Life Sciences Applications
Life sciences research presents unique statistical challenges: pseudoreplication in animal studies, compositional data in flow cytometry, batch effects in genomics, and the CONSORT/ARRIVE compliance requirements of clinical and preclinical studies. These use cases show how Licklider handles the specific data structures and disclosure requirements of biological research.
Section covers: Flow cytometry (compositional data, donor-level replication) ·
Gene expression differential analysis (BH-FDR, volcano plot, batch detection) ·
Animal study compliance (ARRIVE 2.0, pseudoreplication guard, power justification) ·
Clinical trial endpoints (CONSORT, primary vs. secondary, multiplicity)
When to Use This Section
Use Life Sciences Application workflows when:
- You are working with flow cytometry data (proportional / compositional outcomes with donor-level nesting)
- You have RNA-seq or other omics data requiring multiple-testing correction and batch effect disclosure
- Your experiment uses animal subjects and you need to comply with ARRIVE 2.0 reporting standards
- You are preparing a clinical trial analysis with primary and secondary endpoints requiring multiplicity control
Available Scenarios
Flow Cytometry
Cell population comparison across donors with pseudoreplication guard and compositional data handling. Arcsine transformation disclosure.
Gene Expression / Omics
Differential expression with volcano plot, BH-FDR correction, and batch effect detection. Log2 fold change + adjusted p-value reporting.
Animal Study Workflow
Complete ARRIVE 2.0-compliant workflow: pre-study power calculation, pseudoreplication guard, exclusion log, and reproducible Methods text.
Clinical Trial Endpoints
Primary and secondary endpoint analysis with CONSORT-compliant reporting, multiplicity-adjusted secondary analyses, and ITT population disclosure.
Key Guards for Life Sciences
- Pseudoreplication Detection (4.5.1): Detects when measurements from the same animal/donor/plate are treated as independent observations
- Biological vs. Technical Replicate Guard (4.5.2): Enforces averaging of technical replicates before biological-level statistical tests
- Batch / Plate Confounding (4.5.3): Flags when all samples from one group are processed on a single plate or batch — a major source of confounding in genomics and flow cytometry
- Compositional Data Warning (4.7.2): Warns when proportion data (0–100%) are analyzed with methods that assume unconstrained continuous data
- Multiple Comparison Compliance (4.1.5): Enforces BH-FDR or Bonferroni correction for any analysis testing thousands of hypotheses simultaneously (e.g., omics)