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Replicate and Batch-Confounding Analyzer
x402
Replicate and Batch-Confounding Analyzer is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.149001/call, status unknown (last checked 2026-10-21).
Inspects structured sample assignments to detect technical/biological replication issues and batch confounding before comparing experimental groups.
Price / call$0.149001
Success—
StatusUnknown
How this service works
Inspect structured sample assignments for technical/biological replication and batch confounding before comparing experimental groups. No causal conclusion from labels alone. New modular adapter: retained result recovery for 24 hours, minimal receipt metadata for 90 days. Existing live route unchanged.
Output
Returns a structured assessment indicating whether the supplied sample assignments exhibit adequate technical and biological replication, flags potential batch confounding between experimental groups, and provides warnings about any confounders detected. No causal conclusions are drawn from labels alone. Results are retained for 24 hours; minimal receipt metadata is retained for 90 days.
Example use cases
Pre-analysis experimental design audit
“I have 48 sample assignments from a synthetic dataset — can you inspect them for batch confounding and make sure I have proper biological replication before I run my group comparisons?”
Genomics batch effect check
“Before I do differential expression analysis, can you look at these 64 public sample assignments and flag any technical replication problems or batch confounding between my experimental groups?”
Clinical study sample layout validation
“I'm about to compare two treatment arms and I want to make sure my 32 synthetic sample assignments aren't confounded by batch — can you check the replication structure and warn me of any issues?”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/compute/biology/replicate-batch-confounding?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a pre-analysis sanity check on experimental sample layouts for biological or clinical studies, specifically to detect batch confounding and verify replication structure before downstream statistical comparisons. It is distinct from general statistical testing endpoints because it focuses on study-design validity rather than outcome computation.
Side effects
- Fewer than 1 or more than 128 observations causes a validation error
- Invalid dataClass value (not 'public' or 'synthetic') returns a schema rejection
- Malformed observation objects cause a parsing error
- Insufficient sample size to assess replication returns an underpowered warning
- Service unavailable returns a 5xx error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| dataClass | string | |
| observations | array |
Tags
x402
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