About Owkin-managed data

An opinionated structuring framework designed to bridge the gap between bioinformatics outputs and high-performance analytical queries.

Overview

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.

Pipeline overview

Processing & standardization

These steps transform heterogeneous data into a unified, high-performance schema.

Step	What happens	Why
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.

Data filtering & Quality control

The K Data Model applies specific rules to ensure data integrity and visualization speed.

Filter / QC Step	Criteria	Impact
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.

Technologies used

Technology	Purpose
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.