README.Rmd

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output: github_document
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knitr::opts_chunk$set(
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# diffexpR

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The goal of `diffexpR` is to easy do differential expression analysis.

## Installation

You can install the released version of diffexpR from [Github](https://github.com/Moonerss/diffexpR) with:

``` {r eval=FALSE}
install.packages('remotes')
remotes::install_github('Moonerss/diffexpR')
```

## Usage

We can do differential expression analysis underline the methods apply by `limma`, `DESeq2`, and `edgeR`.

```{r example}
library(diffexpR)

## add data
data("OSCC")
data("eset")

## do differential expression analysis in array data
array_res <- diff_limma_array(array_mat = eset$array, label_list = eset$group, groups = 'brain-liver')
head(array_res)

## do differential expression analysis in count data
limma_count_res <- diff_limma_count(count_mat = OSCC$count, label_list = OSCC$group, groups = 'tumor-normal', methods = 'voom')
head(limma_count_res)

deseq2_count_res <- diff_deseq2_count(count_mat = OSCC$count, label_list = OSCC$group, ref.groups = 'normal')
head(deseq2_count_res)

edger_count_res <- diff_edger_count(count_mat = OSCC$count, label_list = OSCC$group, groups = 'tumor-normal')
head(edger_count_res)

## do differential expression analysis in normalized rna-seq data
limma_normalize_res <- diff_limma_normalize(expr_mat = OSCC$rpkm, label_list = OSCC$group, groups = 'tumor-normal')
head(limma_normalize_res)
```

Compare the result of three different methods:

```{r compare}
library(dplyr)
library(ggVennDiagram)

edger_res <- rownames(filter(edger_count_res, FDR < 0.05, abs(logFC) > 1))
deseq2_res <- rownames(filter(deseq2_count_res, padj < 0.05, abs(log2FoldChange) > 1))
limma_res <- rownames(filter(limma_count_res, adj.P.Val < 0.05, abs(logFC) > 1))

## veen 
x <- list(limma_res = limma_res, edger_res = edger_res, deseq2_res = deseq2_res)
ggVennDiagram(x)
```