Turn concentrations into interpretable log2 changes in seconds. Support replicates, batch comparisons, and clear thresholds. Export CSV or PDF summaries for clean lab reports.
Example intensities or concentrations from a chemical assay. Use these to test single or batch mode.
| Analyte | Control | Treated | Comment |
|---|---|---|---|
| Metabolite A | 120 | 240 | Exact 2× increase → log2FC ≈ 1 |
| Metabolite B | 80 | 60 | Decrease → negative log2FC |
| Metabolite C | 0 | 12 | Zero control needs a pseudocount |
| Metabolite D | 35 | 35 | No change → log2FC ≈ 0 |
The log2 fold change expresses the magnitude of change on a base‑2 logarithmic scale.
In analytical chemistry, peak areas, absorbance, and concentrations can span orders of magnitude. Log2 fold change converts multiplicative behavior into an additive scale, so a doubling and a halving are symmetric around zero. This makes comparisons clearer across compounds, dilutions, and instrument batches.
Data processing may yield zeros after blank subtraction or limit‑of‑detection filtering. Adding a small pseudocount to both conditions prevents division by zero and keeps the logarithm defined. A typical starting point is 1e‑6 for normalized signals, or 0.5× the smallest positive value observed. Keep the pseudocount below the noise floor to avoid inflating very low analytes.
A log2FC of 1.00 indicates a two‑fold increase, while −1.00 indicates a two‑fold decrease. A log2FC of 0.5 equals roughly 1.41×, useful for subtle pathway shifts in kinetics screens too. Example: control 120 and treated 240 give ratio 2.00 and log2FC 1.00. Example: control 80 and treated 60 give ratio 0.75 and log2FC about −0.415, a 25% drop. If control is 0 and treated is 12, a pseudocount of 0.001 yields ratio 12001 and log2FC about 13.55, which should be flagged as “driven by zero”.
Replicate injections quantify drift, matrix effects, and extraction variability. Summarizing by mean is efficient for approximately symmetric noise; median is robust when one replicate suffers carryover or transient ion suppression. Reporting sample SD helps reviewers judge precision. Pair this with QC criteria such as relative SD targets for internal standards.
For panels of dozens to thousands of features, batch mode processes rows as “Name, Control, Treated” and outputs one consistent table. This supports fast triage: sort by absolute log2FC, filter by threshold, and focus on high‑confidence changes. Use normalization where possible to improve comparability across runs.
CSV export enables quick filtering, pivoting, and merging with annotation tables. PDF export preserves the computed metrics for reports and audit trails. Before sharing, confirm units match, blanks were handled consistently, and controls are not extremely small relative to the pseudocount. Document the threshold used, the pseudocount value, and whether replicates were summarized by mean or median.
It means the treated value is twice the control value after the pseudocount adjustment. The ratio is 2.00, commonly treated as a strong increase.
Zeros make ratios infinite and logs undefined. A small pseudocount stabilizes calculations and allows consistent comparison when values are at or below detection limits.
Use mean for roughly symmetric measurement noise. Use median when you suspect outliers from carryover, integration artifacts, or transient instrument issues.
A common default is |log2FC| ≥ 1, equivalent to a 2× change. For subtle changes, lower it only after checking precision and noise.
Only after consistent normalization and calibration. Without harmonized preprocessing, differences may reflect instrument response rather than true chemical change.
Percent change is derived from the ratio: (ratio − 1) × 100. It expresses relative increase or decrease on a linear scale.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.