test_differential_abundance-methods: Perform differential transcription testing using edgeR...

In stemangiola/ttBulk: Brings transcriptomics to the tidyverse

Perform differential transcription testing using edgeR quasi-likelihood (QLT), edgeR likelihood-ratio (LR), limma-voom, limma-voom-with-quality-weights or DESeq2

Description

test_differential_abundance() 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_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = 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",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = NULL
)

## S4 method for signature 'tbl_df'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = NULL
)

## S4 method for signature 'tidybulk'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = NULL
)

## S4 method for signature 'SummarizedExperiment'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = NULL
)

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. If there is more than one factor, they should be in the order factor of interest + additional factors.
.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 format 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 vector of length three. 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), "edger_robust_likelihood_ratio", "DESeq2", "limma_voom", "limma_voom_sample_weights", "glmmseq_lme4", "glmmseq_glmmtmb"
test_above_log2_fold_change	A positive real value. This works for edgeR and limma_voom methods. It uses the 'treat' function, which tests that the difference in abundance is bigger than this threshold rather than zero https://pubmed.ncbi.nlm.nih.gov/19176553.
scaling_method	A character string. The scaling method passed to the back-end functions: edgeR and limma-voom (i.e., edgeR::calcNormFactors; "TMM","TMMwsp","RLE","upperquartile"). Setting the parameter to \"none\" will skip the compensation for sequencing-depth for the method edgeR or limma-voom.
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).
...	Further arguments passed to some of the internal experimental functions. For example for glmmSeq, it is possible to pass .dispersion, and .scaling_factor column tidyeval to skip the caluclation of dispersion and scaling and use precalculated values. This is helpful is you want to calculate those quantities on many genes and do DE testing on fewer genes. .scaling_factor is the TMM value that can be obtained with tidybulk::scale_abundance.
significance_threshold	DEPRECATED - A real between 0 and 1 (usually 0.05).
fill_missing_values	DEPRECATED - A boolean. Whether to fill missing sample/transcript values with the median of the transcript. This is rarely needed.
.contrasts	DEPRECATED - This parameter takes the format 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 vector of length three. The first covariate is the one the model is tested against (e.g., ~ factor_of_interest)

Details

'r lifecycle::badge("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, limma_voom_sample_weights https://doi.org/10.1093/nar/gkv412 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) |> // or glmFit according to choice 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()

Underlying method for glmmSeq framework:

counts = .data assay(my_assay)

# Create design matrix for dispersion, removing random effects design = model.matrix( object = .formula |> lme4::nobars(), data = metadata )

dispersion = counts |> edgeR::estimateDisp(design = design)

glmmSeq( .formula, countdata = counts , metadata = metadata |> as.data.frame(), dispersion = dispersion, progress = TRUE, method = method |> str_remove("(?i)^glmmSeq_" ), )

Value

A consistent object (to the input) with additional columns for the statistics from the 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 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).

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

 # edgeR

 tidybulk::se_mini |>
 identify_abundant() |>
	test_differential_abundance( ~ condition )

	# The function `test_differential_abundance` operates with contrasts too

 tidybulk::se_mini |>
 identify_abundant(factor_of_interest = condition) |>
 test_differential_abundance(
	    ~ 0 + condition,
	    contrasts = c( "conditionTRUE - conditionFALSE")
 )

 # DESeq2 - equivalent for limma-voom

my_se_mini = tidybulk::se_mini
my_se_mini$condition  = factor(my_se_mini$condition)

# demontrating with `fitType` that you can access any arguments to DESeq()
my_se_mini  |>
   identify_abundant(factor_of_interest = condition) |>
       test_differential_abundance( ~ condition, method="deseq2", fitType="local")

# testing above a log2 threshold, passes along value to lfcThreshold of results()
res <- my_se_mini  |>
   identify_abundant(factor_of_interest = condition) |>
        test_differential_abundance( ~ condition, method="deseq2",
            fitType="local",
            test_above_log2_fold_change=4 )

# Use random intercept and random effect models

 se_mini[1:50,] |>
  identify_abundant(factor_of_interest = condition) |>
  test_differential_abundance(
    ~ condition + (1 + condition | time),
    method = "glmmseq_lme4", cores = 1
  )

# confirm that lfcThreshold was used
## Not run: 
    res |>
        mcols() |>
        DESeq2::DESeqResults() |>
        DESeq2::plotMA()

## End(Not run)

# The function `test_differential_abundance` operates with contrasts too

 my_se_mini |>
 identify_abundant() |>
 test_differential_abundance(
	    ~ 0 + condition,
	    contrasts = list(c("condition", "TRUE", "FALSE")),
	    method="deseq2",
         fitType="local"
 )

stemangiola/ttBulk documentation built on Oct. 19, 2024, 1:34 p.m.