test_differential_cellularity-methods: Add differential tissue composition information to a tbl

In stemangiola/tidybulk: Brings transcriptomics to the tidyverse

Add differential tissue composition information to a tbl

Description

test_differential_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.

Usage

test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

## S4 method for signature 'spec_tbl_df'
test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

## S4 method for signature 'tbl_df'
test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

## S4 method for signature 'tidybulk'
test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

## S4 method for signature 'SummarizedExperiment'
test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

## S4 method for signature 'RangedSummarizedExperiment'
test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

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))
.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
significance_threshold	A real between 0 and 1 (usually 0.05).
...	Further parameters passed to the method deconvolve_cellularity

Details

'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 generalised linear model: data |> deconvolve_cellularity( !!.sample, !!.transcript, !!.abundance, method=method, reference = reference, action="get", ... ) [..] betareg::betareg(.my_formula, .)

Underlying method for the cox regression: data |> deconvolve_cellularity( !!.sample, !!.transcript, !!.abundance, method=method, reference = reference, action="get", ... ) [..] mutate(.proportion_0_corrected = .proportion_0_corrected |> boot::logit()) survival::coxph(.my_formula, .)

Value

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 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

 # Regular regression
	test_differential_cellularity(
	 tidybulk::se_mini ,
	    . ~ condition,
	    cores = 1
	)

	# Cox regression - multiple

tidybulk::se_mini |>

	# Test
	test_differential_cellularity(
	    survival::Surv(days, dead) ~ .,
	    cores = 1
	)

stemangiola/tidybulk documentation built on Oct. 23, 2024, 8 a.m.