RNA SEQ data analysis in R - investigate differentially expressed genes in the data!

In this tutorial , The negative binomial is used in R Use in DESeq2,pheatmap and tidyverse The package performs differential gene expression analysis .RNA-Seq The workflow of data is ：

Get from the sequencing device FASTQ Sequencing file
assessment quality Sequencing readings
perform genome alignment To identify the source of the read
Generation ratio count matrix Right reading , That is, the number of readings compared with the exons of each gene .

The datasets used in this tutorial are from published Hammer wait forsomeone 2010 Year study . Read count matrix and metadata from Recount The project website gets in short ,Hammer Two weeks and two months later, spinal cord nerve ligation in rats was studied (SNL) And control （ normal ） The effect of the sample . In this tutorial , We explored the difference in gene expression between the first and second time points and the difference in multiple changes between the two time points .

http://bowtie-bio.sourceforge.net/recount/

stay R Load data gene count and metadata

This study has 8 Samples , namely 4 individual controls and 4 Samples spinal nerve ligation.meta data Include sample features , And there are some typos that I corrected manually （ Check the download link above ）.

Use the following R Code import reads count data ：
Use View Function to check the complete data set .

read_Count <- read.table("hammer_count_table.txt"</