adjust_abundance-methods: Adjust transcript abundance for unwanted variation

In stemangiola/tidybulk: Brings transcriptomics to the tidyverse

Adjust transcript abundance for unwanted variation

Description

adjust_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 an additional adjusted abundance column. This method uses scaled counts if present.

Usage

adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

## S4 method for signature 'spec_tbl_df'
adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

## S4 method for signature 'tbl_df'
adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

## S4 method for signature 'tidybulk'
adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

## S4 method for signature 'SummarizedExperiment'
adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = 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	DEPRECATED - A formula with no response variable, representing the desired linear model where the first covariate is the factor of interest and the second covariate is the unwanted variation (of the kind ~ factor_of_interest + batch)
.factor_unwanted	A tidy select, e.g. column names without double quotation. c(batch, country) These are the factor that we want to adjust for, including unwanted batcheffect, and unwanted biological effects.
.factor_of_interest	A tidy select, e.g. column names without double quotation. c(treatment) These are the factor that we want to preserve.
.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 character string. Methods include combat_seq (default), combat and limma_remove_batch_effect.
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 parameters passed to the function sva::ComBat
log_transform	DEPRECATED - A boolean, whether the value should be log-transformed (e.g., TRUE for RNA sequencing data)
transform	DEPRECATED - A function that will tranform the counts, by default it is log1p for RNA sequencing data, but for avoinding tranformation you can use identity
inverse_transform	DEPRECATED - A function that is the inverse of transform (e.g. expm1 is inverse of log1p). This is needed to tranform back the counts after analysis.

Details

'r lifecycle::badge("maturing")'

This function adjusts the abundance for (known) unwanted variation. At the moment just an unwanted covariate is allowed at a time using Combat (DOI: 10.1093/bioinformatics/bts034)

Underlying method: sva::ComBat(data, batch = my_batch, mod = design, prior.plots = FALSE, ...)

Value

A consistent object (to the input) with additional columns for the adjusted counts as '<COUNT COLUMN>_adjusted'

A consistent object (to the input) with additional columns for the adjusted counts as '<COUNT COLUMN>_adjusted'

A consistent object (to the input) with additional columns for the adjusted counts as '<COUNT COLUMN>_adjusted'

A consistent object (to the input) with additional columns for the adjusted counts as '<COUNT COLUMN>_adjusted'

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

cm = tidybulk::se_mini
cm$batch = 0
cm$batch[colnames(cm) %in% c("SRR1740035", "SRR1740043")] = 1

cm |>
identify_abundant() |>
adjust_abundance(	.factor_unwanted = batch, .factor_of_interest =  condition, method="combat"	)

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