test_differential_cellularity {tidybulk}R Documentation

Add differential tissue composition information to a tbl

Description

test_differential_cellularity() takes as input a 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | and returns a 'tbl' 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' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> |

.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 survival::coxph(.my_formula, .)

Value

A 'tbl' with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A 'tbl' with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A 'tbl' with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A 'tbl' 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
	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
	test_differential_cellularity(
	    survival::Surv(days, dead) ~ .,
	    cores = 1
	)




[Package tidybulk version 1.5.0 Index]