DEG_model_simple: Simple Fpkm ratio test DEG

In Roleren/ORFik: Open Reading Frames in Genomics

Simple Fpkm ratio test DEG

Description

If you do not have a valid DESEQ2 experimental setup (contrast), you can use this simplified test

Usage

DEG_model_simple(
  df,
  target.contrast = design[1],
  design = ORFik::design(df),
  p.value = 0.05,
  counts = countTable(df, "mrna", type = "summarized"),
  batch.effect = FALSE
)

Arguments

df	an experiment of usually RNA-seq.
target.contrast	a character vector, default design[1]. The column in the ORFik experiment that represent the comparison contrasts. By default: the first design factor of the full experimental design. This is the factor you will do the comparison on. DESeq will normalize the counts based on the full design, but the log fold change values will be based on this contrast only. It is usually the 'condition' column.
design	a character vector, default design(df.rfp). The full experiment design. Which factors have more than 1 level. Example: stage column are all HEK293, so it can not be a design factor. The condition column has 2 possible values, WT and mutant, so it is a factor of the experiment. Replicates column is not part of design, that is inserted later with setting batch.effect = TRUE. Library type 'libtype' column, can also no be part of initial design, it is always added inside the function, after initial setup.
p.value	a numeric, default 0.05 in interval (0,1). Defines adjusted p-value to be used as significance threshold for the result groups. I.e. for exclusive translation group significant subset for p.value = 0.05 means: TE$padj < 0.05 & Ribo$padj < 0.05 & RNA$padj > 0.05.
counts	a SummarizedExperiment, default: countTable(df, "mrna", type = "summarized"), all transcripts. Assign a subset if you don't want to analyze all genes. It is recommended to not subset, to give DESeq2 data for variance analysis.
batch.effect	logical, default TRUE. Makes replicate column of the experiment part of the design. If you believe you might have batch effects, keep as TRUE. Batch effect usually means that you have a strong variance between biological replicates. Check out pcaExperiment and see if replicates cluster together more than the design factor, to verify if you need to set it to TRUE.

Value

a data.table of fpkm ratios

Examples

## Simple example (use ORFik template, then use only RNA-seq)
df <- ORFik.template.experiment()
df <- df[df$libtype == "RNA",]
#dt <- DEG_model_simple(df)

Roleren/ORFik documentation built on Dec. 18, 2024, 11:39 p.m.