test_differential_abundance-methods: Perform differential transcription testing using edgeR QLT,...
In tidybulk: Brings transcriptomics to the tidyverse

Description Usage Arguments Details Value Examples

test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  .contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  significance_threshold = NULL,
  fill_missing_values = NULL
)

## S4 method for signature 'spec_tbl_df'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  .contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  significance_threshold = NULL,
  fill_missing_values = NULL
)

## S4 method for signature 'tbl_df'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  .contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  significance_threshold = NULL,
  fill_missing_values = NULL
)

## S4 method for signature 'tidybulk'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  .contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  significance_threshold = NULL,
  fill_missing_values = NULL
)

## S4 method for signature 'SummarizedExperiment'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  .contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  significance_threshold = NULL,
  fill_missing_values = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  .contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  significance_threshold = NULL,
  fill_missing_values = NULL
)

`.data`	A 'tbl' formatted as \| <SAMPLE> \| <TRANSCRIPT> \| <COUNT> \| <...> \|
`.formula`	A formula with no response variable, representing the desired linear model
`.sample`	The name of the sample column
`.transcript`	The name of the transcript/gene column
`.abundance`	The name of the transcript/gene abundance column
`.contrasts`	This parameter takes the shape of the contrast parameter of the method of choice. For edgeR and limma-voom is a character vector. For DESeq2 is a list including a character vectors of length three. If contrasts are not present the first covariate is the one the model is tested against (e.g., ~ factor_of_interest)
`method`	A string character. Either "edgeR_quasi_likelihood" (i.e., QLF), "edgeR_likelihood_ratio" (i.e., LRT), "DESeq2", "limma_voom"
`scaling_method`	A character string. The scaling method passed to the back-end function (i.e., edgeR::calcNormFactors; "TMM","TMMwsp","RLE","upperquartile")
`omit_contrast_in_colnames`	If just one contrast is specified you can choose to omit the contrast label in the colnames.
`prefix`	A character string. The prefix you would like to add to the result columns. It is useful if you want to compare several methods.
`action`	A character string. Whether to join the new information to the input tbl (add), or just get the non-redundant tbl with the new information (get).
`significance_threshold`	A real between 0 and 1 (usually 0.05).
`fill_missing_values`	A boolean. Whether to fill missing sample/transcript values with the median of the transcript. This is rarely needed.

\lifecycle

maturing

This function provides the option to use edgeR https://doi.org/10.1093/bioinformatics/btp616, limma-voom https://doi.org/10.1186/gb-2014-15-2-r29, or DESeq2 https://doi.org/10.1186/s13059-014-0550-8 to perform the testing. All methods use raw counts, irrespective of if scale_abundance or adjust_abundance have been calculated, therefore it is essential to add covariates such as batch effects (if applicable) in the formula.

Underlying method for edgeR framework: .data

# Filter keep_abundant( factor_of_interest = !!(as.symbol(parse_formula(.formula)[1])), minimum_counts = minimum_counts, minimum_proportion = minimum_proportion )

# Format select(!!.transcript,!!.sample,!!.abundance) spread(!!.sample,!!.abundance) as_matrix(rownames = !!.transcript)

# edgeR edgeR::DGEList(counts = .) edgeR::calcNormFactors(method = scaling_method) edgeR::estimateDisp(design)

# Fit edgeR::glmQLFit(design) edgeR::glmQLFTest(coef = 2, contrast = my_contrasts) // or glmLRT according to choice

Underlying method for DESeq2 framework: keep_abundant( factor_of_interest = !!as.symbol(parse_formula(.formula)[[1]]), minimum_counts = minimum_counts, minimum_proportion = minimum_proportion )

# DESeq2 DESeq2::DESeqDataSet( design = .formula) DESeq2::DESeq() DESeq2::results()

A 'tbl' with additional columns for the statistics from the 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

 tidybulk::counts_mini %>% 
 tidybulk(sample, transcript, count) %>% 
 identify_abundant() %>% 
	test_differential_abundance( ~ condition )

	# The function `test_differential_abundance` operates with contrasts too

 tidybulk::counts_mini %>%
 tidybulk(sample, transcript, count) %>%
 identify_abundant() %>%
 test_differential_abundance(
	    ~ 0 + condition,
	    .contrasts = c( "conditionTRUE - conditionFALSE")
 )