Molecular & integrative analysis
Connect complementary experimental measurements around a clearly defined biological question.
Analyses are tailored to your experimental design, data quality and research question. The workflow and deliverables are agreed following project review.
Analysis modules
Sample alignment, missingness and cross-assay QC.
Joint exploratory structure and latent-factor analysis.
Associations between receptor expansion and cell states or gene programs.
Genomics, expression and candidate neoantigen evidence integration.
Protein–RNA or chromatin–RNA concordance.
Cross-cohort robustness assessment when compatible validation data exist.
Integrated findings with links to supporting analyses and limitations.
Data and metadata
At least two compatible molecular datasets, stable sample/donor identifiers and a documented matching strategy; research covariates and batch information.
Proposed deliverables
Integrated sample maps; cross-modal associations; evidence-linked research findings; transparent preprocessing and model choices.
Interpretation and feasibility
Integration requires a valid sample-matching design. Association is not causation; supervised models require leakage control and independent or appropriately nested validation.
Methods and references
Methods, reference resources and software are selected for each project after protocol, feasibility and licence review. Applicable versions, references and interpretation limits are documented in the agreed workflow.