I predict what your cells will decide.
From raw multi-modal omics to causal cell-fate decisions. Quantitative analysis services for academic labs and biopharma R&D — single-cell, ML classifiers, and regulatory networks.
Map cellular niches & 3D chromatin wiring.
Subcellular spatial deconvolution (Visium HD / Xenium), ligand-receptor microenvironment signaling, and Hi-C chromatin loop reconstruction in intact disease tissue.
Deploy validated novel ML models.
Train classifiers for rare cell states across 100+ public datasets, prioritize drug target vulnerabilities, and model in silico perturbation responses.
Structured packages designed for publication impact.
Scoping call → Written fixed-price proposal → Milestone execution → Figure-ready deliverables with reproducible pipelines.
Multi-omics Integration
Cross-layer regulatory synthesis that reveals what each single modality hides in isolation.
- Bulk RNA-seq, scRNA-seq, scATAC-seq, and Spatial Transcriptomics
- Large-scale integration across 100+ datasets (scVI, Harmony on Alliance HPC)
- Systematic batch correction, doublets filter, and QC validation
- Manuscript-ready publication figures with written statistical methods
ML Classifier Development
Trained and validated machine learning models deployable directly on your incoming datasets.
- Automated rare cell-type identification across uncurated cohorts
- Out-of-sample validated classification with precision/recall curve reports
- Disease subgroup biomarker extraction (healthy vs. disease states)
- Packaged deployment scripts for future lab experiments
Regulatory Network Reconstruction
From multi-modal data, identify the causal transcription factors and enhancers governing phenotype.
- scMultiome (paired RNA + ATAC) joint manifold and accessibility mapping
- Peak-to-gene linkage with TF binding motif enrichment
- Prioritized candidate enhancer lists for in vivo validation experiments
- Cross-species conservation analysis (e.g., Zebrafish ↔ Mouse ↔ Human)
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
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 is your current data state?
Select your experimental setup to view recommended deliverables, pricing frame, and timeline.
Dr. Qingzhou Zhang
Scientist-to-scientist computational partnership.
I am a computational systems biologist working on predictive models of cell fate decision-making. My research addresses the fundamental question that standard pipelines overlook: what will this injured or perturbed cell decide — repair or fibrosis, response or resistance, differentiation or malignancy?
With 15+ years of computational genomics research and senior author publications in Molecular Cell, Nature Biotechnology, and Nature Communications, I bridge the gap between high-dimensional molecular assays and actionable biological hypotheses.
I take on a limited number of service contracts each quarter. Every engagement operates under a strict fixed-price, milestone-governed scope — with 100% reproducible Conda/Docker scripts, clean intermediate matrices, and publication-ready figures.
Start with a 30-minute scoping call.
Send a brief paragraph about your dataset and biological question. I will provide an honest feasibility assessment and a fixed-price proposal within 48 hours — before any commitment.
Schedule a 30-Min Scoping Call →