Fraposa-pgsc

What's Changed

* Remove FID replacement command, leave that to output by smlmbrt in https://github.com/PGScatalog/fraposa_pgsc/pull/20

**Full Changelog**: https://github.com/PGScatalog/fraposa_pgsc/compare/v1.0.1...v1.0.2

Changelog

* Fix FID/IID filtering exclusive numeric IDs in v1.0.1 by smlmbrt in https://github.com/PGScatalog/fraposa_pgsc/pull/18


**Full Changelog**: https://github.com/PGScatalog/fraposa_pgsc/compare/v1.0.0...v1.0.1

Changelog

* Use FID and IID when working with sample IDs by nebfield in https://github.com/PGScatalog/fraposa_pgsc/pull/17

**Full Changelog**: https://github.com/PGScatalog/fraposa_pgsc/compare/v0.1.1...v1.0.0

> [!WARNING]
> There's a breaking change to the CLI parameter `--stu_filt_iid`, which now expects a file in plink fam format to support FID & IIDs

Changelog

* Add homepage to pyproject.toml by smlmbrt in https://github.com/PGScatalog/fraposa_pgsc/pull/12
* Patch v0.1.1 by nebfield in https://github.com/PGScatalog/fraposa_pgsc/pull/14
* Fix broken scripts `fraposa_pred` and `fraposa_plot`
* Update vulnerable dependencies
* Drop shell scripts (superceded by pgscatalog-utils) from package
* Move to src layout
* Update README.md by nebfield in https://github.com/PGScatalog/fraposa_pgsc/pull/15

**Full Changelog**: https://github.com/PGScatalog/fraposa_pgsc/compare/v0.1.0...v0.1.1

The first release of our fork, which is integrated with [pgsc_calc](https://github.com/PGScatalog/pgsc_calc).

Our main aim for the fork is to improve functionality when processing data at scale, e.g. on 500,000 genomes at UK Biobank, and perform QC to make sure that the variants are identical (and oriented correctly) between the reference dataset and the study population you are projecting.

Improvements:

* Variant QC: added checks and minor fixes for variant matching, orientation, and sort order of ref/study variants to ensure results are consistent between the reference and study datasets
* refactor original scripts into python package
* added end to end test with pytest
* support batch-processing study samples without splitting the original dataset into multiple file (useful to parallelise large datasets)

Fixes:

* make output PCs have consistent precision
* deduplicate outputs when projecting study samples after the PCA space has been derived