adjust_abundance {tidybulk} | R Documentation |
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.
adjust_abundance( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, log_transform = TRUE, action = "add", ... ) ## S4 method for signature 'spec_tbl_df' adjust_abundance( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, log_transform = TRUE, action = "add", ... ) ## S4 method for signature 'tbl_df' adjust_abundance( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, log_transform = TRUE, action = "add", ... ) ## S4 method for signature 'tidybulk' adjust_abundance( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, log_transform = TRUE, action = "add", ... ) ## S4 method for signature 'SummarizedExperiment' adjust_abundance( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, log_transform = TRUE, action = "add", ... ) ## S4 method for signature 'RangedSummarizedExperiment' adjust_abundance( .data, .formula, .sample = NULL, .transcript = NULL, .abundance = NULL, log_transform = TRUE, action = "add", ... )
.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 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) |
.sample |
The name of the sample column |
.transcript |
The name of the transcript/gene column |
.abundance |
The name of the transcript/gene abundance column |
log_transform |
A boolean, whether the value should be log-transformed (e.g., TRUE for RNA sequencing data) |
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 |
'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, ...)
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
cm = tidybulk::se_mini cm$batch = 0 cm$batch[colnames(cm) %in% c("SRR1740035", "SRR1740043")] = 1 res = cm %>% tidybulk(sample, transcript, count) |> identify_abundant() |> adjust_abundance( ~ condition + batch )