📖 Master User Guide & Scientific Principles (Click to Expand)
⚠️ Disclaimer: This software is not intended or approved for medical, diagnostic, or clinical use.
🚀 Quick Start Workflow
  1. Metadata: Enter experiment name and date.
  2. Sample Setup: Add samples with Name, Target, and Role (Control/Condition/Standard).
  3. Plate A: Select a sample color and click/paint wells to assign positions (typically technical triplicates).
  4. Plate B: Enter or paste Ct values directly from your qPCR machine export.
  5. Analysis: View dynamic charts and export your results (PDF/JPEG/JSON).
💡 Confused? Try a Template

Want to see a fully filled plate with standard curves and analysis? Load our "Golden Standard" example to explore all features instantly.

🤖 How to Prompt the AI

To get a perfect plate design, explain your experiment dimensions clearly to Gemma:

"I have 2 cell lines (HeLa and MCF7) treated with DrugX or DMSO. I am measuring HOXA1 expression and using GAPDH as my reference gene. I also need a 5-point standard curve for all targets starting at 100ng."

The AI will then design a symmetrical matrix and assign the correct roles (Control, Condition, Standard) for you.

1. RT‑qPCR (2^-ΔΔCt)

The gold standard for relative gene expression.

  • ΔCt: Target gene Ct normalized to a reference (housekeeping) gene.
  • ΔΔCt: Sample ΔCt relative to a control (e.g., WT or untreated).
  • Fold Change: Calculated as 2 to the power of negative ΔΔCt.
2. Calibration Range (Standard Curve)

Quantifies COPY NUMBERS or PRIMER EFFICIENCY.

  • Regression: Maps Ct values against log10(Known Quantities).
  • Usability: Use the Generator to create 1:10 or custom dilution series instantly.
  • Efficiency (E): Calculated as (10^-1/slope - 1). Standard range is 90-110%.
  • Integrated Analysis: Standards co-exist with unknowns on the same plate, automatically calibrating your whole experiment.
3. RT‑qPCR (Pfaffl Method)

Superior accuracy for relative gene expression.

  • Efficiency Corrected: If you run a standard curve, the software automatically uses the Pfaffl Equation to calculate fold change.
  • Logic: Ratio = (E_target^ΔCt_target) / (E_ref^ΔCt_ref). It no longer assumes 100% efficiency.
  • Mixed Plates: Analyze standard curves and experimental treatments side-by-side.
4. Multi‑condition (ANOVA)

For experiments with 3+ biological groups.

  • Stats: Applies Welch's t-test for pairwise comparisons.
  • FDR: Uses Benjamini-Hochberg (BH) correction to prevent false positives in large gene sets.
4. Dose‑Response / Time‑Course

Tracking kinetic shifts or drug sensitivity.

  • Visualization: Defaults to Log2 Y-axis to show symmetrical up/down regulation trends.
  • Trend: Best for identifying IC50 or activation peaks.
5. Reference Gene Screening

Validating your normalization controls.

  • Multi-Ref: Allows you to select multiple housekeeping candidates.
  • Logic: Averages the Ct values of selected refs for a more stable normalization baseline.
6. SNP Genotyping

Allelic discrimination between variants.

  • Cluster Plot: Uses 2-dye fluorescence (e.g., FAM/VIC) to group samples.
  • Calls: Identifies Homozygous (Allele 1/2) and Heterozygous clusters.
7. Technical QC Plate

Validating pipetting and equipment precision.

  • Outliers: Uses Z-score or IQR to flag "bad wells" automatically.
  • Precision: Monitors CV% (Coefficient of Variation) across replicates.
8. Epigenetic Enrichment

For Cut&Run, Cut&Tag, or ChIP-qPCR.

  • Control: Usually normalized against an IgG background.
  • Result: Displays Fold Enrichment over the non-specific background.
📊 Analytical Principles & Settings
1. Calculation Mode
  • ΔCt: (Target - Reference). Raw expression.
  • 2^-ΔCt: Relative expression level.
  • ΔΔCt: (ΔCt Sample - ΔCt Control).
  • 2^-ΔΔCt: Fold Change relative to Control.
2. QC (Outliers)
  • Z-score: Best for triplicates. Flags wells > N standard deviations from mean.
  • IQR: Removes values outside 1.5x the Interquartile Range.
3. Error & Scale
  • SD / SEM: Deviation (spread) vs. Error (precision of mean).
  • Linear / Log2: Standard Y-axis vs. Symmetrical up/down regulation.
4. Statistics
  • t-test: Compares two groups (e.g., WT vs KO).
  • ANOVA: Compares 3+ groups to prevent false discoveries.
  • BH (FDR): Corrects p-values for high-throughput (5+ genes).
5. Standard Curves & Toolboxes
  • Pfaffl: Adjusts for variable primer efficiency (e.g., 95% vs 100%).
Quick Noise Cleanup: If your error bars are massive, tick QC (Z-score) in the settings bar and set Z to 1.5. This often cleans up technical noise from bad wells automatically.

🧙‍♀️ Gemma: Your Plate Whisperer Waking up your private AI...

Private AI Assistant (Google Gemma 4)
🛡️ Data Safety: Your research and chat history remain private.
📜 Licensing: Powered by Gemma. Distributed under the Apache License 2.0.
💡 Friendly tip: Even experts make mistakes! Please verify the plate maps before starting your lab work.
Hello! I am the Sample Architect.

I can automatically generate your complete sample list. To help me map everything perfectly, please tell me your:
1. Groups (e.g., cell lines)
2. Targets (e.g., genes of interest, reference genes)
3. Treatments (e.g., antibodies, drugs, time points)

Experiment metadata

Sample setup

Use "Name_Target_Condition". Group controls what’s analyzed together.

🧪 Generate Calibration Range (Standard Curve / Dilutions)

This will automatically create a series of samples with numeric condition names (concentrations).

Plate A — Assign samples/conditions to wells

Sample Selector & Legend (Click to paint wells)

Master Mix Planner

Target Assigned wells Extra wells Total wells used
Totals per target = per‑well × (Assigned + Extra).
Reagent µL per well
Totals per target
Total per well (µL)
Master tube total per target (µL)

Plate B — Enter Ct values

Paste a column or 2D block from Excel/Sheets starting at the focused cell, or click "Import qPCR File" (compatible with standard "Quantification Plate View Results" files).

Ct summary — All Groups