Deciphering cellular response, spatial architecture, and perturbation mechanics through reproducible computational systems biology.
Omics with Johnson is my independent computational biology practice, uniting rigorous research, reproducible software pipelines, and specialized scientific consulting for research labs.
For the past decade, molecular biology has prioritized cataloging: generating static atlases and differential expression tables. But knowing what differs between conditions does not explain how a cell arrived there or how it will respond to intervention.
I focus on the transition from descriptive genomics to predictive systems biology: mapping continuous cellular response fields, stress-testing foundation models against empirical baselines, and developing deterministic, containerized pipelines.
My research investigates how molecular state and spatial context govern cell fate decisions and therapeutic response.
Cellular response fields, counterfactual prediction, and state transitions
Moving computational biology beyond descriptive differential expression toward mechanistic models of how cells shift between attractor states under genetic, chemical, and physical perturbations.
Atlas-scale analysis, cell-state discovery, spatial organization, and multimodal integration
Developing computational foundations for spatial microenvironments, multimodal co-profiling (RNA + ATAC + protein), and high-resolution cell-state maps in complex tissues across five major spatial platforms.
Representation learning, biological generalization, and rigorous model benchmarking
Evaluating what single-cell and biological foundation models can and cannot do. Investigating whether self-supervised pre-training yields genuine biological generalizability or memorizes batch statistics.
Kidney pathology, cardiovascular remodeling, oncology, and tissue repair
Applying systems-level omics and predictive frameworks to translational human pathology: dissecting maladaptive repair in kidney disease, smooth muscle cell plasticity in atherosclerosis, and myocardial repair.
In-depth spatiotemporal, single-cell, and multi-omics analyses re-analyzing published clinical trials and benchmark datasets.
Systematic re-analysis of 128 publicly available spatial transcriptomics samples across five major platforms: GeoMx, Visium, Xenium, NanoString, and MERFISH. Mapping spatial cellular niches, microenvironmental boundaries, and cross-platform concordance.
Open Interactive ReportDeep spatiotemporal multi-omics re-analysis of the GCAR1 trial integrating longitudinal scRNA-seq, single-cell TCR V(D)J repertoires, and sub-micron Visium HD spatial transcriptomics. Mapped CAR-T exhaustion trajectories, perivascular resistance barriers, and epitope spreading.
Open Interactive ReportCharacterized cellular heterogeneity and lineage transitions among clonally expanding vascular smooth muscle cells (VSMCs) in atherosclerotic lesions of ApoE-deficient mice using SMART-seq2 full-length single-cell sequencing.
Open Interactive ReportUnpacking computational biology failure modes, single-cell foundation model stress tests, and statistical fallacies.
A systems biology perspective on the Arc Institute State paper in Cell, examining whether large scale perturbation models capture authentic cell fate bifurcations.
Read Scientific AnalysisCommentary on context dependent therapeutic mechanisms and testing intervention predictions beyond training contexts.
Read Scientific AnalysisBioinformatics software, Bioconductor packages, PyPI libraries, and reproducible pipelines authored and maintained by Dr. Qingzhou Zhang.
High-resolution spatial transcriptomics atlas of the kidney reconciling Visium HD, Xenium, MERFISH, GeoMx, and Nanostring data for detailed spatial structure and cellular organization.
View RepositoryUnified reproducible pipeline for chromatin accessibility and occupancy assays: ATAC-seq, CUT&RUN, and ChIP-seq. Container-ready with single-command execution.
View RepositoryMulti-interface Python package for deep characterization and statistical profiling of gene expression patterns, marker distribution, and co-expression in single-cell data.
View RepositoryComprehensive R toolset for functional analysis, topological exploration, and publication-ready visualization of protein complex and interactome data.
View RepositoryFixed-scope computational biology consulting, single-cell/spatial analysis, and bespoke institutional workshops.
Use public data to test hypotheses and build credible preliminary evidence before sequencing
Identify suitable public cohorts and datasets (GEO, SRA, UK Biobank, TCGA, HCA) to test the biological signal behind your grant question and generate proposal-ready figures.
Resolve cell states, tissue microenvironments, and treatment responses across platforms
Turn complex single-cell and spatial runs (10x Chromium, Visium HD, Xenium, MERFISH, GeoMx) into interpretable cellular neighborhoods, receptor-ligand communication networks, and disease states.
Test signature transferability and connect multi-omics layers to clinical outcomes
Test whether a molecular signature remains robust and predictive across external cohorts, different sequencing platforms, and longitudinal time points. Connect RNA, ATAC, and proteomics layers.
Private workshops, institutional training, and advanced bioinformatics programs
Intensive, bespoke computational biology training engineered for wet-lab biologists transitioning to dry-lab, core facility bioinformaticians, and dry-lab scientists advancing to predictive modeling.
Omics with Johnson was created by Dr. Qingzhou Zhang, a computational systems biologist working at the intersection of genomics, single-cell analysis, and predictive modeling.
“Modern omics produces enormous amounts of descriptive data. The next challenge is not simply identifying what differs between conditions, but understanding biological states, predicting how systems change, and determining which interventions can alter those trajectories.”
I clearly distinguish between my scientific research as an investigator (Dr. Qingzhou Zhang), my consulting business & practice (Omics with Johnson), my peer-reviewed publications, and my open-source codebase on GitHub.