Calculator
Use this model for sorted single-cell projects or sequencing-only runs.
Example Data Table
This sample table shows how different project designs can change cost per usable cell.
| Scenario | Samples | Loaded Cells per Sample | Viability (%) | Usable Cells | Total Cost | Cost per Usable Cell |
|---|---|---|---|---|---|---|
| Targeted immune sort | 2 | 120,000 | 85 | 27,311 | 8,745.55 | 0.3202 |
| Broad tumor panel | 4 | 180,000 | 80 | 55,080 | 14,632.80 | 0.2657 |
| Sequencing only | 3 | 100,000 | 88 | 52,272 | 9,949.50 | 0.1903 |
Formula Used
Loaded Cells Total = Samples × Cells Loaded per Sample
Viable Cells Total = Loaded Cells Total × Viability Rate
Target Cells Total = Viable Cells Total × Target Population
Sorted Cells Total = Target Cells Total × Sort Efficiency
Captured Cells Total = Sorted Cells Total × Capture Rate
Usable Sequenced Cells = Captured Cells Total × QC Pass Rate
Total Reads Required = Usable Sequenced Cells × Reads per Cell
Sequencing Cost = Total Reads Required ÷ 1,000,000 × Sequencing Cost per Million Reads
Total Project Cost = FACS Cost + Library Cost + Sequencing Cost + Consumables + Bioinformatics + Fixed Overhead
Cost per Usable Cell = Total Project Cost ÷ Usable Sequenced Cells
How to Use This Calculator
- Choose the workflow. Use the sorted option when FACS happens before sequencing.
- Enter sample count and loaded cells per sample.
- Fill in biological assumptions such as viability, target population, capture rate, and QC pass rate.
- Add read depth and all cost inputs. Include labor, library, sequencing, and overhead.
- Press calculate. The result appears above the form with a summary table.
- Review total cost, usable cells, and per-cell cost. Then export the output as CSV or PDF.
Why a Cost Per Cell Model Matters
Single-cell projects are powerful. They are also expensive. Many teams focus on headline sequencing price alone. That view is incomplete. Real budgets depend on sorting loss, viability, capture performance, and downstream QC. A cost per cell model gives a better planning baseline.
Main Cost Drivers in FACS and Single-Cell RNA Sequencing
FACS can improve biological specificity. It can also shrink the final usable population. Every gate, wash, transfer, and filter step affects yield. Library preparation is usually predictable per sample. Sequencing cost scales with read depth and final cell count. Bioinformatics, consumables, and fixed facility overhead often become meaningful once pilot work expands.
Why Yield Assumptions Change the Budget
A project may begin with strong cell numbers. Yet only a fraction becomes usable sequenced cells. Viability reduces the starting pool. Target population abundance limits the sortable subset. Sort efficiency can remove more cells. Capture rate and QC pass rate reduce the library again. Small losses compound. That is why per-cell cost can rise quickly even when headline line items look stable.
Planning Better Experiments
This calculator helps scientists compare designs before booking instruments. You can test whether higher read depth is worth the added spend. You can estimate the impact of poor viability. You can compare a sorted workflow against sequencing only. That makes it easier to align budget, throughput, and biological goals.
Using the Output
The most useful figures are total project cost, usable sequenced cells, and cost per usable cell. Those numbers support pilot planning, grant estimates, and core facility discussions. They also help teams explain tradeoffs clearly. If cost per cell rises too high, the model shows which assumption is driving the increase.
FAQs
1. What does cost per usable cell mean?
It is total project cost divided by the cells that survive capture and QC. This value is usually more useful than raw sequencing price because it reflects biological and technical loss.
2. When should I use the sequencing-only workflow?
Use it when you are not sorting cells before library generation. The calculator then removes sorting cost and treats target population and sort efficiency as fully retained.
3. Why do viability and QC pass rate matter so much?
They compound losses. A small decline at each step can sharply reduce final usable cells. That pushes up cost per cell even when fixed budget items stay unchanged.
4. What is a good reads-per-cell input?
It depends on assay design, tissue complexity, and analysis goals. Pilot data or core facility guidance usually provides the safest starting range for read depth assumptions.
5. Should I include analysis and labor costs?
Yes. Ignoring them can understate the real project budget. Core fees, technician time, data processing, and cleanup materials often matter in multi-sample studies.
6. Can this calculator handle pilot studies?
Yes. It is useful for pilot work because you can stress-test assumptions. Change cell input, capture rate, or read depth to see how fragile the budget is.
7. Why is target population percentage important in sorted workflows?
If the target population is rare, many starting cells never reach the library. Rare populations often create higher per-cell cost because sort yield becomes the main bottleneck.
8. Is this calculator suitable for grant planning?
Yes. It helps build transparent budget assumptions. You can show how sample number, yield loss, read depth, and overhead contribute to the requested funding.