We anticipate that joint dimensionality reduction and projection will become a foundational norm for comparative and integrative analysis of long-range interaction profiles in Hi-C/3C+ data. e.g. existing methods for working with classic A/B vectors can be extended to joint higher-order embeddings.

We jointly-hic to create an atlas of 89 human Hi-C samples, uncovering distinct patterns of nuclear architecture associated with heterochromatin composition and demonstrating how higher-order principal components capture missing information about gene expression and regulatory element activity.

jointly-hic accomplishes this using mini-batch incremental PCA, allowing for joint decomposition of arbitrarily many contact matrices at any resolution with constant memory.

Joint decomposition allows for robust and directly comparable low dimensional representations of arbitrarily many contact maps, providing insights into genome organization across diverse biological contexts, from different tissues to developmental stages.

The classic A/B compartment track comes from matrix factorization of a contact matrix into eigenvectors or PCs. Done separately, each map is projected onto a different coordinate system. Comparing such vectors directly is problematic, especially if seeking info from **higher-order** components.

We introduce a framework and Python toolkit (github.com/abdenlab/joi...) for analyzing compartmentalization and long-range interactions in chromosome conformation capture data.

Yes, and more recently Zarr too academic.oup.com/gigascience/...

While oxbow makes legacy data more accessible, it is a good conduit to more general-purpose persistent storage.

(4) bpnet-lite: Load official Chrom/BPNet models into PyTorch for downstream tangermeme integration. Improved command-line tools + docs. Still concerns about perf of models trained from scratch -- will be resolved next version!

github.com/jmschrei/bpn...

bsky.app/profile/jmsc...

We’re excited and eager for feedback, so please give oxbow a try!

`pip install oxbow`

I’m also excited to be presenting Oxbow as part of my talk on composability at the #SciPy2025 Conference on Wednesday! Hope to see some of you there.

cfp.scipy.org/scipy2025/ta...

It also supports:

* Column projection and pushdown (parsing only the fields you need)
* Complex and nested field types (e.g. alignment tags, variant genotype call data, etc.)
* Genomic range-based queries via an index
* User-defined transports and file systems

This update (v0.4.x) provides complete #ApacheArrow data models for 11 file formats and counting, including the GA4GH/htslib formats and UCSC’s BigWig/BigBed.

We revamped the #rustlang backend and implemented a new "DataSource" API in #Python, which allows for streaming conventional #genomic files – in-memory, on-disk, or in the cloud – into the modern data tools you use regularly, including #Pandas, #Polars, #DuckDB, and #Dask.

We’re excited and eager for feedback, so please give oxbow a try!

`pip install oxbow`

I’m also excited to be presenting Oxbow as part of my talk on composability at the #SciPy2025 Conference on Wednesday! Hope to see some of you there.

cfp.scipy.org/scipy2025/ta...

It also supports:

* Column projection and pushdown (parsing only the fields you need)
* Complex and nested field types (e.g. alignment tags, variant genotype call data, etc.)
* Genomic range-based queries via an index
* User-defined transports and file systems

This update (v0.4.x) provides complete #ApacheArrow data models for 11 file formats and counting, including the GA4GH/htslib formats and UCSC’s BigWig/BigBed.

We revamped the #rustlang backend and implemented a new "DataSource" API in #Python, which allows for streaming conventional #genomic files – in-memory, on-disk, or in the cloud – into the modern data tools you use regularly, including #Pandas, #Polars, #DuckDB, and #Dask.