analysis_DESeq: DESEQ2 analysis pipeline
In HCGB-IGTP/HCGB.IGTP.DAnalysis: Useful in-house functions for multiple analysis

analysis_DESeq

R Documentation

DESEQ2 analysis pipeline

Description

When running DESeq2 you usually add multiple terms to the matrix design. Test the effect of them

Usage

analysis_DESeq(
  OUTPUT_Data_dir_given,
  count_table,
  sample_sheet_given,
  list_of_cols,
  formula_given,
  int_threads = 2,
  sign_value.given = 0.05,
  LFC.given = log2(1.2),
  coef_n = NA,
  early_return = FALSE,
  comp_ID = NULL,
  cutoff.given = 0.9,
  localFit = FALSE,
  forceResults = FALSE,
  min_cutoff_to_plot = 3,
  max_cutoff_to_plot = 50,
  gene.annot = NULL,
  data_type = "mRNA",
  shrinkage.given = "apeglm"
)

Arguments

`OUTPUT_Data_dir_given`	Absolute path to store results
`count_table`	Dataframe/matrix of counts
`sample_sheet_given`	Samplesheet containing metadata information
`formula_given`	Design formula to use
`int_threads`	Number of threads to use
`sign_value.given`	Adjusted pvalue cutoff. Default=0.05,
`LFC.given`	Log Fold change cutoff. Default=log2(1.2),
`coef_n`	Number of the coefficient of results to test (if desired)
`early_return`	Whether to return exploratory results early or not
`comp_ID`	Tag name to include for each comparison
`cutoff.given`	add an option to include cutoff when removing Zeros
`localFit`	Use a fitType=local for mean dispersion fit in DESeq2
`forceResults`	Boolean to force re-run analysis if already generated in the folder provided
`min_cutoff_to_plot`	Minimun number of genes significant to continue analysis. Default=3
`max_cutoff_to_plot`	Number of genes significant to plot as candidates analysis. Default=50
`gene.annot`	Dataframe containing gene annotation (Default: NULL)
`shrinkage.given`	LFC shrinkage estimator provided. Available: apeglm, ashr or normal