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 1.4.0 Index]