deg_deseq2: Differential expression using DESeq2 package

In espors/idepGolem: Integrated Differential Expression and Pathway Analysis

Differential expression using DESeq2 package

Description

Used in the limma_value function to perform DEG analysis using the DESeq2 package. It is not recommended to use this function on its own.

Usage

deg_deseq2(
  raw_counts,
  max_p_limma = 0.05,
  min_fc_limma = 2,
  selected_comparisons = NULL,
  sample_info = NULL,
  model_factors = NULL,
  block_factor = NULL,
  reference_levels = NULL,
  threshold_wald_test = FALSE,
  independent_filtering = TRUE,
  descr = ""
)

Arguments

raw_counts	The matrix of counts before processing for gene expression data
max_p_limma	Significant p-value to use for the fold-change values
min_fc_limma	Minimum fold-change to include in the results
selected_comparisons	Comparisons being analyzed in the DEG analysis
sample_info	Experiment file information for grouping
model_factors	Vector of selected factors and interaction terms from the model design
block_factor	The selected factors for batch effect
reference_levels	Vector of reference levels to use for the selected factors
threshold_wald_test	whether to use threshold-based Wald test to test null hypothesis that the absolute value of fold-change is bigger than a value.
independent_filtering	If yes(default), conduct independent filtering

Value

The return value is the results of the DEG analysis. These results are filtered and formatted by the limma_value function. results, a data frame with up or down regulated genes for all comparisons comparisons, a vectors holding comparison_names, exp_type, a character holding experimental design or error messages. top_genes, a list, each elements hold the lfc & FDR for a comparison

espors/idepGolem documentation built on April 23, 2024, 1:11 p.m.