Traditional Workflow
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Platelet Workflow
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Combining separate workflows into one
No reshaping, no reannotation, no loss of context.
Invite-only preview · rolling out weekly
Bench-side
statistics
simplified.
Design experiments, analyze data and visualize with tools built for your assay.
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Imatinib
Erlotinib
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Measurement
Number of cells▾
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Erlotinib
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Measurement
Number of cells▾
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Why Platelet
Replace scattered workflows with one structured platform.
Bench science still runs on a patchwork of Excel sheets, one-off scripts, and manual copy-paste between tools. Platelet replaces that with a single structured platform.
Go from plate to analysis to publication-ready figure without the handoffs, reformatting, and lost context in between.
You upload
read on
upload
upload
Platelet already knows
| Well | Condition | Conc. |
|---|---|---|
| A1 | Vehicle | — |
| A2 | Compound X | 1 μM |
| A3 | Compound X | 10 μM |
| ... | 96 wells mapped | |
Analysis that understands your plate
Platelet reads your plate map on upload and knows what every well means — no manual setup.
Measurements›Normalization›Spatial correction›Outlier detection›Visualization
Raw · edge effects
Corrected
IC₅₀ · publication-ready
Deep statistical analysis, zero code required
Robust normalization, spatial correction, outlier detection, and visualization.
Plate map
Statistical output
From plate map to p-values, in one view
Your layout carries straight through to every statistical result — no reshaping, no reannotation.
Reproducible by default
Every step is structured, deterministic, and traceable back to the raw data.
How it works
Design. Analyze. Visualize.
From wells to final figures in minutes
Design structured experiments, apply robust normalization and anomaly detection, and generate publication-ready insights — all within a single, reproducible workflow.
Supported assays
Built around the assays you already run
Point Platelet at your raw export and pick the assay type — the pipeline, statistics, and output are already configured.
qPCR — Relative quantification
Bio-Rad · Applied Biosystems · QuantStudioTurn a raw Ct export into fold-change, without touching a formula.
Input
Ct values per well, tagged with reference gene, target gene, and group.
Computes
ΔΔCt, fold-change, propagated error, group comparisons.
Output
Fold-change chart with statistics, ready for a figure.
Ready to accelerate your research?
Join researchers worldwide who are streamlining their workflows with Platelet. Start designing experiments, visualizing data, and generating insights today.