Introduction
TileDB offers a specialized product, called TileDB-SOMA, specifically designed for managing and analyzing single-cell genomics data at scale. The TileDB-SOMA project is a collaboration between the Chan Zuckerberg Initiative (CZI) and TileDB, Inc.
A unified solution for omics data
At its core, TileDB is a database solution architected around multi-dimensional arrays. This powerful and flexible data structure allows us to easily build any special-purpose database system to capture demanding use cases with complex data. Single-cell sequencing is one of these use cases.
TileDB-SOMA is an open-source library introducing a data format, query engine and API for storing, managing and analyzing collections of annotated matrices and derived results that are typical of systems biology data. While single-cell genomics data is the most common use case, TileDB-SOMA is equally useful for other types of omics data as well, such as bulk RNA-seq.
The open-source library can be coupled with the TileDB Cloud commercial product to offer a powerful solution for storing, organizing, sharing, and analyzing omics data at atlas-scale.
SOMA and TileDB-SOMA
Systems biology lacks any standard for storing omics data. Instead, many toolkits use their own format, making it difficult to share and aggregate data. Furthermore, these toolkit-specific formats typically require loading the entire dataset into memory, which is increasingly infeasible as datasets grow in size. Finally, these formats are not optimized for cloud object stores, which have become the preferred and most economical storage option for large-scale data.
TileDB has partnered with the Chan Zuckerberg Initiative (CZI) to develop a scalable, efficient, and user-friendly storage solution for single-cell genomics data. The collaboration aims to address the challenges posed by the rapidly growing volume and complexity of single-cell data, enabling researchers to focus more on science and less on data management. The outcome of this collaboration is two projects:
- The SOMA (Stack Of Matrices, Annotated) project, which is an open data model and API specification for single-cell data.
- The TileDB-SOMA project, which is SOMA’s implementation with TileDB as the backend storage and processing engine. TileDB-SOMA is also open-source (under the MIT License), and take advantage of TileDB’s powerful multi-dimensional array engine.
To address the existing issues and challenges when working with single-cell genomics data, TileDB-SOMA is built to be:
- Interoperable: TileDB-SOMA offers efficient implementations in both Python and R, and tightly integrates with Seurat, Bioconductor and scanpy.
- Optimized for object stores: TileDB-SOMA inherits the cloud-native array format, particularly optimized for object stores (such as the popular Amazon S3, Google Cloud Storage, Azure Blob Storage and MinIO).
- Highly scalable: TileDB-SOMA is proven to handle tens of millions of cells, and has the ability to scale to billions of cells when coupled with TileDB Cloud’s distributed computing engine.
- Multi-modal: TileDB-SOMA offers out-of-the-box support for storing multiple measurements per experiment.
Section organization
This rest of the Single-Cell section is organized as follows:
- Quickstart: This is the best way to get started with TileDB-SOMA. You will learn how to install TileDB and TileDB-SOMA in your preferred language and run basic examples.
- Foundation: This contains all the background information and internal mechanics of TileDB-SOMA. Learning these will provide a very deep understanding of the TileDB and TileDB-SOMA technology and power, and help maximize the value users get from TileDB.
- Tutorials: This is a series of tutorials covering all aspects of TileDB-SOMA, from basic ingestion to advanced topics. Running those tutorials can help users start without any prior knowledge of TileDB and TileDB-SOMA and become power users.
- API Reference: This lists all the TileDB-SOMA functionality across the numerous programming languages it supports, and enables fast lookups on API usage.