Package 'xCell2'

Title: A Tool for Generic Cell Type Enrichment Analysis
Description: xCell2 provides methods for cell type enrichment analysis using cell type signatures. It includes three main functions - 1. xCell2Train for training custom references objects from bulk or single-cell RNA-seq datasets. 2. xCell2Analysis for conducting the cell type enrichment analysis using the custom reference. 3. xCell2GetLineage for identifying dependencies between different cell types using ontology.
Authors: Almog Angel [aut, cre] , Dvir Aran [aut]
Maintainer: Almog Angel <[email protected]>
License: GPL (>= 3)
Version: 0.99.107
Built: 2025-01-20 03:27:32 UTC
Source: https://github.com/bioc/xCell2

Help Index

Blueprint and ENCODE Projects Reference

Description

Pre-trained xCell2 reference for use in xCell2Analysis or extending via xCell2Train.

Usage

data(BlueprintEncode.xCell2Ref, package = "xCell2")

Format

An xCell2Object containing:

params

Linear transformation parameters.

signatures

Gene signatures for cell types.

dependencies

Cell lineage dependencies.

spill_mat

Spillover correction matrix.

genes_used

Genes included in the reference.

Details

Blueprint and ENCODE Projects Reference (human)

A pre-trained xCell2 reference object based on the Blueprint and ENCODE projects datasets.

Source

Martens JHA and Stunnenberg HG (2013); The ENCODE Project Consortium (2012), curated by Aran D (2019); trained by Angel A, et al. (2024).

See Also

xCell2Analysis and xCell2Train.

Access Cell Type Dependencies

Description

Retrieve or assign hierarchical dependencies between cell types for an xCell2Object.

Retrieve or assign the hierarchical dependencies between cell types for an xCell2Object.

Usage

getDeps(object)

setDeps(object) <- value

## S4 method for signature 'xCell2Object'
getDeps(object)

## S4 replacement method for signature 'xCell2Object'
setDeps(object) <- value

Arguments

object

An xCell2Object.
value

A list of hierarchical dependencies (for the setter).

Value

For 'getDeps', a list of hierarchical dependencies. For 'setDeps<-', the updated xCell2Object.

For 'getDeps', a list of hierarchical dependencies. For 'setDeps<-', the updated xCell2Object.

See Also

xCell2Object-class

xCell2Object-class

Examples

data(DICE_demo.xCell2Ref, package = "xCell2")
setDeps(DICE_demo.xCell2Ref) <- list("Parent" = "ChildType1")
getDeps(DICE_demo.xCell2Ref)
data(DICE_demo.xCell2Ref, package = "xCell2")
setDeps(DICE_demo.xCell2Ref) <- list("ParentType" = "ChildType1")

Subset of the DICE Reference

Description

Demo reference object derived from the DICE dataset for training xCell2 references.

Usage

data(dice_demo_ref, package = "xCell2")

Format

A SummarizedExperiment object.

Details

Subset of the DICE Reference

A subset of the DICE reference stored as a 'SummarizedExperiment' object for the xCell 2.0 vignette. This demo reference object demonstrates the use of xCell2Train for generating a custom xCell2 reference.

Source

Schmiedel B et al. (2018).

See Also

xCell2Train for generating references, and xCell2Analysis for enrichment analysis.

Demo xCell2 Reference Object

Description

Pre-trained xCell2 reference object based on the DICE dataset.

Usage

data(DICE_demo.xCell2Ref, package = "xCell2")

Format

An xCell2Object containing:

params

Linear transformation parameters.

signatures

Cell-type-specific gene signatures.

dependencies

Cell type lineage dependencies.

spill_mat

Spillover correction matrix.

genes_used

Genes included in the reference.

Details

Demo xCell2 Reference Object from DICE Subset (human)

A demo xCell2 reference object derived from a subset of the DICE dataset. Suitable for demonstrating the use of xCell2Analysis.

Source

Schmiedel B et al. (2018).

See Also

xCell2Analysis for enrichment analysis, and xCell2Train for training custom references.

Access Genes Used

Description

Retrieve or assign the genes used in training the reference for an xCell2Object.

Retrieve or assign the genes used in training the reference for an xCell2Object.

Usage

getGenesUsed(object)

setGenesUsed(object) <- value

## S4 method for signature 'xCell2Object'
getGenesUsed(object)

## S4 replacement method for signature 'xCell2Object'
setGenesUsed(object) <- value

Arguments

object

An xCell2Object.
value

A character vector of genes (for the setter).

Value

For 'getGenesUsed', a character vector of genes. For 'setGenesUsed<-', the updated xCell2Object.

For 'getGenesUsed', a character vector of genes. For 'setGenesUsed<-', the updated xCell2Object.

See Also

xCell2Object-class

xCell2Object-class

Examples

data(DICE_demo.xCell2Ref, package = "xCell2")
setGenesUsed(DICE_demo.xCell2Ref) <- c("GeneA", "GeneB", "GeneC")
getGenesUsed(DICE_demo.xCell2Ref)
data(DICE_demo.xCell2Ref, package = "xCell2")
setGenesUsed(DICE_demo.xCell2Ref) <- c("GeneA", "GeneB", "GeneC")

Immunologic Genome Project Reference

Description

Pre-trained xCell2 reference for use in xCell2Analysis or extending via xCell2Train.

Usage

data(ImmGenData.xCell2Ref, package = "xCell2")

Format

An xCell2Object containing:

params

Linear transformation parameters.

signatures

Gene signatures for cell types.

dependencies

Cell lineage dependencies.

spill_mat

Spillover correction matrix.

genes_used

Genes included in the reference.

Details

Immunologic Genome Project Reference

A pre-trained xCell2 reference object based on the Immunologic Genome Project dataset.

Source

The Immunological Genome Project Consortium (2008), curated by Aran D (2019); trained by Angel A, et al. (2024).

See Also

xCell2Analysis and xCell2Train.

Immune Compendium Reference

Description

Pre-trained xCell2 reference for use in xCell2Analysis or extending via xCell2Train.

Usage

data(ImmuneCompendium.xCell2Ref, package = "xCell2")

Format

An xCell2Object containing:

params

Linear transformation parameters.

signatures

Gene signatures for cell types.

dependencies

Cell lineage dependencies.

spill_mat

Spillover correction matrix.

genes_used

Genes included in the reference.

Details

Immune Compendium Reference (human)

A pre-trained xCell2 reference object based on the Immune Compendium dataset for immune cell profiling.

Source

Curated by Zaitsev A (2022); trained by Angel A, et al. (2024).

See Also

xCell2Analysis and xCell2Train.

LM22 Reference

Description

Pre-trained xCell2 reference for use in xCell2Analysis or extending via xCell2Train.

Usage

data(LM22.xCell2Ref, package = "xCell2")

Format

An xCell2Object containing:

params

Linear transformation parameters.

signatures

Gene signatures for cell types.

dependencies

Cell lineage dependencies.

spill_mat

Spillover correction matrix.

genes_used

Genes included in the reference.

Details

LM22 Reference (human)

A pre-trained xCell2 reference object based on the LM22 dataset.

Source

Newman AM (2015); trained by Angel A, et al. (2024).

See Also

xCell2Analysis and xCell2Train.

Demo Bulk Gene Expression Data

Description

Example RNA-Seq data to demonstrate xCell2Analysis.

Usage

data(mix_demo, package = "xCell2")

Format

A matrix with genes (rows) and samples (columns).

Details

Demo Bulk Gene Expression Data (RNA-Seq)

A demo mixture matrix for bulk RNA-Seq gene expression data. Use this dataset to test xCell2Analysis with pre-trained xCell2 references.

See Also

xCell2Analysis for enrichment analysis.

Mouse RNA-Seq Data Reference

Description

Pre-trained xCell2 reference for use in xCell2Analysis or extending via xCell2Train.

Usage

data(MouseRNAseqData.xCell2Ref, package = "xCell2")

Format

An xCell2Object containing:

params

Linear transformation parameters.

signatures

Gene signatures for cell types.

dependencies

Cell lineage dependencies.

spill_mat

Spillover correction matrix.

genes_used

Genes included in the reference.

Details

Mouse RNA-Seq Data Reference

A pre-trained xCell2 reference object based on the MouseRNAseqData dataset.

Source

Benayoun B (2019); trained by Angel A, et al. (2024).

See Also

xCell2Analysis and xCell2Train.

PanCancer Reference

Description

Pre-trained xCell2 reference for use in xCell2Analysis or extending via xCell2Train.

Usage

data(PanCancer.xCell2Ref, package = "xCell2")

Format

An xCell2Object containing:

params

Linear transformation parameters.

signatures

Gene signatures for cell types.

dependencies

Cell lineage dependencies.

spill_mat

Spillover correction matrix.

genes_used

Genes included in the reference.

Details

PanCancer Reference (human)

A pre-trained xCell2 reference object based on the PanCancer dataset for cancer-specific analyses.

Source

Nofech-Mozes I (2023); trained by Angel A, et al. (2024).

See Also

xCell2Analysis and xCell2Train.

Access Transformation Parameters

Description

Retrieve or assign linear transformation parameters for an xCell2Object.

Retrieve or assign linear transformation parameters for cell types for an xCell2Object.

Usage

getParams(object)

setParams(object) <- value

## S4 method for signature 'xCell2Object'
getParams(object)

## S4 replacement method for signature 'xCell2Object'
setParams(object) <- value

Arguments

object

An xCell2Object.
value

A data frame of transformation parameters (for the setter).

Value

For 'getParams', a data frame of transformation parameters. For 'setParams<-', the updated xCell2Object.

For 'getParams', a data frame of transformation parameters. For 'setParams<-', the updated xCell2Object.

See Also

xCell2Object-class

xCell2Object-class

Examples

data(DICE_demo.xCell2Ref, package = "xCell2")
setParams(DICE_demo.xCell2Ref) <- data.frame(celltype = "T_cells", a = 0.5, b = 2, m = 1.5)
getParams(DICE_demo.xCell2Ref)
data(DICE_demo.xCell2Ref, package = "xCell2")
setParams(DICE_demo.xCell2Ref) <- data.frame(celltype = "B_cells", a = 0.6, b = 1.8, m = 2.1)

Access Cell Type Signatures

Description

Retrieve or assign the cell type-specific gene signatures for an xCell2Object.

Retrieve or assign the cell type-specific gene signatures for an xCell2Object.

Usage

getSignatures(object)

setSignatures(object) <- value

## S4 method for signature 'xCell2Object'
getSignatures(object)

## S4 replacement method for signature 'xCell2Object'
setSignatures(object) <- value

Arguments

object

An xCell2Object.
value

A list of cell type-specific gene signatures (for the setter).

Value

For 'getSignatures', a list of cell type-specific gene signatures. For 'setSignatures<-', the updated xCell2Object.

For 'getSignatures', a list of cell type-specific gene signatures. For 'setSignatures<-', the updated xCell2Object.

See Also

xCell2Object-class

xCell2Object-class

Examples

data(DICE_demo.xCell2Ref, package = "xCell2")
setSignatures(DICE_demo.xCell2Ref) <- list("T_cells" = c("GeneA", "GeneB"), "B_cells" = c("GeneC"))
getSignatures(DICE_demo.xCell2Ref)
data(DICE_demo.xCell2Ref, package = "xCell2")
setSignatures(DICE_demo.xCell2Ref) <- list("T_cells" = c("GeneA", "GeneB"))

Access Spillover Matrix

Description

Retrieve or assign the spillover correction matrix for an xCell2Object.

Retrieve or assign the spillover correction matrix for an xCell2Object.

Usage

getSpillMat(object)

setSpillMat(object) <- value

## S4 method for signature 'xCell2Object'
getSpillMat(object)

## S4 replacement method for signature 'xCell2Object'
setSpillMat(object) <- value

Arguments

object

An xCell2Object.
value

A matrix of spillover correction factors (for the setter).

Value

For 'getSpillMat', a matrix of spillover correction factors. For 'setSpillMat<-', the updated xCell2Object.

For 'getSpillMat', a matrix of spillover correction factors. For 'setSpillMat<-', the updated xCell2Object.

See Also

xCell2Object-class

xCell2Object-class

Examples

data(DICE_demo.xCell2Ref, package = "xCell2")
spill_mat <- matrix(c(1, 0.1, 0.1, 1), nrow = 2, byrow = TRUE)
rownames(spill_mat) <- colnames(spill_mat) <- c("T_cells", "B_cells")
setSpillMat(DICE_demo.xCell2Ref) <- spill_mat
getSpillMat(DICE_demo.xCell2Ref)
data(DICE_demo.xCell2Ref, package = "xCell2")
spill_mat <- matrix(c(1, 0.05, 0.05, 1), nrow = 2, byrow = TRUE)
rownames(spill_mat) <- colnames(spill_mat) <- c("T_cells", "B_cells")
setSpillMat(DICE_demo.xCell2Ref) <- spill_mat

Tabula Muris Blood Reference

Description

Pre-trained xCell2 reference for use in xCell2Analysis or extending via xCell2Train.

Usage

data(TabulaMurisBlood.xCell2Ref, package = "xCell2")

Format

An xCell2Object containing:

params

Linear transformation parameters.

signatures

Gene signatures for cell types.

dependencies

Cell lineage dependencies.

spill_mat

Spillover correction matrix.

genes_used

Genes included in the reference.

Details

Tabula Muris Blood Reference (mouse)

A pre-trained xCell2 reference object based Tabula Muris dataset.

Source

The Tabula Muris Consortium (2018); trained by Angel A, et al. (2024).

See Also

xCell2Analysis and xCell2Train.

Tabula Sapiens Blood Reference

Description

Pre-trained xCell2 reference for use in xCell2Analysis or extending via xCell2Train.

Usage

data(TabulaSapiensBlood.xCell2Ref, package = "xCell2")

Format

An xCell2Object containing:

params

Linear transformation parameters.

signatures

Gene signatures for cell types.

dependencies

Cell lineage dependencies.

spill_mat

Spillover correction matrix.

genes_used

Genes included in the reference.

Details

Tabula Sapiens Blood Reference (human)

A pre-trained xCell2 reference object based on the Tabula Sapiens dataset.

Source

The Tabula Sapiens Consortium (2022); trained by Angel A, et al. (2024).

See Also

xCell2Analysis and xCell2Train.

Tumor Microenvironment Compendium Reference

Description

Pre-trained xCell2 reference object for analyzing tumor microenvironments.

Usage

data(TMECompendium.xCell2Ref, package = "xCell2")

Format

An xCell2Object containing:

params

Linear transformation parameters.

signatures

Cell-type-specific gene signatures.

dependencies

Cell type lineage dependencies.

spill_mat

Spillover correction matrix.

genes_used

Genes included in the reference.

Details

Tumor Microenvironment Compendium Reference (human)

An xCell2 reference object created from the Tumor Microenvironment Compendium dataset.

Normalized data for training can be accessed at: https://science.bostongene.com/kassandra/downloads.

Source

Curated by Zaitsev A (2022) and trained by Angel A, et al. (2024).

References

Zaitsev, A., et al. (2022). Cancer Cell, 40(8), 879-894.

See Also

xCell2Analysis and xCell2Train.

Perform Cell Type Enrichment Analysis

Description

Estimates the relative enrichment of cell types in a bulk gene expression mixture. This function uses gene signatures from a pre-trained xCell2Object to compute enrichment scores, with options for linear transformation and spillover correction to improve specificity.

Usage

xCell2Analysis(
  mix,
  xcell2object,
  minSharedGenes = 0.9,
  rawScores = FALSE,
  spillover = TRUE,
  spilloverAlpha = 0.5,
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

mix

A bulk mixture of gene expression data (genes in rows, samples in columns). The input must use the same gene annotation system as the reference object.
xcell2object

A pre-trained reference object of class xCell2Object, created using xCell2Train. Pre-trained references for common cases are provided within the package.
minSharedGenes

Minimum fraction of shared genes required between the mixture and the reference object (default: 0.9). If the shared fraction is below this threshold, the function stops with an error or warning, as sufficient overlap is necessary for accurate analysis.
rawScores

Logical; if TRUE, returns raw enrichment scores without transformation or spillover correction (default: FALSE).
spillover

Logical; enables spillover correction on enrichment scores (default: TRUE). Spillover occurs when closely related cell types share gene expression patterns, inflating enrichment scores. Correction enhances specificity, particularly for related cell types.
spilloverAlpha

Numeric value controlling spillover correction strength (default: 0.5). Lower values apply weaker correction, while higher values apply stronger correction.
BPPARAM

A BiocParallelParam instance to define parallelization strategy (see "Details"). Default is BiocParallel::SerialParam().

Details

The xCell2Analysis function performs cell type enrichment analysis by leveraging gene signatures from a pre-trained xCell2Object. It computes enrichment scores for each cell type in the provided bulk gene expression mixture (mix), applies linear transformations, and corrects for spillover. Spillover correction addresses the overlap of gene expression patterns between closely related cell types, improving the specificity of the enrichment scores.

## Parallelization with BPPARAM To achieve faster processing by running computations in parallel, xCell2Analysis supports parallelization through the BPPARAM parameter. Users can define a parallelization strategy using BiocParallelParam from the BiocParallel package. For example, MulticoreParam is suitable for multi-core processing on Linux and macOS, while SnowParam or SerialParam are better suited for Windows systems. Refer to the BiocParallel documentation for further guidance on parallelization strategies.

## Relationship with Other Function(s) The pre-trained xCell2Object used in xCell2Analysis is created via the xCell2Train function.

The xCell2Analysis function computes enrichment scores for each cell type using gene signatures from a pre-trained xCell2Object. Linear transformations and spillover corrections refine the results, improving specificity when cell types have overlapping gene expression patterns.

Parallelization with BPPARAM: Computations can be parallelized using the BPPARAM parameter. Supported strategies include:

See the BiocParallel documentation.

Relationship with Other Functions: The input reference object (xCell2Object) is created via xCell2Train.

Value

A data frame containing the cell type enrichment for each sample in the input matrix, as estimated by xCell2. Each row corresponds to a cell type, and each column corresponds to a sample.

A data frame containing enrichment scores for each cell type and sample. Rows correspond to cell types and columns to samples.

Author(s)

Almog Angel and Dvir Aran

See Also

xCell2Train, for creating the reference object used in this analysis.

Examples

# For detailed example read xCell2 vignette.

library(xCell2)

# Load "ready to use" xCell2 reference object or generate a new one using `xCell2Train`
data(DICE_demo.xCell2Ref, package = "xCell2")

# Load demo bulk RNA-Seq gene expression mixture

# For detailed examples, see the xCell2 vignette.

library(xCell2)

# Load pre-trained reference object
data(DICE_demo.xCell2Ref, package = "xCell2")

# Load demo bulk gene expression mixture
data(mix_demo, package = "xCell2")

# Perform cell type enrichment analysis
xcell2_res <- xCell2::xCell2Analysis(
  mix = mix_demo, 
  xcell2object = DICE_demo.xCell2Ref
)

# Parallel processing example with BiocParallel
library(BiocParallel)
parallel_param <- MulticoreParam(workers = 2)
xcell2_res_parallel <- xCell2::xCell2Analysis(
  mix = mix_demo, 
  xcell2object = DICE_demo.xCell2Ref, 
  BPPARAM = parallel_param
)

Identify Cell Type Lineage Dependencies

Description

Identifies cell type dependencies based on the Cell Ontology, including both descendants and ancestors for each cell type. Enables manual inspection and refinement of lineage relationships to improve biological accuracy in xCell2 analyses.

Usage

xCell2GetLineage(labels, outFile = NULL)

Arguments

labels

A data frame with the following required columns:

  • "ont": Cell type ontology ID (e.g., "CL:0000545"). Use NA if unavailable. Ontologies can be accessed via EBI Ontology Lookup Service (OLS) or the ontologyIndex package.

  • "label": Cell type name (e.g., "T-helper 1 cell").

  • "sample": Sample or cell identifier matching column names in the gene expression matrix.

  • "dataset": Dataset or subject source. Use a constant value if not applicable.

outFile

Optional. Output file name for saving dependencies as a TSV file. The file includes columns for "ont", "label", "descendants", and "ancestors". Suitable for manual inspection and refinement before use in downstream analyses.

Details

The xCell2GetLineage function generates lineage relationships for cell types based on the Cell Ontology. These relationships refine lineage-based dependencies, improving the biological relevance of gene signatures. Users can:

  • Use the generated TSV file for manual adjustments before training custom references via xCell2Train.

  • Skip this step entirely, allowing xCell2Train to infer dependencies automatically.

If no ontology IDs ("ont") are provided, the function outputs empty dependencies with a message for user guidance.

Relationship with Other Functions:

  • xCell2Train: Incorporates lineage relationships during reference training.

  • xCell2Analysis: Uses trained references for enrichment analysis.

Value

If outFile is:

  • NULL: Returns a list of dependencies for each cell type, with descendants and ancestors as components.

  • Specified: Writes a TSV file and warns the user to inspect and validate results manually.

Author(s)

Almog Angel and Dvir Aran

See Also

xCell2Train for training custom references with lineage data. xCell2Analysis for enrichment analysis using trained references. AnnotationHub to access ontology data. ontologyIndex to programmatically explore ontologies.

Examples

# For detailed examples, see the xCell2 vignette.

library(xCell2)

# Load demo reference object
data(dice_demo_ref, package = "xCell2")

# Prepare labels data frame
dice_labels <- SummarizedExperiment::colData(dice_demo_ref)
dice_labels <- as.data.frame(dice_labels)
dice_labels$ont <- NA
dice_labels$sample <- colnames(dice_demo_ref)
dice_labels$dataset <- "DICE"

# Assign ontology IDs
dice_labels[dice_labels$label == "B cells", ]$ont <- "CL:0000236"
dice_labels[dice_labels$label == "Monocytes", ]$ont <- "CL:0000576"
dice_labels[dice_labels$label == "NK cells", ]$ont <- "CL:0000623"
dice_labels[dice_labels$label == "T cells, CD8+", ]$ont <- "CL:0000625"
dice_labels[dice_labels$label == "T cells, CD4+", ]$ont <- "CL:0000624"
dice_labels[dice_labels$label == "T cells, CD4+, memory", ]$ont <- "CL:0000897"

# Generate cell type lineage dependencies
xCell2::xCell2GetLineage(labels = dice_labels)

# Manually inspect and adjust saved dependencies for refined lineage relationships
# Use the adjusted file as input to xCell2Train via the `lineageFile` parameter.

xCell2Object Class

Description

An S4 class to represent the xCell2 reference object. This object contains cell type-specific gene signatures, hierarchical dependencies, linear transformation parameters, spillover correction matrices, and genes used for training.

Slots

signatures

A list of cell type-specific gene signatures.

dependencies

A list of hierarchical dependencies between cell types.

params

A data frame containing linear transformation parameters for cell types.

spill_mat

A matrix containing spillover correction factors for cell types.

genes_used

A character vector of genes used for training the xCell2 reference object.

Train Custom xCell2 Reference Object

Description

This function creates a custom reference object for xCell2Analysis, enabling cell type enrichment analysis. It supports references derived from RNA-Seq, microarray, and scRNA-Seq data and can be derived from various tissues and organisms.

Usage

xCell2Train(
  ref,
  mix = NULL,
  labels = NULL,
  refType,
  lineageFile = NULL,
  BPPARAM = BiocParallel::SerialParam(),
  useOntology = TRUE,
  returnSignatures = FALSE,
  returnAnalysis = FALSE,
  useSpillover = TRUE,
  spilloverAlpha = 0.5,
  minPbCells = 30,
  minPbSamples = 10,
  minScGenes = 10000
)

Arguments

ref

A reference gene expression matrix (genes in rows, samples/cells in columns) or a SummarizedExperiment/SingleCellExperiment object with expression data in the assays slot.

Valid Assays:

"tpm"

Transcripts Per Million (recommended for RNA-Seq).

"logcounts"

Log-transformed normalized counts.

"normcounts"

Normalized counts.

"counts"

Raw counts (required for microarray references).

Notes:

  • If multiple assays exist, "tpm" is prioritized.

  • For microarray data, the "counts" assay must be used.

mix

A bulk mixture of gene expression matrix (genes in rows, samples in columns) (optional). This parameter is required if returnAnalysis is set to TRUE, as it is used for enrichment analysis.
labels

A data frame with the following columns:

  • "ont": The cell type ontology ID (e.g., "CL:0000545"). Set to NA if not available. Ontologies can be found at EBI Ontology Lookup Service (OLS) or by using the ontologyIndex package.

  • "label": The cell type name (e.g., "T-helper 1 cell").

  • "sample": The sample or cell identifier, matching column names in the reference matrix.

  • "dataset": The dataset source for each sample. If not applicable, use a constant value for all samples.

This parameter is unnecessary if ref is a SummarizedExperiment or SingleCellExperiment object, as metadata should be in colData.
refType

The type of reference data: "rnaseq" for RNA-Seq, "array" for microarray, or "sc" for scRNA-Seq.
lineageFile

Path to a manually curated cell type lineage file generated with xCell2GetLineage (optional).
BPPARAM

A BiocParallelParam instance that determines the parallelization strategy (more in "Details"). Default is BiocParallel::SerialParam().
useOntology

A Boolean indicating whether to use ontological integration for cell type dependencies (default: TRUE). Lineage relationships are determined using the Cell Ontology (CL). Users can refine these dependencies with xCell2GetLineage and provide them via the lineageFile parameter.
returnSignatures

A Boolean to return only cell type signatures (default: FALSE).
returnAnalysis

A Boolean to return xCell2Analysis results instead of a reference object (default: FALSE).
useSpillover

A Boolean to use spillover correction during analysis when returnAnalysis is TRUE (default: TRUE). Spillover correction enhances the specificity of enrichment scores by accounting for overlaps between cell types.
spilloverAlpha

Numeric value controlling spillover correction strength (default: 0.5). Lower values apply weaker correction, while higher values apply stronger correction.
minPbCells

Minimum number of cells in a pseudo-bulk sample for scRNA-Seq references (default: 30).
minPbSamples

Minimum number of pseudo-bulk samples for scRNA-Seq references (default: 10).
minScGenes

Minimum number of genes for pseudo-bulk samples for scRNA-Seq references (default: 1e4).

Details

Ontological Integration: Ontological integration (useOntology) leverages hierarchical cell type relationships to ensure biologically meaningful signatures. Dependencies can be refined using xCell2GetLineage, which generates lineage files for manual review.

Spillover Correction: Spillover correction enhances the specificity of enrichment scores by reducing overlaps between related cell types. Use the spilloverAlpha parameter to tune the strength of correction.

Contribute Your xCell2 Reference Object: Users are encouraged to share their reference objects via the xCell2 Reference Repository.

Value

An xCell2Object containing:

  • signatures: Cell type-specific gene signatures.

  • dependencies: Lineage-based dependencies.

  • params: Linear transformation parameters.

  • spill_mat: A spillover correction matrix.

  • genes_used: Genes used for training.

Author(s)

Almog Angel and Dvir Aran

See Also

xCell2Analysis, for enrichment analysis. xCell2GetLineage, for refining cell type dependencies.

Examples

library(xCell2)
data(dice_demo_ref, package = "xCell2")
dice_ref <- SummarizedExperiment::assay(dice_demo_ref, "logcounts")
colnames(dice_ref) <- make.unique(colnames(dice_ref))
dice_labels <- as.data.frame(SummarizedExperiment::colData(dice_demo_ref))
dice_labels$ont <- NA
dice_labels$sample <- colnames(dice_ref)
dice_labels$dataset <- "DICE"
DICE.xCell2Ref <- xCell2::xCell2Train(ref = dice_ref, labels = dice_labels, refType = "rnaseq")