When studying gene expression in the field of biology and genetics, it is common to come across the term ‘Log Fold Change‘ (LFC), which is used to quantify differences in gene expression between two conditions or experimental groups. In this post, we will explore what Log Fold Change is exactly and how it is calculated, as well as its importance in interpreting gene expression data.
Definition and Basic Concept of Log Fold Change
The Log Fold Change is a measure used to compare gene expression between two different conditions or groups. It represents the magnitude of the change in gene expression between these conditions. Although traditionally used to quantify gene expression changes, it can also be applied to other areas such as metataxonomy, assessing differences in the abundance of a microorganism between two conditions or environments.
The LFC is expressed in terms of base 2 logarithm and is calculated by dividing the gene expression level in experimental condition or group 1 by the gene expression level in experimental condition or group 2.
Calculation of Log Fold Change
The calculation of LFC involves several steps. First, the gene expression level is obtained for each condition, usually through techniques such as microarrays or RNA sequencing (RNA-seq). Next, the data is normalized to account for technical and biological variations, ensuring an accurate comparison. Once the expression levels are normalized, the LFC is calculated by taking the base 2 logarithm of the ratio between the two expression values:
LFC = log2 (A/B),
where A and B represent the expression levels of a gene in two different conditions.
Interpretation of Log Fold Change
A Log Fold Change (LFC) measures the magnitude of the gene expression change between two conditions.
- LFC values close to 0 (LFC = 0) indicate no change in gene expression between the two conditions.
- Positive LFC values (LFC > 0) indicate an increase in gene expression in condition A compared to B.
- Negative LFC values (LFC < 0) indicate a decrease in gene expression in condition A compared to B.
Furthermore, if of interest, a more detailed interpretation of the LFC can be performed and expressed on a decimal scale. This is achieved by raising 2 to the power of the LFC result (2LFC).
Interpretation of a positive LFC:
If the LFC between the two conditions resulted in 1.2 (LFC = 1.2), it means that the gene expression in condition A is 2LFC = 21.2 = 2.30 times the gene expression in condition B. In other words, the gene expression increases by 2.30 times in A compared to B.
Interpretation of a negative LFC:
If the LFC between the two conditions resulted in -2.7 (LFC = -2.7), it means that the gene expression in condition A is 2LFC = 2-2.7 = 0.15 times the gene expression in condition B. In other words, the gene expression decreases by 1/0.15= 6.67 times in A compared to B.
Importance and Limitations of Log Fold Change
The Log Fold Change is a valuable tool for analyzing and comparing gene expression as it provides a quantitative and easily interpretable measure of differences. It allows for identifying how gene expression changes and helps prioritize the most relevant genes in a study.
However, it’s important to note some limitations of the Log Fold Change. On its own, it does not provide information about the statistical significance of the observed changes. Additional tests, such as differential expression analyses based on the negative binomial distribution, are necessary to determine if the expression changes are statistically significant.
Additionally, the calculation of LFC can be influenced by technical and biological variability, as well as baseline levels of expression. Therefore, it is essential to consider these sources of variation and conduct careful analyses to avoid misinterpretations.
