limma_value: DEG analysis function

In espors/idepGolem: Integrated Differential Expression and Pathway Analysis

DEG analysis function

Description

Use the limma or DESeq2 package to perform DEG analysis with the specified model design. Core function for the DEG panel of iDEP.

Usage

limma_value(
  data_file_format,
  counts_deg_method,
  raw_counts,
  limma_p_val,
  limma_fc,
  select_model_comprions,
  sample_info,
  select_factors_model,
  select_interactions,
  select_block_factors_model,
  factor_reference_levels,
  processed_data,
  counts_log_start,
  p_vals,
  threshold_wald_test = FALSE,
  independent_filtering = TRUE,
  descr = ""
)

Arguments

data_file_format	Type of gene data being examined
counts_deg_method	The method or package being used for the DEG analysis
raw_counts	The matrix of counts before processing for gene expression data
limma_p_val	Significant p-value to use for expressed genes
limma_fc	Minimum fold-change cutoff for the DEG analysis
select_model_comprions	Selected comparisons to analyze in the DEG analysis
sample_info	Experiment file information for grouping
select_factors_model	The selected factors for the model expression
select_interactions	The interaction terms being used in the model design
select_block_factors_model	The selected factors for batch effect
factor_reference_levels	Vector of reference levels to use for the selected factors
processed_data	Data that has been through the pre-processing
counts_log_start	The constant added to the log transformation from pre-processing
p_vals	The vector of p-vals calculated in pre-process for significant expression
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	whether or not to conduct independent filtering in DESeq2 results function.

Value

List with the results of the DEG analysis. When the function is successful there are four entries in the list. "results" is a matrix with the same dimensions as the processed data. The entries in "results" are c(-1, 0, 1) for (negative fold change, no significant change, positive fold change) respectively. The second entry is "comparisons" and is a character vector of the different comparisons that were analyzed in the function. Third is "exp_type" and details the model expression that was used for the DEG analysis. Lastly is "top_genes" which is itself a list. The "top_genes" list has an entry for each comparison. Each entry is a data frame with two columns. One column is the calculated fold change for the comparison and the other is the adjusted p-value for the fold change calculation.

espors/idepGolem documentation built on Oct. 27, 2024, 4:56 a.m.