How to normalize RNA-seq data in DESeq2?

Install and Load DESeq2

• Ensure that R and Bioconductor are installed on your system. Open R and execute the command `source("https://bioconductor.org/biocLite.R")` if Bioconductor isn't installed.

 

• Install DESeq2 if it hasn't been installed already by executing `biocLite("DESeq2")` in the R console.

 

• Load DESeq2 into your R session with the library command: `library(DESeq2)`.

Prepare Your Data

• Prepare a count matrix, where rows represent genes and columns represent samples. Ensure data is in a numeric matrix format.

 

• Create the metadata dataframe describing your experimental design, with rows as samples and columns for factors like condition, batch, etc.

Create DESeqDataSet Object

• Use `DESeqDataSetFromMatrix()` function with arguments for count matrix, metadata, and design formula to create a DESeqDataSet object.

 

• The command looks like this: `dds <- DESeqDataSetFromMatrix(countData = count_matrix, colData = metadata, design = ~ condition)`.

Normalize the Data

• Run the normalization regime by executing `dds <- estimateSizeFactors(dds)`. This method calculates normalization factors to adjust for differences in sequencing depth.

 

• Access the normalized counts with the function `counts(dds, normalized = TRUE)`, which gives you the count data scaled by the normalization factors.

Check for Outliers

• Create a sample-to-sample distance heatmap or PCA plot using the `plotPCA()` function on normalized data to inspect for outliers or abnormalities in sample clustering.

 

• Carefully examine these plots to identify any samples deviating significantly from expected clustering patterns.

Use the Normalized Data

• Now that your data is normalized, proceed with differential expression analysis or other downstream analyses using DESeq2 functions like `DESeq()` and `results()`.

 

• Use your normalized data for visualization or integration with other data types to derive meaningful biological insights.