Add SenKin CITE-seq method (LightGBM + GRU ensemble) - #18
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rcannood merged 10 commits intoJul 30, 2026
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VladimirShitov
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Thanks! Looks very good overall, but some functionality should be moved to https://github.com/lueckenlab/senkin-tmp-cite-pred and imported from there for better reusability
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Could you update this one with the updated code from senkin-tmp repo and then merge (or tell me to merge)? |
Replace the inline _lognorm and _clr_tsvd_fitted duplicates in senkin_train with the now-published library functions log_normalize() and clr_tsvd() from senkin_tmp_cite_pred.preprocess. Behaviour is unchanged: log_normalize uses target_sum=1e4 (CP10K + log1p) matching the old _lognorm, and clr_tsvd uses random_state=42 for reproducible components. The previously-kept fitted TSVD object was unused and is dropped.
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Replaced the inline _lognorm and _clr_tsvd_fitted duplicates in senkin_train Ready to merge @VladimirShitov |
log_normalize() preserves the input sparsity, so on the sparse counts layer it returns a sparse matrix. np.asarray() on a sparse matrix yields a 0-d object array, which crashed at X_lognorm_all[train_mask] with an IndexError. Use the existing _to_dense() helper (and restore float64) so the downstream boolean-mask slicing and concatenation work, matching the previous inline _lognorm behaviour. Verified: viash test src/methods/senkin/senkin_train passes (1/1). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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* Add methods/senkin to run_benchmark's dependencies -- it was in the methods list but not declared, so the generated workflow referenced an undefined variable and the whole run died at startup * Report "senkin" as the method id rather than senkin_predict; the metrics copy uns["method_id"] straight into the score * Drop the gpu label: measured on a T4, senkin runs entirely on CPU (LightGBM's pip wheel is CPU-only and dominates the runtime, and TensorFlow cannot load CUDA in this image) * Give senkin_predict a test_setup and generate its model in test_resources.sh, so viash test has an --input_model to use
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* Rename the method and both sub-components to `senkin_tmp`, so the name credits both team members * Point the doi at the 2026 competition paper (10.64898/2026.02.24.707614) Co-authored-by: Vladimir Shitov <35199218+VladimirShitov@users.noreply.github.com>
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@benjaminfreyuu @VladimirShitov Can we expect this method to be runnable on pairs other than GEX -> PROT, or will it always fail? |
* Cap the boosting rounds, early stopping patience and nn epochs with `info.test_default`, the way openproblems-bio#57 did for the other two train components * Fold the senkin_tmp fixture into the skip-if-up-to-date structure that came in with openproblems-bio#57
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