An opinionated structuring framework designed to bridge the gap between bioinformatics outputs and high-performance analytical queries.
The K Data Model optimizes information retrieval across multiple biological scales by transforming raw files (e.g. h5ad, csv, zarr) into a unified, SQL-queryable parquet structure. It enables seamless cross-modality analysis—from clinical longitudinal trends down to single-cell gene expression and spatial spots—ensuring every data point is mapped to a common coordinate system and ontology.
These steps transform heterogeneous data into a unified, high-performance schema.
Multi-Level structuring
Data is organized into Patient, Sample, Slide, and Cell/Spot levels.
Optimizes query performance for different scales of analysis.
Schema harmonization
Conversion of raw formats to Parquet files.
Enables universal SQL querying and high-speed data loading.
Ontology mapping
Gene names and disease labels are matched to a single reference.
Ensures consistency and enables cross-dataset comparisons.
Normalization
Counts for bulk, single-cell, and spatial data are normalized.
Removes technical variation to allow biological interpretation.
Feature enrichment
Computation of UMAPs (bulk & single-cell) and pseudobulk.
Provides instant, high-quality visualizations without on-the-fly lag.
The K Data Model applies specific rules to ensure data integrity and visualization speed.
Relational mapping
Uses common keys to link patients, samples, and cells.
Prevents "orphaned" data; ensures clinical context is always available.
Pre-computed metadata
Ranges, completeness, and cohort counts are calculated upfront.
Accelerates UI responsiveness for filtering and exploration.
Visual optimization
Data is partitioned/saved specifically for heatmaps and dotplots.
Delivers near-instant rendering of complex large-scale matrices.
Longitudinal alignment
Chronological ordering of treatments and events.
Enables accurate survival and treatment-response analysis.
AnnData / Scanpy
Handling single-cell and spatial transcriptomics objects.
NumPy / Pandas
Core matrix manipulation and numerical processing.
Parquet
Columnar storage format for efficient, large-scale data access.
AWS SDK (or other vendor)
Cloud-native data management and storage interface.
SQL
Universal query language for all standardized data levels.
What this means for your analyses:
Granular access: Query clinical data (e.g., all patients on immunotherapy) and drill down to single-cell gene expression or immune cell density.
Ready-to-view: Pre-computed visualizations like UMAPs and heatmaps reduce wait times.
Ontological truth: Synonymous gene names or mismatched disease labels are aligned automatically.
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