What is Log2 Fold Change in Bioinformatics
What is Log2 Fold Change?
The log2 fold change (log2FC) term is commonly used in bioinformatics to measure the changes in gene expression between two conditions (e.g. control vs. treated).
log2 fold change is the ratio of expression levels between two conditions and it is calculated by taking log2 of the ratio of expression levels of two conditions.
For example, you have two conditions such as control and treated, and you measured the expression changes (read counts) between two conditions across several genes. The log2 fold change for a particular gene can be calculated as:
Where, T is the gene expression count in the treated sample and C is the gene expression count in the control sample.
Why to use Log2 Fold Change?
Easy to understand changes in gene expression
Log2 fold change helps to measure the changes in gene expression in different directions equally from zero.
For example, a log2 fold change of 1 (positive fold change), indicates that the gene is upregulated by a factor of 2. Similarly, a log2 fold change of -1 (negative fold change) indicates that the gene is downregulated by a factor of 2.
This symmetry in both directions helps easy to understand the changes in gene expression.
It also helps in easy comparison allowing visualization and comparison in plots like volcano plots, heatmaps, MA plot, etc.
Biological relevance
Gene expression changes could be significantly different with very high numbers (sometimes exponential) under varying biological conditions.
Log transformation simplifies these changes in gene expression and makes the results more interpretable in a biological context.
For instance, a log2 fold change of 1 indicates a change in gene expression by a factor of 2 (i.e. doubling of expression).
Log2 Fold Change calculation
Consider a gene with an expression count of 500 in the control sample and 2000 in the treated sample (i.e. gene is upregulated in the treated sample by 4 fold)
Conversely, if the expression counts are 2000 in the control sample and 500 in the treated sample (i.e. gene is downregulated in the treated sample by 4 fold):
You can see that log2 transformation helped easy comparison in gene expression changes in the control and treated samples. Without log2 transformation, we will get the values of 4 for upregulation and 0.25 for downregulation.
Hence, log2 fold change is a straightforward and meaningful way to describe changes in gene expression.
In Bioinformatics, the log2 fold change is reported by various tools such as DESeq2, EdgeR, etc.
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