Data-rich.
Decision-poor.
I help research labs turn complex omics data into reproducible findings and a clear next step.
More signals. Fewer answers.
Integrate cell states, spatial context, biomarkers, and molecular layers without hiding uncertainty.
Find what is worth validating.
Move from long candidate lists to ranked mechanisms, defensible biomarkers, and focused experiments.
Turn complex data into a clear research decision.
Current question → data and feasibility review → fixed-scope proposal → reproducible analysis and decision-ready deliverables.
Grant-ready preliminary evidence
Use public data to test a new research direction and build credible preliminary evidence before committing to a larger study or grant proposal.
- Identify suitable public cohorts and datasets
- Test the biological signal behind your grant question
- Generate reproducible figures and proposal-ready evidence
- Document limitations, citations, and analysis boundaries
- Define the next experiment or analysis milestone
Bulk transcriptomics & pathway analysis
Turn case-control, perturbation, or treatment-response data into a reproducible set of biological findings.
- Sample and metadata quality assessment
- Replicate-aware differential expression analysis
- Pathway and gene-set interpretation
- Publication-ready figures and methods
Single-cell & spatial omics
Resolve cell states, rare populations, tissue microenvironments, and treatment responses in complex biological data.
- Cell-state and rare-population characterization
- Spatial neighborhoods and cell–cell interaction analysis
- Cross-sample integration with explicit QC and limitations
- Decision-ready figures and reproducible analysis outputs
Biomarker validation
Test whether a molecular signature remains interpretable across cohorts, platforms, or time points.
- Independent-cohort and cross-platform validation
- Signature reproducibility and transferability assessment
- Confounder, subgroup, and longitudinal sensitivity checks
- Validation report with clear go / refine / stop decision
Multi-omics mechanism
Connect molecular layers and clinical phenotypes to prioritize mechanisms and the next experiment.
- Transcriptomics, epigenomics, proteomics, and phenotype integration
- Cross-layer agreement and mechanism prioritization
- Candidate pathways, regulators, and experimental hypotheses
- Reproducible figures, intermediate matrices, and methods
Predictive Cell Fate Modelling
Predict which cells commit to disease vs. recovery, and identify the interventions that reverse fate.
- Developmental trajectory and RNA velocity vector field analysis
- Transition boundary distance estimation (pre-symptomatic biomarker discovery)
- In silico perturbation screening and combination intervention ranking
- Applied to AKI→fibrosis, cancer drug resistance, and reprogramming
Integrated mechanism & experimental prioritization
Resolve disagreement across molecular layers and produce a ranked, defensible plan for the next experiment.
- Integrated transcriptomic, epigenomic, proteomic, and metabolomic evidence
- Clinical or phenotypic context and covariate review
- Ranked mechanisms, targets, and candidate experiments
- Evidence matrix, uncertainty, and stopping criteria
Delivered project modalities.
Standardized workflows, reproducible pipelines, and figure-ready deliverables across spatial, single-cell, multi-omics, and ML architectures.
Representative Data & Analysis Scopes
Multi-platform spatial deconvolution, Squidpy neighborhood graphs, ligand-receptor interaction modeling, and perivascular barrier mapping in complex tissues.
Spatiotemporal cellular kinetics, TCR clonotype expansion tracking, exhaustion trajectory characterization, and immunotherapy resistance mechanisms.
Joint chromatin accessibility embedding, transcription factor motif enrichment, peak-to-gene cis-linkage mapping, and prioritized candidate enhancer targets.
HPC-scale data harmonization, batch-corrected deep latent integration across disease states, rare subpopulation classification, and deployable prediction scripts.
scVI deep generative batch correction, pseudotime trajectory with driver gene velocity, and sex-stratified pseudobulk differential response modeling.
Salmon quantification, DESeq2 modeling, secretomic and metabolic pathway functional enrichment, and publication-ready manuscript figure packages.
What decision does your data need to support?
Select the closest project type to see the likely deliverable and timeline. Public-data analysis can also support proposal-ready preliminary evidence. Final pricing follows a short scope review.
Dr. Qingzhou Zhang
Scientist-to-scientist computational partnership.
My work is grounded in more than a decade of computational genomics research, including publications in Molecular Cell, Nature Biotechnology, and Nature Communications.
I work across bulk and single-cell transcriptomics, multi-omics, spatial data, regulatory genomics, and machine-learning workflows. I help research labs turn complex datasets into reproducible analyses, defensible biological conclusions, and clear next-step decisions.
I take on a limited number of service contracts each quarter. Every engagement operates under a fixed-price, milestone-governed scope — with reproducible Conda/Docker environments, clean intermediate matrices, publication-ready figures, and an explicit account of what the data can and cannot support.
Start with one current research question.
Send a brief paragraph about your current dataset—or the grant question you want to test with public data—along with the decision you need to make. I will first assess whether the project is a fit, then define a fixed-scope proposal before any commitment. I can discuss timing, NDA, data access, and procurement requirements during the call.
Schedule a 30-Min Scoping Call →