Get cell type proportions from samples — deconvolve

deconvolve_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,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

# S4 method for spec_tbl_df
deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

# S4 method for tbl_df
deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

# S4 method for tidybulk
deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

# S4 method for SummarizedExperiment
deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

# S4 method for RangedSummarizedExperiment
deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

Arguments

.data: A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment))
.sample: The name of the sample column
.transcript: The name of the transcript/gene column
.abundance: The name of the transcript/gene abundance column
reference: 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.
method: A character string. The method to be used. At the moment Cibersort (default, can accept custom reference), epic (can accept custom reference) and llsr (linear least squares regression, can accept custom reference), mcp_counter, quantiseq, xcell are available.
prefix: A character string. The prefix you would like to add to the result columns. It is useful if you want to reshape data.
action: A character string. Whether to join the new information to the input tbl (add), or just get the non-redundant tbl with the new information (get).
...: Further parameters passed to the function Cibersort

Value

A consistent object (to the input) including additional columns for each cell type estimated

A `SummarizedExperiment` object

Details

`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, ...)

Examples


library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:tidybulk’:
#> 
#>     bind_cols, bind_rows
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

# Subsetting for time efficiency
tidybulk::se_mini |> deconvolve_cellularity(cores = 1)
#> class: SummarizedExperiment 
#> dim: 527 5 
#> metadata(0):
#> assays(1): count
#> rownames(527): ABCB4 ABCB9 ... ZNF324 ZNF442
#> rowData names(1): entrez
#> colnames(5): SRR1740034 SRR1740035 SRR1740043 SRR1740058 SRR1740067
#> colData names(27): Cell.type time ... Eosinophils Neutrophils