CSCD: Performs gene expression decomposition.

In empiricalbayes/CSCDRNA: Covariance Based Single-Cell Decomposition of Bulk Expression Data

Performs gene expression decomposition.

Description

Provides accurate cell-type proportion estimation by incorporating covariance structure in given single-cell RNA-seq (scRNA-seq) and bulk RNA-seq datasets, see Karimnezhad (2022). The approach uses an extension of the transformation used in Jew et al. (2020) implemented in the BisqueRNA::ReferenceBasedDecomposition() function.

Usage

CSCD(
  bulk.eset,
  sc.eset,
  min.p = NULL,
  markers = NULL,
  cell.types = "cellType",
  subj.names = "SubjectName",
  verbose = TRUE
)

Arguments

bulk.eset	ExpressionSet with bulk data. Bulk RNA-seq data can be converted from a matrix with samples in columns and genes in rows to an ExpressionSet. See example_data for an example on how to create a bulk.eset object.
sc.eset	ExpressionSet with single-cell data. Single-cell data requires additional information in the ExpressionSet, specifically cell-type labels and individual labels. See example_data for an example on how to create a sc.eset object.
min.p	A percentage. This parameter is passed to the Seurat::FindAllMarkers() function (Butler et al., 2019) to pick the most relevant genes to each cell-type cluster. Users may pick a number between 0.3 and 0.5 for best results. The higher the value, the more genes to be excluded from the analysis.
markers	A character vector containing marker genes to be used in decomposition. If NULL provided, the method will use all available genes for decomposition.
cell.types	Character string. A vector of cell-type labels.
subj.names	Character string. A vector of individual labels that correspond to cells.
verbose	Boolean. Whether to print log info during decomposition. Errors will be printed regardless.

Value

A list. Slot bulk.props contains a matrix of cell-type proportion estimates with cell types as rows and individuals as columns. Slot sc.props contains a matrix of cell-type proportions estimated directly from counting single-cell data. Slot transformed.bulk contains the covariance-based transformed bulk expression used for decomposition. These values are generated by applying a linear transformation to the CPM expression. Slot genes.used contains a vector of genes used in decomposition. Slot rnorm contains Euclidean norm of the residuals for each individual's proportion estimates.

References

Butler, A. et al. (2019). Seurat: Tools for Single Cell Genomics. R package version, 4.1.1.

Jew, B. et al. (2020) Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat Commun 11, 1971. https://doi.org/10.1038/s41467-020-15816-6

Jew, B. and Alvarez, M. (2020). BisqueRNA: Decomposition of Bulk Expression with Single-Cell Sequencing. R package version, 1.0.5.

Karimnezhad, A. (2022) More accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Bioinformatics Advances, Volume 2, Issue 1, 2022, vbac049. https://doi.org/10.1093/bioadv/vbac049

empiricalbayes/CSCDRNA documentation built on Nov. 2, 2024, 7:29 a.m.