View source: R/Dif_expression_Analysis.R
DEG.analysis | R Documentation |
Expression analysis of 1 dimension, usually between conditions of RNA-seq.
Using the standardized DESeq2 pipeline flow.
Creates a DESeq model (given x is the target.contrast argument)
(usually 'condition' column)
1. RNA-seq model: design = ~ x (differences between the x groups in RNA-seq)
DEG.analysis(
df,
target.contrast = design[1],
design = ORFik::design(df),
p.value = 0.05,
counts = countTable(df, "mrna", type = "summarized"),
batch.effect = TRUE,
pairs = combn.pairs(unlist(df[, target.contrast]))
)
df |
an |
target.contrast |
a character vector, default |
design |
a character vector, default |
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$padj > 0.05. |
counts |
a SummarizedExperiment, default: countTable(df, "mrna", type = "summarized"), all transcripts. Assign a subset if you don't want to analyze all genes. It is recommended to not subset, to give DESeq2 data for variance analysis. |
batch.effect |
logical, default TRUE. Makes replicate column of the experiment
part of the design. |
pairs |
list of character pairs, the experiment contrasts. Default:
|
#' Analysis is done between each possible
combination of levels in the target contrast If target contrast is the condition column,
with factor levels: WT, mut1 and mut2 with 3 replicates each. You get comparison
of WT vs mut1, WT vs mut2 and mut1 vs mut2.
The respective result categories are defined as:
(given a user defined p value, shown here as 0.05):
Significant - p-value adjusted < 0.05 (p-value cutoff decided by 'p.value argument)
The LFC values are shrunken by lfcShrink(type = "normal").
Remember that DESeq by default can not
do global change analysis, it can only find subsets with changes in LFC!
a data.table with columns: (contrast variable, gene id, regulation status, log fold changes, p.adjust values, mean counts)
doi: 10.1002/cpmb.108
Other DifferentialExpression:
DEG.plot.static()
,
DEG_model()
,
DTEG.plot()
,
te.table()
,
te_rna.plot()
## Simple example (use ORFik template, then use only RNA-seq)
df <- ORFik.template.experiment()
df.rna <- df[df$libtype == "RNA",]
design(df.rna) # The full experimental design
design(df.rna)[1] # Default target contrast
#dt <- DEG.analysis(df.rna)
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