R/deconvolve_cellularity.R
deconvolve_cellularity-methods.Rddeconvolve_cellularity() takes as input A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with the estimated cell type abundance for each sample
deconvolve_cellularity(
.data,
.abundance = NULL,
reference = NULL,
method = "cibersort",
prefix = "",
feature_column = NULL,
...
)
# S4 method for class 'SummarizedExperiment'
deconvolve_cellularity(
.data,
.abundance = NULL,
reference = NULL,
method = "cibersort",
prefix = "",
feature_column = NULL,
...
)
# S4 method for class 'RangedSummarizedExperiment'
deconvolve_cellularity(
.data,
.abundance = NULL,
reference = NULL,
method = "cibersort",
prefix = "",
feature_column = NULL,
...
)A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment))
The name of the transcript/gene abundance column
A data frame. The methods cibersort and llsr can accept a custom rectangular dataframe with genes as rows names, cell types as column names and gene-transcript abundance as values. For exampler tidybulk::X_cibersort. The transcript/cell_type data frame of integer transcript abundance. If NULL, the default reference for each algorithm will be used. For llsr will be LM22.
A character string. The method to be used. Available methods: "cibersort", "llsr", "epic", "mcp_counter", "quantiseq", "xcell". If a vector is provided, an error will be thrown. Default is all available methods.
A character string. The prefix you would like to add to the result columns. It is useful if you want to reshape data.
A character string. The name of a column in rowData to use as feature names instead of rownames. If NULL (default), rownames are used.
Further parameters passed to the function Cibersort
A consistent object (to the input) including additional columns for each cell type estimated
A `SummarizedExperiment` object
A `SummarizedExperiment` object
`r lifecycle::badge("maturing")`
This function infers the cell type composition of our samples (with the algorithm Cibersort; Newman et al., 10.1038/nmeth.3337).
Underlying method: CIBERSORT(Y = data, X = reference, ...)
Mangiola, S., Molania, R., Dong, R., Doyle, M. A., & Papenfuss, A. T. (2021). tidybulk: an R tidy framework for modular transcriptomic data analysis. Genome Biology, 22(1), 42. doi:10.1186/s13059-020-02233-7
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods, 12(5), 453-457. doi:10.1038/nmeth.3337
Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E., & Gfeller, D. (2017). Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife, 6, e26476. doi:10.7554/eLife.26476
## Load airway dataset for examples
data('airway', package = 'airway')
# Ensure a 'condition' column exists for examples expecting it
SummarizedExperiment::colData(airway)$condition <- SummarizedExperiment::colData(airway)$dex
# Map ENSEMBL rownames to SYMBOLs for compatibility with reference signatures
library(tidySummarizedExperiment)
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: generics
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#> Attaching package: ‘generics’
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#> as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
#> setequal, union
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#> IQR, mad, sd, var, xtabs
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#> mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#> rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
#> unsplit, which.max, which.min
#> Loading required package: S4Vectors
#>
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
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#> findMatches
#> The following objects are masked from ‘package:base’:
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#> I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: Seqinfo
#> Loading required package: Biobase
#> Welcome to Bioconductor
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#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
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#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
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#> rowMedians
#> The following objects are masked from ‘package:matrixStats’:
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#> anyMissing, rowMedians
#>
#> Attaching package: ‘tidySummarizedExperiment’
#> The following object is masked from ‘package:generics’:
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#> tidy
library(org.Hs.eg.db)
#> Loading required package: AnnotationDbi
#>
#> Attaching package: ‘AnnotationDbi’
#> The following object is masked from ‘package:dplyr’:
#>
#> select
if (FALSE) { # \dontrun{
airway |>
mutate(gene_symbol = AnnotationDbi::mapIds(
org.Hs.eg.db::org.Hs.eg.db,
keys = .feature,
keytype = 'ENSEMBL',
column = 'SYMBOL',
multiVals = 'first'
)) |>
mutate(gene_symbol = ifelse(is.na(gene_symbol) | gene_symbol == '', .feature, gene_symbol)) |>
deconvolve_cellularity(feature_column = 'gene_symbol', cores = 1)
} # }
# Alternatively, if you already have a feature column in rowData
# se_with_features <- airway
# rowData(se_with_features)$gene_symbol <- rownames(se_with_features)
# se_with_features |> deconvolve_cellularity(feature_column = 'gene_symbol', cores = 1)