deconvolve_cellularity {tidybulk} | R Documentation |
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 signature 'spec_tbl_df' deconvolve_cellularity( .data, .sample = NULL, .transcript = NULL, .abundance = NULL, reference = NULL, method = "cibersort", prefix = "", action = "add", ... ) ## S4 method for signature 'tbl_df' deconvolve_cellularity( .data, .sample = NULL, .transcript = NULL, .abundance = NULL, reference = NULL, method = "cibersort", prefix = "", action = "add", ... ) ## S4 method for signature 'tidybulk' deconvolve_cellularity( .data, .sample = NULL, .transcript = NULL, .abundance = NULL, reference = NULL, method = "cibersort", prefix = "", action = "add", ... ) ## S4 method for signature 'SummarizedExperiment' deconvolve_cellularity( .data, .sample = NULL, .transcript = NULL, .abundance = NULL, reference = NULL, method = "cibersort", prefix = "", action = "add", ... ) ## S4 method for signature 'RangedSummarizedExperiment' deconvolve_cellularity( .data, .sample = NULL, .transcript = NULL, .abundance = NULL, reference = NULL, method = "cibersort", prefix = "", action = "add", ... )
.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. A rectangular dataframe with genes as rows names, cell types as column names and gene-transcript abundance as values. 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), epic and llsr (linear least squares regression) 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 |
'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, ...)
A consistent object (to the input) including additional columns for each cell type estimated
A consistent object (to the input) including additional columns for each cell type estimated
A consistent object (to the input) including additional columns for each cell type estimated
A consistent object (to the input) including additional columns for each cell type estimated
A 'SummarizedExperiment' object
A 'SummarizedExperiment' object
library(dplyr) # Subsetting for time efficiency tidybulk::se_mini |> tidybulk() |>filter(sample=="SRR1740034") |> deconvolve_cellularity(sample, feature, count, cores = 1)