test_stratification_cellularity-methods: Test of stratification of biological replicates based on...

In stemangiola/tidybulk: Brings transcriptomics to the tidyverse

Test of stratification of biological replicates based on tissue composition, one cell-type at the time, using Kaplan-meier curves.

Description

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.

Usage

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,
  ...
)

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
...	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 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)

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 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).

Examples

tidybulk::se_mini |>
test_stratification_cellularity(
	survival::Surv(days, dead) ~ .,
	cores = 1
)

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