Log2 Fold Change Calculator
Log2 fold change is how gene expression work reports a shift between two conditions: a control of 100 and a treatment of 800 is not just “8 times higher,” it is a log2FC of 3.0000, on a scale where doubling reads +1 and halving reads -1. Enter two positive expression values on the same scale and this calculator returns the fold change, the log2 fold change to four decimals, and a regulation call at the strict +1 and -1 cutoffs.
Log2 fold change
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Quick Answer
A control of 100 and a treatment of 800 give a fold change of 8.0000 and a log2 fold change of 3.0000, called Up-regulated because 3.0000 is above the +1 cutoff. The call is strict: an exact doubling (log2FC 1.0000) still reads No Change, and only a value above +1 counts as up-regulated. The calculator above is pre-filled with this exact pair.
Key takeaways
- Log2FC is log2(treatment / control). Positive means higher in treatment, negative means lower, and the scale is symmetric: +2 and -2 are equal-sized moves in opposite directions.
- This tool uses strict cutoffs. Up-regulated needs log2FC above +1, down-regulated needs log2FC below -1, and everything in between, including exactly +1 and exactly -1, is No Change.
- Both inputs must be positive and on the same scale. A zero is rejected, not patched: pipelines that add a pseudocount are making a modelling choice this simple ratio does not make for you.
- A fold change alone is not evidence. In a real differential-expression study it sits next to an adjusted p-value from a tool such as DESeq2 or edgeR.
- Quick Answer
- How to use the log2 fold change calculator
- The log2 fold change formula, and why the scale is base 2
- Worked example: control 100, treatment 800, plus the cases that trip people up
- Zeros, negative values and low counts: what this tool does and does not do
- What other log2 fold change calculators miss
- Frequently asked questions
- Add This Calculator to Your Website
- References
How to use the log2 fold change calculator
Enter one control value and one treatment value, both positive and measured the same way, then click Calculate. The result shows the log2 fold change as the headline number, with the plain fold change and the regulation call underneath.
- Control expression: the reference condition, for example untreated samples or healthy tissue. It is the denominator, which is why it can never be zero here.
- Treatment expression: the compared condition, for example treated samples or tumour tissue, in the same units and the same normalization as the control.
- Read the three outputs together: the fold change says how many times bigger or smaller the treatment is, the log2FC puts that ratio on the symmetric scale used in volcano plots and MA plots, and the regulation call applies the strict +1 / -1 rule.
- Check the boundary before you report a call: a treatment of 200 against a control of 100 is a genuine doubling, yet this tool labels it No Change because 1.0000 is not above +1. If your study defines differential expression as |log2FC| of 1 or more, say so in your methods and treat the boundary cases by your stated rule, not by this label.
The two values must be the same kind of number. Raw counts, normalized counts, TPM and FPKM all work, provided both sides match. Sample handling before the numbers exist matters just as much: a chemical dilution calculator is the sort of prep check that keeps a qPCR or library dilution from quietly shifting one condition before any fold change is computed.
The log2 fold change formula, and why the scale is base 2
Fold change is the treatment divided by the control, and log2 fold change is the base-2 logarithm of that ratio. The two forms below are the same calculation; the second is how most software computes it internally.
Log2 fold change = log2(Fold change) = log2(Treatment) − log2(Control)
Back-conversion: Fold change = 2log2FC
Base 2 is used because expression changes are usually described in doublings. On the plain fold-change scale, increases run 2, 4, 8, 16 toward infinity while decreases are squeezed into 0.5, 0.25, 0.125 toward zero, so a fourfold rise looks four times as dramatic as a fourfold fall. Log2 fixes that: +2 and -2 sit the same distance from zero. If you want to work the logarithm itself by hand, the log base 2 calculator evaluates log2 of any positive number, and the exponential and logarithmic equations calculator covers the wider family of log and power equations behind the back-conversion.
| Log2FC | Fold change | Meaning | This tool calls it |
|---|---|---|---|
| 3 | 8x | 8 times higher | Up-regulated |
| 2 | 4x | 4 times higher | Up-regulated |
| 1 | 2x | Doubled | No Change (strict cutoff) |
| 0.585 | 1.5x | Common 1.5-fold screening cutoff | No Change |
| 0 | 1x | No change | No Change |
| -1 | 0.5x | Halved | No Change (strict cutoff) |
| -2 | 0.25x | Quartered | Down-regulated |
| -3 | 0.125x | 8 times lower | Down-regulated |
Worked example: control 100, treatment 800, plus the cases that trip people up
The pre-filled run is a gene at 100 in control tissue and 800 in treated tissue. Every row below was run through the tool above; the boundary rows are the ones readers most often misread.
| Control | Treatment | Fold change | Log2FC | Regulation |
|---|---|---|---|---|
| 100 | 800 | 8.0000 | 3.0000 | Up-regulated |
| 100 | 200 | 2.0000 | 1.0000 | No Change (exact boundary) |
| 100 | 150 | 1.5000 | 0.5850 | No Change |
| 800 | 100 | 0.1250 | -3.0000 | Down-regulated |
| 250 | 40 | 0.1600 | -2.6439 | Down-regulated |
Step by step for the headline run: fold change = 800 / 100 = 8.0000, then log2(8) = 3.0000, and 3.0000 is greater than +1, so the call is Up-regulated. Reverse the pair (control 800, treatment 100) and the same gene reads -3.0000 and Down-regulated, exactly as far below zero as it was above it. That mirror symmetry is the whole reason the log scale exists, and it is also what separates this statistic from an ordinary percentage comparison such as the one a general change calculator reports, where a rise from 100 to 800 is +700% but the fall back is only -87.5%.
Zeros, negative values and low counts: what this tool does and does not do
A zero or negative entry never reaches the logarithm in this tool. The calculator checks both inputs in order and stops with a specific message: a blank or non-numeric control asks for a valid positive control value, a control of zero or less says the control value must be greater than zero, and the same two checks then apply to the treatment value.
That refusal is mathematically correct for a plain ratio: log2 of zero is undefined and log2 of a negative ratio does not exist. Real RNA-seq pipelines handle zeros differently, and the difference matters. DESeq2 and similar tools work from replicate counts with a fitted model, and several fold-change conventions add a small pseudocount first, for example log2((treatment + 0.5) / (control + 0.5)), which turns a 0-against-100 comparison into a finite, shrunken number instead of an error. Which convention a pipeline uses changes the reported log2FC for low-count genes a lot, and barely touches high-count genes. This page deliberately keeps the textbook ratio with no pseudocount, so the number you get here is the number the formula promises. If your data contain zeros, add your pseudocount to both values yourself before entering them, and report that you did.
Two related warnings from practice. First, never enter values that are already log-transformed: if your table already holds log2 expression, the fold change is the simple difference of the two logged values, and running them through this ratio again double-logs the data. Second, a large log2FC from tiny counts (1 against 1,000 gives -9.9658 here) is arithmetically right and biologically weak; count depth is exactly what the statistical layer of a pipeline such as the one described in the DESeq2 vignette from Bioconductor is built to weigh.
What other log2 fold change calculators miss
The pages and tools that surface for this keyword mostly are not single-pair calculators at all, and the ones that are tend to hide the decisions that change the answer:
- TrueGeometry fold change tutorials: long RNA-seq explainers whose calculator quietly builds in a +0.5 pseudocount, so a zero count returns a finite number instead of an error. That is a defensible pipeline choice, but presenting it as the plain fold change formula misleads anyone checking hand calculations. This page states its no-pseudocount rule openly.
- Oreate AI Excel walkthrough: teaches the spreadsheet route but writes the formula with the natural log function in a way that flips the subtraction order, so a reader following it literally can get the sign backwards. Sign errors are the most common log2FC mistake in student work.
- DESeq2 and Seurat documentation: authoritative, and the right place for real analysis, but they answer a different question. Their log2FC values are model-based, shrunken estimates across replicates, not the ratio of two numbers, and no doc page gives a quick two-value check.
- Generic fold change calculators: return the ratio only, with no log transform, no regulation call and no threshold context, leaving the reader to find the +1 / -1 convention elsewhere.
- The gap this page fills: the exact textbook ratio and log2 to four decimals, the regulation call with its strict boundary behaviour stated in plain words (an exact doubling is No Change here), the zero and pseudocount question answered honestly, and a reference table that ties each log2FC band to the call this tool actually makes.
Frequently asked questions
The base-2 logarithm of the ratio of expression between two conditions. It compresses large ratios into a symmetric scale where +1 means doubled and -1 means halved.
Plain fold change is asymmetric: doublings run 2, 4, 8 to infinity while halvings squeeze into 0.5, 0.25, 0 toward zero. The log2 transform makes up and down changes mirror images, which is easier to plot and interpret.
Positive means higher in treatment than control; negative means lower. A log2FC of 2 is a fourfold increase; -1 is a twofold decrease. This tool calls values above +1 up-regulated and below -1 down-regulated.
Common practice uses |log2FC| greater than 1 (a twofold change), sometimes 0.585 (a 1.5-fold change) for more sensitive screens. Thresholds vary by study; always report the one you used, and pair it with a significance test rather than using fold change alone.
Raise 2 to the power of the log2FC: fold change = 2^(log2FC). A log2FC of 3 gives 2^3 = 8.
Division by zero is undefined, so the calculator requires positive values. In real RNA-seq analysis, a small pseudocount (such as 0.5) is added to both conditions before computing the ratio; some pipelines use log2((T+0.5)/(C+0.5)).
Exactly +1 (a perfect doubling) and exactly -1 (a perfect halving) are both reported as No Change, because the tool uses strict cutoffs: up-regulated requires a log2FC above +1 and down-regulated requires a log2FC below -1. A treatment of 200 against a control of 100 gives log2FC 1.0000 and No Change; 200.01 gives 1.0001 and Up-regulated.
Yes for TPM, FPKM, normalized counts or any other positive expression measure, as long as both values use the same measure and the same normalization. Do not enter raw Ct values directly: Ct runs backwards (lower Ct means more RNA) and needs the delta-delta Ct conversion first, where log2FC equals negative delta-delta Ct.
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References
- Love, M.I., Huber, W. and Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biology. Accessed October 22, 2026.
- Anders, S. and Huber, W. Differential expression analysis for sequence count data, Genome Biology. Accessed October 22, 2026.
- Bioconductor. Analyzing RNA-seq data with DESeq2 (vignette). Accessed October 22, 2026.
- National Center for Biotechnology Information. Gene Expression Omnibus: Overview. Accessed October 22, 2026.
- SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium, Nature Biotechnology. Accessed October 22, 2026.
- Bullard, J.H. et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments, BMC Bioinformatics. Accessed October 22, 2026.