvl_2_final_clean")
pb.milo <- scater::runPCA(pb.milo, assay.type = "Feature_space")

# Define root and branch tips
pca <- t(as.matrix(SingleCellExperiment::reducedDim(pb.milo, type = "PCA")))
branch.tips <- c(which.min(pca[, 2]), which.max(pca[, 2]))
names(branch.tips) <- c("CD8+T", "CD4+T")
root <- which.min(pca[, 1])

# Construct Diffusion Map
dm <- destiny::DiffusionMap(t(pca), n_pcs = 10, n_eigs = 5)
dif.pse <- destiny::DPT(dm, tips = c(root, branch.tips), w_width = 0.1)

# Markov Chain Construction
pb.milo <- markovProbability(
    milo = pb.milo,
    diffusionmap = dm,
    diffusiontime = dif.pse[[paste0("DPT", root)]],
    terminal_state = branch.tips,
    root_cell = root,
    pseudotime_key = "pseudotime"
)

Perform UMAP on the Adjacency Matrix of a Milo Object

Description

This function uses uwot::umap to perform UMAP dimensionality reduction on the adjacency matrix of the KNN graph in a Milo object.

Usage

miloUmap(
  milo,
  slot_name = "UMAP_knngraph",
  n_neighbors = 50L,
  metric = "euclidean",
  min_dist = 0.3,
  ...
)
miloUmap(
  milo,
  slot_name = "UMAP_knngraph",
  n_neighbors = 50L,
  metric = "euclidean",
  min_dist = 0.3,
  ...
)

Arguments

`milo`	the milo object with knn graph that needed to conduct umap on.
`slot_name`	character, with default 'UMAP_knngraph'. The slot name in reduceDim where the result store
`n_neighbors`	integer, with default 50L. the size of local neighborhood (in terms of number of neighboring sample points) used for manifold approximation. Here, the goal is to create large enough neighborhoods to capture the local manifold structure to allow for hypersampling.
`metric`	character, with default 'euclidean' the choice of metric used to measure distance to find nearest neighbors. Default is 'euclidean'.
`min_dist`	numeric, with default 0.3 the minimum distance between points in the low dimensional space
`...`	other parameters passed to uwot::umap

Value

milo object with umap reduction

Examples

data(sce_vdj)
# downsample to just 1000 cells
sce_vdj <- sce_vdj[, 1:1000]
sce_vdj <- setupVdjPseudobulk(sce_vdj,
    already.productive = FALSE,
    allowed_chain_status = c("Single pair", "Extra pair")
)
# Build Milo Object
milo_object <- miloR::Milo(sce_vdj)
milo_object <- miloR::buildGraph(milo_object,
    k = 50, d = 20,
    reduced.dim = "X_scvi"
)
milo_object <- miloR::makeNhoods(milo_object,
    reduced_dims = "X_scvi", d = 20
)

# Construct UMAP on Milo Neighbor Graph
milo_object <- miloUmap(milo_object)

data(sce_vdj)
# downsample to just 1000 cells
sce_vdj <- sce_vdj[, 1:1000]
sce_vdj <- setupVdjPseudobulk(sce_vdj,
    already.productive = FALSE,
    allowed_chain_status = c("Single pair", "Extra pair")
)
# Build Milo Object
milo_object <- miloR::Milo(sce_vdj)
milo_object <- miloR::buildGraph(milo_object,
    k = 50, d = 20,
    reduced.dim = "X_scvi"
)
milo_object <- miloR::makeNhoods(milo_object,
    reduced_dims = "X_scvi", d = 20
)

# Construct UMAP on Milo Neighbor Graph
milo_object <- miloUmap(milo_object)

Project Probabilities from Markov Chain to Pseudobulks

Description

This function projects probabilities calculated from a Markov chain onto each pseudobulk based on a diffusion distance matrix.

Usage

projectProbability(
  diffusionmap,
  waypoints,
  probabilities,
  t = 1,
  verbose = TRUE
)
projectProbability(
  diffusionmap,
  waypoints,
  probabilities,
  t = 1,
  verbose = TRUE
)

Arguments

`diffusionmap`	diffusion map, used to reconstruct diffustion distance matrix
`waypoints`	Integer vector. Indices of the waypoints used in the Markov chain.
`probabilities`	Numeric vector. Probabilities associated with the waypoints, calculated from the Markov chain.
`t`	Numeric. The diffusion time to be used in the projection.
`verbose`	Boolean, whether to print messages/warnings.

Value

each pseudobulk's probabilites

Project Pseudotime and Branch Probabilities to Single Cells

Description

This function projects pseudotime and branch probabilities from pseudobulk data to single-cell resolution (milo). The results are stored in the colData of the milo object.

Usage

projectPseudotimeToCell(
  milo,
  pb_milo,
  term_states = NULL,
  pseudotime_key = "pseudotime",
  suffix = "",
  verbose = TRUE
)
projectPseudotimeToCell(
  milo,
  pb_milo,
  term_states = NULL,
  pseudotime_key = "pseudotime",
  suffix = "",
  verbose = TRUE
)

Arguments

`milo`	A `SingleCellExperiment` or `Milo` object. Represents single-cell data where pseudotime and branch probabilities will be projected.
`pb_milo`	A pseudobulk `Milo` object. Contains aggregated branch probabilities and pseudotime information to be transferred to single cells.
`term_states`	Named vector of terminal states, with branch probabilities to be transferred. The names should correspond to branches of interest.
`pseudotime_key`	Character. The column name in `colData` of `pb_milo` that contains the pseudotime information which was used in the `markovProbability` function. Default is `"pseudotime"`.
`suffix`	Character. A suffix to be added to the new column names in `colData`. Default is an empty string (`''`).
`verbose`	Boolean, whether to print messages/warnings.

Value

subset of milo or SingleCellExperiment object where cell that do not belong to any neighbourhood are removed and projected pseudotime information stored colData

Examples

data(sce_vdj)
# downsample to first 2000 cells
sce_vdj <- sce_vdj[, 1:2000]
sce_vdj <- setupVdjPseudobulk(sce_vdj,
    already.productive = FALSE,
    allowed_chain_status = c("Single pair", "Extra pair")
)
# Build Milo Object
set.seed(100)
milo_object <- miloR::Milo(sce_vdj)
milo_object <- miloR::buildGraph(milo_object,
    k = 50, d = 20,
    reduced.dim = "X_scvi"
)
milo_object <- miloR::makeNhoods(milo_object,
    reduced_dims = "X_scvi",
    d = 20
)

# Construct Pseudobulked VDJ Feature Space
pb.milo <- vdjPseudobulk(milo_object, col_to_take = "anno_lvl_2_final_clean")
pb.milo <- scater::runPCA(pb.milo, assay.type = "Feature_space")

# Define root and branch tips
pca <- t(as.matrix(SingleCellExperiment::reducedDim(pb.milo, type = "PCA")))
branch.tips <- c(which.min(pca[, 2]), which.max(pca[, 2]))
names(branch.tips) <- c("CD8+T", "CD4+T")
root <- which.min(pca[, 1])

# Construct Diffusion Map
dm <- destiny::DiffusionMap(t(pca), n_pcs = 10, n_eigs = 5)
dif.pse <- destiny::DPT(dm, tips = c(root, branch.tips), w_width = 0.1)

# Markov Chain Construction
pb.milo <- markovProbability(
    milo = pb.milo,
    diffusionmap = dm,
    diffusiontime = dif.pse[[paste0("DPT", root)]],
    terminal_state = branch.tips,
    root_cell = root,
    pseudotime_key = "pseudotime"
)
# Project Pseudobulk Data
projected_milo <- projectPseudotimeToCell(
    milo_object,
    pb.milo,
    branch.tips,
    pseudotime_key = "pseudotime"
)

data(sce_vdj)
# downsample to first 2000 cells
sce_vdj <- sce_vdj[, 1:2000]
sce_vdj <- setupVdjPseudobulk(sce_vdj,
    already.productive = FALSE,
    allowed_chain_status = c("Single pair", "Extra pair")
)
# Build Milo Object
set.seed(100)
milo_object <- miloR::Milo(sce_vdj)
milo_object <- miloR::buildGraph(milo_object,
    k = 50, d = 20,
    reduced.dim = "X_scvi"
)
milo_object <- miloR::makeNhoods(milo_object,
    reduced_dims = "X_scvi",
    d = 20
)

# Construct Pseudobulked VDJ Feature Space
pb.milo <- vdjPseudobulk(milo_object, col_to_take = "anno_lvl_2_final_clean")
pb.milo <- scater::runPCA(pb.milo, assay.type = "Feature_space")

# Define root and branch tips
pca <- t(as.matrix(SingleCellExperiment::reducedDim(pb.milo, type = "PCA")))
branch.tips <- c(which.min(pca[, 2]), which.max(pca[, 2]))
names(branch.tips) <- c("CD8+T", "CD4+T")
root <- which.min(pca[, 1])

# Construct Diffusion Map
dm <- destiny::DiffusionMap(t(pca), n_pcs = 10, n_eigs = 5)
dif.pse <- destiny::DPT(dm, tips = c(root, branch.tips), w_width = 0.1)

# Markov Chain Construction
pb.milo <- markovProbability(
    milo = pb.milo,
    diffusionmap = dm,
    diffusiontime = dif.pse[[paste0("DPT", root)]],
    terminal_state = branch.tips,
    root_cell = root,
    pseudotime_key = "pseudotime"
)
# Project Pseudobulk Data
projected_milo <- projectPseudotimeToCell(
    milo_object,
    pb.milo,
    branch.tips,
    pseudotime_key = "pseudotime"
)

Example Dataset for V(D)J Analysis

Description

The sce_vdj object is a down-sampled demo dataset derived from Suo et al., 2024, Nature Biotechnology.
This dataset is used in vignettes to demonstrate workflows for V(D)J analysis.
For details, see the original publication at https://www.nature.com/articles/s41587-023-01734-7.

Usage

data(sce_vdj)
data(sce_vdj)

Format

A SingleCellExperiment object with the following slots:

colData

A DataFrame containing metadata about each sample, corresponding to obs in AnnData (Python). The following columns are relevant for vignette usage:

productive_(mode)_VDJ, productive_(mode)_VJ

Factors indicating whether the heavy or light chain is productive. mode refers to the extraction mode for V(D)J genes and can be one of:

'abT': TCR alpha-beta
'gdT': TCR gamma-delta
'B': BCR

Gene segment fields

Gene segment annotations with column names in the format (v/d/j)_call_(mode)_(VDJ/VJ). Examples include:

v_call_abT_VDJ: V gene for TCR alpha-beta VDJ recombination
d_call_abT_VJ: D gene for TCR alpha-beta VJ recombination

chain_status

A factor describing the receptor chain's status.

anno_lvl_2_final_clean

Cell type annotations.

int_colData

A DataFrame containing additional assay metadata important for further analysis. Includes:

X_scvi: A dimensionality reduction matrix from the scVI model.
UMAP: A UMAP reduction matrix.

Source

Suo et al., 2024, Nature Biotechnology.
https://www.nature.com/articles/s41587-023-01734-7.

Examples

data(sce_vdj)
data(sce_vdj)

Preprocess V(D)J Data for Pseudobulk Analysis

Description

This function preprocesses single-cell V(D)J sequencing data for pseudobulk analysis. It filters data based on productivity and chain status, subsets data, extracts main V(D)J genes, and removes unmapped entries.

Usage

setupVdjPseudobulk(
  sce,
  mode_option = c("abT", "gdT", "B"),
  already.productive = TRUE,
  productive_cols = NULL,
  productive_vj = TRUE,
  productive_vdj = TRUE,
  allowed_chain_status = NULL,
  subsetby = NULL,
  groups = NULL,
  extract_cols = NULL,
  filter_unmapped = TRUE,
  check_vj_mapping = c(TRUE, TRUE),
  check_vdj_mapping = c(TRUE, FALSE, TRUE),
  check_extract_cols_mapping = NULL,
  remove_missing = TRUE,
  verbose = TRUE
)
setupVdjPseudobulk(
  sce,
  mode_option = c("abT", "gdT", "B"),
  already.productive = TRUE,
  productive_cols = NULL,
  productive_vj = TRUE,
  productive_vdj = TRUE,
  allowed_chain_status = NULL,
  subsetby = NULL,
  groups = NULL,
  extract_cols = NULL,
  filter_unmapped = TRUE,
  check_vj_mapping = c(TRUE, TRUE),
  check_vdj_mapping = c(TRUE, FALSE, TRUE),
  check_extract_cols_mapping = NULL,
  remove_missing = TRUE,
  verbose = TRUE
)

Arguments

`sce`	A `SingleCellExperiment` object. V(D)J data should be contained in `colData` for filtering.
`mode_option`	Optional character. Specifies the mode for extracting V(D)J genes. If `NULL`, `extract_cols` must be specified. Default is `NULL`.
`already.productive`	Logical. Whether the data has already been filtered for productivity. If `TRUE`, skips productivity filtering. Default is `FALSE`.
`productive_cols`	Character vector. Names of `colData` columns used for productivity filtering. Default is `NULL`.
`productive_vj`	Logical. If `TRUE`, retains cells where the main VJ chain is productive. Default is `TRUE`.
`productive_vdj`	Logical. If `TRUE`, retains cells where the main VDJ chain is productive. Default is `TRUE`.
`allowed_chain_status`	Character vector. Specifies chain statuses to retain. Valid options include`c('single pair', 'Extra pair', 'Extra pair-exception', 'Orphan VDJ', 'Orphan VDJ-exception')`. Default is `NULL`.
`subsetby`	Character. Name of a `colData` column for subsetting. Default is `NULL`.
`groups`	Character vector. Specifies the subset condition for filtering. Default is `NULL`.
`extract_cols`	Character vector. Names of `colData` columns where V(D)J information is stored, used instead of the standard columns. Default is `NULL`.
`filter_unmapped`	Logic. Whether to filter unmapped data. Default is TRUE.
`check_vj_mapping`	Logic vector. Whether to check for VJ mapping. Default is `c(TRUE, TRUE)`. If the first element is TRUE, function will filter the unmapped data in V gene of the VJ chain If the second element is TRUE, function will filter the unmapped data in J gene of the VJ chain
`check_vdj_mapping`	Logic vector. Specifies columns to check for VDJ mapping. Default is `⁠c(TRUE, FALSE, 'TRUE)⁠`. If the first element is TRUE, function will filter the unmapped data in V gene of the VDJ chain If the second element is TRUE, function will filter the unmapped data in D gene of the VDJ chain If the third element is TRUE, function will filter the unmapped data in J gene of the VDJ chain
`check_extract_cols_mapping`	Character vector. Specifies columns related to `extract_cols` for mapping checks. Default is `NULL`.
`remove_missing`	Logical. If `TRUE`, removes cells with contigs matching the filter. If `FALSE`, masks them with uniform values. Default is `TRUE`.
`verbose`	Logical. Whether to print messages. Default is `TRUE`.

Details

The function performs the following preprocessing steps:

Productivity Filtering:
- Skipped if already.productive = TRUE.
- Filters cells based on productivity using productive_cols or standard colData columns named ⁠productive_{mode_option}_{type}⁠ (where type is 'VDJ' or 'VJ').
- mode_option
  - function will check colData(s) named ⁠productive_{mode_option}_{type}⁠, where type should be 'VDJ' or 'VJ' or both, depending on values of productive_vj and productive_vdj.
  - If set as NULl, the function needs the option 'extract_cols' to be specified
- productive_cols
  - must be be specified when productivity filtering is need to conduct and mode_option is NULL.
  - where VDJ/VJ information is stored so that this will be used instead of the standard columns.
- productive_vj, productive_vdj
  - If TRUE, cell will only be kept if the main V(D)J chain is productive
Chain Status Filtering:
- Retains cells with chain statuses specified by allowed_chain_status.
Subsetting:
- Conducted only if both subsetby and groups are provided.
- Retains cells matching the groups condition in the subsetby column.
Main V(D)J Extraction:
- Uses extract_cols to specify custom columns for extracting V(D)J information.
Unmapped Data Filtering:
- decided to removes or masks cells based on filter_unmapped.
- Checks specific columns for unclear mappings using check_vj_mapping, check_vdj_mapping, or check_extract_cols_mapping.
- filter_unmapped
  - pattern to be filtered from object.
  - If is set to be NULL, the filtering process will not start
- check_vj_mapping, check_vdj_mapping
  - only colData specified by these arguments (check_vj_mapping and check_vdj_mapping) will be checked for unclear mappings
- check_extract_cols_mapping, related to extract_cols
  - Only colData specified by the argument will be checked for unclear mapping, the colData should first specified by extract_cols
- remove_missing
  - If TRUE, will remove cells with contigs matching the filter from the object.
  - If FALSE, will mask them with a uniform value dependent on the column name.

Value

filtered SingleCellExperiment object

Examples


# load data
data(sce_vdj)
# check the dimension
dim(sce_vdj)
# filtered the data
sce_vdj <- setupVdjPseudobulk(
    sce = sce_vdj,
    mode_option = "abT", # set the mode to alpha-beta TCR
    allowed_chain_status = c("Single pair", "Extra pair"),
    already.productive = FALSE
) # need to filter the unproductive cells
# check the remaining dim
dim(sce_vdj)

# load data
data(sce_vdj)
# check the dimension
dim(sce_vdj)
# filtered the data
sce_vdj <- setupVdjPseudobulk(
    sce = sce_vdj,
    mode_option = "abT", # set the mode to alpha-beta TCR
    allowed_chain_status = c("Single pair", "Extra pair"),
    already.productive = FALSE
) # need to filter the unproductive cells
# check the remaining dim
dim(sce_vdj)

Generate Pseudobulk V(D)J Feature Space

Description

This function creates a pseudobulk V(D)J feature space from single-cell data, aggregating V(D)J information into pseudobulk groups. It supports input as either a Milo object or a SingleCellExperiment object.

Usage

vdjPseudobulk(
  milo,
  pbs = NULL,
  col_to_bulk = NULL,
  extract_cols = c("v_call_abT_VDJ_main", "j_call_abT_VDJ_main", "v_call_abT_VJ_main",
    "j_call_abT_VJ_main"),
  mode_option = c("abT", "gdT", "B"),
  col_to_take = NULL,
  normalise = TRUE,
  renormalize = FALSE,
  min_count = 1L,
  verbose = TRUE
)
vdjPseudobulk(
  milo,
  pbs = NULL,
  col_to_bulk = NULL,
  extract_cols = c("v_call_abT_VDJ_main", "j_call_abT_VDJ_main", "v_call_abT_VJ_main",
    "j_call_abT_VJ_main"),
  mode_option = c("abT", "gdT", "B"),
  col_to_take = NULL,
  normalise = TRUE,
  renormalize = FALSE,
  min_count = 1L,
  verbose = TRUE
)

Arguments

`milo`	A `Milo` or `SingleCellExperiment` object containing V(D)J data.
`pbs`	Optional. A binary matrix with cells as rows and pseudobulk groups as columns. If `milo` is a `Milo` object, this parameter is not required. If `milo` is a `SingleCellExperiment` object, either `pbs` or `col_to_bulk` must be provided.
`col_to_bulk`	Optional character or character vector. Specifies `colData` column(s) to generate `pbs`. If multiple columns are provided, they will be combined. Default is `NULL`. If `milo` is a `Milo` object, this parameter is not required. If `milo` is a `SingleCellExperiment` object, either `pbs` or `col_to_bulk` must be provided.
`extract_cols`	Character vector. Specifies column names where V(D)J information is stored. Default is `⁠c('v_call_abT_VDJ_main', 'j_call_abT_VDJ_main', ' 'v_call_abT_VJ_main', 'j_call_abT_VJ_main')⁠`.
`mode_option`	Character. Specifies the mode for extracting V(D)J genes. Must be one of `c('B', 'abT', 'gdT')`. Default is `'abT'`. Note: This parameter is considered only when `extract_cols = NULL`. If `NULL`, uses column names such as `v_call_VDJ` instead of `v_call_abT_VDJ`.
`col_to_take`	Optional character or vector of characters. Specifies names of colData of milo that need to identify the most common value for each pseudobulk Default is `NULL`.
`normalise`	Logical. If `TRUE`, scales the counts of each V(D)J gene group to 1 for each pseudobulk. Default is `TRUE`.
`renormalize`	Logical. If `TRUE`, rescales the counts of each V(D)J gene group to 1 for each pseudobulk after removing 'missing' calls. Useful when `setupVdjPseudobulk()` was run with `remove_missing = FALSE`. Default is `FALSE`.
`min_count`	Integer. Sets pseudobulk counts in V(D)J gene groups with fewer than this many non-missing calls to 0. Default is `1`.
`verbose`	Logical. If `TRUE`, prints messages and warnings. Default is `TRUE`.

Details

This function aggregates V(D)J data into pseudobulk groups based on the following logic:

Input Requirements:
If milo is a Milo object, neither pbs nor col_to_bulk is required.
If milo is a SingleCellExperiment object, the user must provide either pbs or col_to_bulk.
Normalization:
When normalise = TRUE, scales V(D)J counts to 1 for each pseudobulk group.
When renormalize = TRUE, rescales the counts after removing 'missing' calls.
Mode Selection:
If extract_cols = NULL, the function relies on mode_option to determine which V(D)J columns to extract.
Filtering:
Uses min_count to filter pseudobulks with insufficient counts for V(D)J groups.

Value

SingleCellExperiment object

Examples

data(sce_vdj)
sce_vdj <- setupVdjPseudobulk(sce_vdj,
    already.productive = FALSE,
    allowed_chain_status = c("Single pair", "Extra pair")
)
# Build Milo Object
milo_object <- miloR::Milo(sce_vdj)
milo_object <- miloR::buildGraph(milo_object,
    k = 50, d = 20,
    reduced.dim = "X_scvi"
)
milo_object <- miloR::makeNhoods(milo_object,
    reduced_dims = "X_scvi",
    d = 20
)

# Construct pseudobulked VDJ feature space
pb.milo <- vdjPseudobulk(milo_object, col_to_take = "anno_lvl_2_final_clean")

data(sce_vdj)
sce_vdj <- setupVdjPseudobulk(sce_vdj,
    already.productive = FALSE,
    allowed_chain_status = c("Single pair", "Extra pair")
)
# Build Milo Object
milo_object <- miloR::Milo(sce_vdj)
milo_object <- miloR::buildGraph(milo_object,
    k = 50, d = 20,
    reduced.dim = "X_scvi"
)
milo_object <- miloR::makeNhoods(milo_object,
    reduced_dims = "X_scvi",
    d = 20
)

# Construct pseudobulked VDJ feature space
pb.milo <- vdjPseudobulk(milo_object, col_to_take = "anno_lvl_2_final_clean")

Package 'dandelionR'

Help Index

Example AIRR Dataset for V(D)J Analysis

Description

Usage

Format

Source

Examples

Example SCE Dataset that does not contain V(D)J information

Description

Usage

Format

Source

Examples

Compute Branch Probabilities Using Markov Chain

Description

Usage

Arguments

Value

Markov Chain Construction and Probability Calculation

Description

Usage

Arguments

Value

Examples

Perform UMAP on the Adjacency Matrix of a Milo Object

Description

Usage

Arguments

Value

Examples

Project Probabilities from Markov Chain to Pseudobulks

Description

Usage

Arguments

Value

Project Pseudotime and Branch Probabilities to Single Cells

Description

Usage

Arguments

Value

Examples

Example Dataset for V(D)J Analysis

Description

Usage

Format

Source

Examples

Preprocess V(D)J Data for Pseudobulk Analysis

Description

Usage

Arguments

Details

Value

Examples

Generate Pseudobulk V(D)J Feature Space

Description

Usage

Arguments

Details

Value

Examples