run_DESeq2: Differential Expression Analysis by DEseq2 Package
In HuaZou/MyRtools: Statistic Approaches on Data Analysis

Description Usage Arguments Details Value Author(s) Examples

DEseq2 requires the count data (a matrix of integer values) to input. The normalization method is to use the standard factor vector per feature.

run_DESeq2(dataset=ExpressionSet,
                  trim="none",
                  Group_info="Group",
                  Group_name=c("HC", "AA"),
                  Pvalue=0.05, Log2FC=1)

`trim,`	Character; filter to apply.(default: trim="none").
`Group_info,`	Character; design factor(default: "Group").
`Group_name,`	Character; (Required) the group for comparison.
`Pvalue,`	Numeric; significant level(default: 0.05).
`Log2FC,`	Numeric; log2FoldChange(default: 1).
`Expression,`	ExpressionSet; (Required) ExpressionSet object.

12/2/2021 Guangzhou China

a list object: DESeq object DESeq results significant difference with enriched directors

Hua Zou

data(ExprSetRawCount)

DESeq2_res <- run_DESeq2(dataset=ExprSetRawCount, Group_info="Group", Group_name=c("HC", "AA"), Pvalue=0.05, Log2FC=1)
DESeq2_res$res