Host-Response Classifier Development Services
A modular discovery-to-panel program helps teams choose the level of evidence required for their research question, rather than treating every differentially expressed gene as a validated diagnostic marker.
A
Cohort Definition & RNA-seq Discovery
We help define infection status, the research phenotype, comparator groups, and sampling windows before expression analysis. Study planning can account for biological replicates, tissue or blood compartments, available metadata, treatment exposure, and pathogen-testing results.
RNA-seq analysis can encompass sample-level quality review, expression quantification, differential expression, exploratory visualization, and appropriately controlled comparisons. This foundation helps prioritize markers that relate to the intended phenotype rather than obvious technical or cohort differences.
Key Research Outputs
Cohort assessment, RNA-seq QC summary, differential-expression results, and preliminary host-response gene candidates.
B
Biomarker Ranking & Pathway Interpretation
Candidate genes are reviewed for expression effect size, biological plausibility, consistency between comparisons, technical detectability, and assay transfer feasibility. Pathway enrichment and immune-response module analysis can organize candidate signals into interpretable host processes.
Depending on sample counts and study goals, we evaluate sparse feature-selection approaches, expression correlation, and marker redundancy while tracking candidate provenance. This avoids proposing an unnecessarily large panel with overlapping information and limited generalizability.
Key Research Outputs
Prioritized biomarker shortlist, functional enrichment summary, marker-selection rationale, and candidate robustness assessment.
C
Transcriptomic Classifier Construction
Classifiers may be framed around case versus comparator, disease-stage categories, or prespecified response phenotypes when sufficient labeled data exist. We compare models with respect to sample size and class imbalance, defining scores, prespecified metrics, and uncertainty.
Model selection is separated from final evaluation. Cross-validation, subject-level splitting, locked validation rules, and sensitivity analyses are selected to reduce leakage and optimism. Where data permit, assessment extends beyond AUROC to precision-recall performance and probability calibration; results remain research-stage estimates.
Key Research Outputs
Model specification, selected feature set, classification score definition, performance estimates, and evaluation limitations.
D
Targeted Panel Transfer & Confirmation
Shortlisted transcripts can be assessed for targeted qPCR feasibility with reference-gene selection, primer/probe design planning, dynamic range, and RNA quality considerations. Where suitable, we map how a reduced gene set could be measured with a repeatable panel.
Panel design and analytical testing are separate scope items; independent-cohort validation is planned with explicit acceptance criteria and sample requirements. A research-use interpretation guide describes score calculation, known limitations, and circumstances that warrant additional verification.
Key Research Outputs
Proposed qPCR target panel, normalization considerations, analytical feasibility criteria, and independent validation roadmap.
