Step: Differential Expression Analysis (DEA) using Limma

Run differential analysis using linear model-based analysis via glystats::gly_limma(), then filter the experiment to keep only the differentially expressed variables using glystats::filter_sig_vars(). By default, this runs DEA on the main experiment (exp), but can be configured to run on derived traits (trait_exp) or other experiment objects. This step is the recommended DEA method for all experiments, for both two-group and multi-group experiments.

Usage

step_dea_limma(
  on = "exp",
  p_adj_method = "BH",
  covariate_cols = NULL,
  subject_col = NULL,
  ref_group = NULL,
  contrasts = NULL,
  filter_p_adj_cutoff = 0.05,
  filter_p_val_cutoff = NULL,
  filter_fc_cutoff = NULL,
  ...
)

Arguments

on: Name of the experiment data in ctx$data to run analysis on. Default is "exp" for differential expression analysis. Use "trait_exp" for differential trait analysis. Use "motif_exp" for differential motif analysis.
p_adj_method: A character string specifying the method for multiple testing correction. Must be one of the methods supported by stats::p.adjust(). Default is "BH" (Benjamini-Hochberg). Set to NULL to skip p-value adjustment.
covariate_cols: (Only for gly_limma()) A character vector specifying column names in sample information to include as covariates in the limma model. Default is NULL.
subject_col: (Only for gly_limma()) A character string specifying the column name in sample information that contains subject identifiers for paired comparisons. Default is NULL.
ref_group: A character string specifying the reference group. If NULL (default), the first level of the group factor is used as the reference. Only used for two-group comparisons.
contrasts: A character vector specifying custom contrasts. If NULL (default), all pairwise comparisons are automatically generated, and the levels coming first in the factor will be used as the reference group. Supports two formats: "group1-group2" or "group1_vs_group2". Use the second format if group names contain hyphens. "group1" will be used as the reference group.
filter_p_adj_cutoff: Adjusted p-value cutoff for filtering.
filter_p_val_cutoff: Raw p-value cutoff for filtering.
filter_fc_cutoff: Fold change cutoff for filtering.
...: Additional arguments passed to limma::lmFit().

Value

A glysmith_step object.

Details

Data required:

exp (if on = "exp"): The experiment to run DEA on
trait_exp (if on = "trait_exp"): The trait experiment to run DEA on
motif_exp (if on = "motif_exp"): The motif experiment to run DEA on

Data generated:

dea_res: The DEA (differential expression analysis) results (if on = "exp", default)
dta_res: The DTA (differential trait analysis) results (if on = "trait_exp")
dma_res: The DMA (differential motif analysis) results (if on = "motif_exp")
sig_exp: The filtered experiment (if on = "exp", default)
sig_trait_exp: The filtered trait experiment (if on = "trait_exp")
sig_motif_exp: The filtered motif experiment (if on = "motif_exp")

Tables generated:

dea: A table containing the DEA (differential expression analysis) result (if on = "exp", default)
dta: A table containing the DTA (differential trait analysis) result (if on = "trait_exp")
dma: A table containing the DMA (differential motif analysis) result (if on = "motif_exp")

AI Prompt

This section is for AI in inquire_blueprint() only.

Use this step to perform DEA by default, unless the user asks for other methods.

Examples

step_dea_limma()
#> <step "step_dea_limma()"> Differential expression analysis (limma)
step_dea_limma(on = "trait_exp")  # Differential trait analysis
#> <step "step_dea_limma(on = \"trait_exp\")"> Differential trait analysis (limma)
step_dea_limma(p_adj_method = "BH")
#> <step "step_dea_limma(p_adj_method = \"BH\")"> Differential expression analysis
#> (limma)