test_stratification_cellularity {tidybulk} | R Documentation |
test_stratification_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 additional columns for the statistics from the hypothesis test.
test_stratification_cellularity( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, method = "cibersort", reference = X_cibersort, ... ) ## S4 method for signature 'spec_tbl_df' test_stratification_cellularity( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, method = "cibersort", reference = X_cibersort, ... ) ## S4 method for signature 'tbl_df' test_stratification_cellularity( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, method = "cibersort", reference = X_cibersort, ... ) ## S4 method for signature 'tidybulk' test_stratification_cellularity( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, method = "cibersort", reference = X_cibersort, ... ) ## S4 method for signature 'SummarizedExperiment' test_stratification_cellularity( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, method = "cibersort", reference = X_cibersort, ... ) ## S4 method for signature 'RangedSummarizedExperiment' test_stratification_cellularity( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, method = "cibersort", reference = X_cibersort, ... )
.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)) |
.formula |
A formula representing the desired linear model. The formula can be of two forms: multivariable (recommended) or univariable Respectively: \"factor_of_interest ~ .\" or \". ~ factor_of_interest\". The dot represents cell-type proportions, and it is mandatory. If censored regression is desired (coxph) the formula should be of the form \"survival::Surv\(y, dead\) ~ .\" |
.sample |
The name of the sample column |
.transcript |
The name of the transcript/gene column |
.abundance |
The name of the transcript/gene abundance column |
method |
A string character. Either \"cibersort\", \"epic\" or \"llsr\". The regression method will be chosen based on being multivariable: lm or cox-regression (both on logit-transformed proportions); or univariable: beta or cox-regression (on logit-transformed proportions). See .formula for multi- or univariable choice. |
reference |
A data frame. The transcript/cell_type data frame of integer transcript abundance |
... |
Further parameters passed to the method deconvolve_cellularity |
'r lifecycle::badge("maturing")'
This routine applies a deconvolution method (e.g., Cibersort; DOI: 10.1038/nmeth.3337) and passes the proportions inferred into a generalised linear model (DOI:dx.doi.org/10.1007/s11749-010-0189-z) or a cox regression model (ISBN: 978-1-4757-3294-8)
Underlying method for the test: data deconvolve_cellularity( !!.sample, !!.transcript, !!.abundance, method=method, reference = reference, action="get", ... ) [..] mutate(.high_cellularity = .proportion > median(.proportion)) survival::survdiff(data = data, .my_formula)
A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
library(dplyr) library(tidyr) tidybulk::se_mini |> tidybulk() |> # Add survival data nest(data = -sample) |> mutate( days = c(1, 10, 500, 1000, 2000), dead = c(1, 1, 1, 0, 1) ) %>% unnest(data) |> test_stratification_cellularity( survival::Surv(days, dead) ~ ., cores = 1 )