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cscdrna

https://github.com/empiricalbayes/cscdrna

Science Score: 26.0%

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  • Host: GitHub
  • Owner: empiricalbayes
  • Language: R
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Created about 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

CSCDRNA

Covariance Based Single-Cell Decomposition of Bulk Expression Data

### Cell-type decomposition This approach provides accurate cell-type proportion estimation by incorporating covariance structure in a given set of 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).

Installation

The package can be installed from the GitHub repository r devtools::install_github("empiricalbayes/CSCDRNA")

Getting Started

You can load CSCDRNA as follows:

r library(CSCDRNA)

How to run CSCD on an example data containing synthetic bulk and single-cell datasets.

```r

Load example data.

data(example_data)

Build ExpressionSet with bulk data.

bulk.eset <- Biobase::ExpressionSet(assayData = example_data$bulk.matrix)

Build ExpressionSet with single-cell data.

sc.counts.matrix=exampledata$sc.counts.matrix individual.labels=exampledata$individual.labels cell.type.labels=example_data$cell.type.labels sample.ids <- colnames(sc.counts.matrix)

individual.labels and cell.types should be in the same order as in sample.ids.

sc.pheno <- data.frame(check.names=FALSE, check.rows=FALSE, stringsAsFactors=FALSE,row.names=sample.ids, SubjectName=individual.labels,cellType=cell.type.labels) sc.meta <- data.frame(labelDescription=c("SubjectName","cellType"), row.names=c("SubjectName","cellType")) sc.pdata <- new("AnnotatedDataFrame",data=sc.pheno, varMetadata=sc.meta) sc.eset <- Biobase::ExpressionSet(assayData=sc.counts.matrix,phenoData=sc.pdata)

Run CSCD on the example data.

analysis <- CSCD(bulk.eset=bulk.eset,sc.eset= sc.eset, min.p=0.3,markers=NULL,cell.types="cellType", subj.names="SubjectName",verbose=TRUE)

Estimated cell-type proportions.

analysis$bulk.props

Cell-type proportions estimated directly by counting single-cell data.

analysis$sc.props

The covariance based transformed bulk expression used for decomposition.

analysis$transformed.bulk

Genes used in the decomposition.

analysis$genes.used

Euclidean norm of the residuals for each individual's proportion estimates.

analysis$rnorm ```

References

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

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, vbac049. https://doi.org/10.1093/bioadv/vbac049

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    • cran 164 last-month
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cran.r-project.org: CSCDRNA

Covariance Based Single-Cell Decomposition of Bulk Expression Data

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 164 Last month
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ali.karimnezhad@gmail.com
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Dependencies

DESCRIPTION cran
  • R >= 4.0 depends
  • Biobase * imports
  • BisqueRNA * imports
  • MAST * imports
  • Seurat * imports
  • limSolve * imports
  • methods * imports
  • nlshrink * imports
  • plyr * imports
  • testthat >= 3.0.0 suggests