Log2 Fold Change Calculator
Paste control and treatment values and get the log2 fold change, linear fold change, and direction of regulation for every gene. Includes a pseudocount option for low counts and CSV export.
5 rows · columns: label, control, treatment
Added to both values to avoid division by zero at low counts.
| Gene | Control | Treatment | log2 FC | Fold change | Direction |
|---|---|---|---|---|---|
| TP53 | 120 | 480 | +1.991 | 3.98× | up |
| BRCA1 | 300 | 150 | -0.995 | 0.50× | down |
| EGFR | 50 | 800 | +3.973 | 15.71× | up |
| GAPDH | 1000 | 1050 | +0.070 | 1.05× | up |
| MYC | 0 | 240 | +7.913 | 241.00× | up |
Why Expression Change Is Measured on a Log2 Scale
Gene expression change is naturally multiplicative: a gene goes up twofold, fourfold, or tenfold, not by a fixed additive amount. On a raw scale this is awkward, because a doubling (2) and a halving (0.5) sit at very different distances from 1, so up- and down-regulation cannot be compared or plotted symmetrically. Taking the base-2 logarithm fixes this: a doubling becomes +1 and a halving becomes −1, equal magnitudes in opposite directions.
Base 2 is chosen deliberately over natural log or base 10 because each unit is one doubling, which is easy to reason about biologically: a log2 fold change of 3 is an eightfold increase. This is why differential-expression packages such as DESeq2 (Love et al., 2014) and edgeR (Robinson et al., 2010) report effect sizes in log2 fold change, and why it forms the horizontal axis of the volcano plot.
The one hazard is low counts. When a control value is zero or very small, the ratio explodes and the log2 fold change becomes unstable or undefined. Adding a pseudocount to both values, which this calculator supports, tames these ratios, and production pipelines go further with shrinkage estimators that pull noisy low-count fold changes toward zero. That is why fold change should never be read alone.
A fold change tells you the size of a change but not whether it is real. Pairing it with a significance test and multiple-testing correction is essential, which is what the false discovery rate calculator provides, and the volcano plot generator plots both axes at once. For a complete analysis from raw counts to a shrinkage-adjusted, corrected gene list, the bioinformatics analysis service runs the full pipeline.
How to Use This Calculator
Paste your data
One row per gene with a label, the control value, and the treatment value.
Set a pseudocount
Add a small pseudocount to both values to keep ratios stable at low or zero counts.
Read the fold changes
Get log2 fold change, linear fold change, and the direction of regulation for each gene.
Export the table
Export as a CSV to feed a volcano plot or a supplementary results table.
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Fold changes computed. Want a PhD to run the whole workup?
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Frequently Asked Questions
What is log2 fold change?
Log2 fold change is the base-2 logarithm of the ratio between a treatment value and a control value, log2(treatment / control). It expresses how much a gene's expression changed between two conditions on a symmetric scale: a log2 fold change of +1 means the expression doubled, −1 means it halved, and 0 means no change. It is the standard effect-size measure in differential gene expression analysis.
How do you calculate log2 fold change?
Divide the treatment value by the control value to get the fold change, then take the base-2 logarithm. For a gene expressed at 480 in treatment and 120 in control, the fold change is 4 and the log2 fold change is 2. To avoid dividing by zero when a value is very small or zero, a small pseudocount is added to both values first, which this calculator lets you set.
What does a log2 fold change of 1 mean?
A log2 fold change of 1 means the gene is expressed twice as highly in the treatment condition as in the control, a doubling. A value of 2 is a fourfold increase, and 3 is an eightfold increase, because each unit on the log2 scale corresponds to another doubling. Negative values mirror this: −1 is a halving and −2 is a quarter.
Why use log2 fold change instead of raw fold change?
Raw fold change is asymmetric: a doubling is 2 while a halving is 0.5, so up- and down-regulation are on different scales and are hard to compare or plot. Taking the base-2 logarithm makes them symmetric around zero, so +1 and −1 represent changes of equal magnitude in opposite directions. This symmetry is why log2 fold change is the x-axis of a volcano plot and the effect size reported by tools like DESeq2 and edgeR.
What is a meaningful log2 fold change threshold?
A common threshold is an absolute log2 fold change of 1, corresponding to at least a twofold change, often combined with a false discovery rate cutoff on the p-value. The right threshold depends on the biology and the noise in the data; a subtle but real regulatory change may have a smaller fold change, while noisy low-count genes can show large fold changes that are not significant. Fold change should always be paired with a significance test.
Should I filter by fold change or by p-value?
Both. Fold change measures the size of the effect while the p-value, after multiple-testing correction, measures the confidence that the effect is real. A gene with a large fold change but a non-significant adjusted p-value may be a low-count artefact, and a gene with a tiny fold change but a very small p-value may be real but biologically trivial. Volcano plots exist precisely to show these two axes together.
Related Analysis Tools
To plot fold change against significance, the volcano plot generator draws both axes, and the false discovery rate calculator corrects the p-values. To normalize raw counts before computing fold change, the RNA-seq normalization calculator converts to CPM, FPKM, and TPM. For the full differential-expression analysis, the bioinformatics analysis service delivers publication-ready results.
Reviewed by
Dr. Sarah Mitchell
Dr. Sarah Mitchell holds a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health and has over 15 years of experience in systematic review methodology and meta-analysis. She has authored or co-authored 40+ peer-reviewed publications in journals including the Journal of Clinical Epidemiology, BMC Medical Research Methodology, and Research Synthesis Methods. A former Cochrane Review Group statistician and current editorial board member of Systematic Reviews, Dr. Mitchell has supervised 200+ evidence synthesis projects across clinical medicine, public health, and social sciences. She reviews all Research Gold tools to ensure statistical accuracy and compliance with Cochrane Handbook and PRISMA 2020 standards.
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