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d-laub
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Jan 20, 2025
| - v0.3.0: Improved out of core functionality, robust BED classification datasets | ||
| - v0.0.4 — Interoperability with AnnData and SnapATAC2 | ||
| - v0.X.0: Improved out of core functionality, robust BED classification datasets | ||
| - v0.X.4 — Interoperability with AnnData and SnapATAC2 |
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| ### Loading data from BigWig files | ||
| [BigWig files](https://genome.ucsc.edu/goldenpath/help/bigWig.html) are a common way to store track-based data and the workhorse of modern genomic sequence based ML. ... | ||
| Because BAM files contain read alignments, we can use different strategies for quantifying the pileup at each position. See the TODO for a deeper dive into...TODO |
| ### Working with Zarr stores and XArray objects | ||
| The SeqData API is built to convert data from common formats to Zarr stores on disk. The Zarr store... When coupled with XArray and Dask, we also have the ability to lazy load data and work with data that is too large to fit in memory. | ||
| ### Building a dataloader | ||
| One of the main goals of SeqData is to allow a seamless flow from files on disk to machine learning ready datasets. This can be achieved after loading data from the above functions by building a PyTorch dataloader with the `get_torch_dataloader` function: |
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after loading data from constructing Xarray datasets with the above functions
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Looks pretty good! Some typos e.g. "Adatped from..."
Converting Xarray to other formats looks unfinished?
Consider reworking Zarr stores section? Intent with SeqData is to never use the default Dataset.to_zarr() method because it concatenates the length axis of string/char arrays by default, and maybe some other issues I don't remember off the top of my head.
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Updated first tutorial for introducing Zarr and Xarray.