| --- |
| pretty_name: Malinois/Gosai MPRA Regression |
| task_categories: |
| - tabular-regression |
| tags: |
| - biology |
| - genomics |
| - dna |
| - mpra |
| - carbon |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # Malinois/Gosai MPRA Regression |
|
|
| This dataset preprocesses the Gosai et al. 2024 supplementary MPRA table used by the |
| Malinois benchmark for supervised DNA-to-activity regression. Each row contains a DNA |
| sequence and three cell-type-specific activity targets: `K562_log2FC`, |
| `HepG2_log2FC`, and `SKNSH_log2FC`. |
|
|
| No new license is asserted by this preprocessing. Users should follow the terms of |
| the source publication and supplementary data. |
|
|
| ## Source |
|
|
| - Publication: Gosai et al., *Machine-guided design of cell-type-targeting |
| cis-regulatory elements*, Nature 2024. |
| - Source table: `41586_2024_8070_MOESM4_ESM.txt`. |
| - Source URL: `https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-024-08070-z/MediaObjects/41586_2024_8070_MOESM4_ESM.txt`. |
|
|
| ## Splits |
|
|
| Chromosome splits match the Carbon fine-tuning experiments and the public Malinois |
| setup we used: |
|
|
| | Split | Chromosomes | Rows | Rows with all SE <= 1.0 | |
| |---|---:|---:|---:| |
| | train | all except validation/test chromosomes | 668,946 | 627,661 | |
| | validation | 19, 21, X | 62,406 | 58,811 | |
| | test | 7, 13 | 66,712 | 62,582 | |
|
|
| Total rows after filtering finite targets/standard errors and nonempty sequences: |
| 798,064. |
|
|
| ## Columns |
|
|
| - `id`: original row identifier from the source table. |
| - `split`: train, validation, or test. |
| - `chromosome`: normalized chromosome label. |
| - `data_project`, `oligo`, `variant_class`: source metadata. |
| - `sequence`: uppercase DNA sequence. |
| - `reverse_complement`: reverse complement of `sequence`. |
| - `forward_rc_concat`: `<dna>sequence</dna><dna>reverse_complement</dna>`, |
| matching the best Carbon fine-tuning recipe. |
| - `K562_log2FC`, `HepG2_log2FC`, `SKNSH_log2FC`: raw regression targets. |
| - `K562_lfcSE`, `HepG2_lfcSE`, `SKNSH_lfcSE`: target standard errors. |
| - `*_train_zscore`: target standardized using train-split mean/std. |
| - `all_se_le_1`: true when all three SE columns are `<= 1.0`; this was the |
| main reported validation/test metric filter. |
| - `any_log2fc_gt_0_5`: true when any target is greater than `0.5`; this was used |
| for optional high-activity training upsampling. |
|
|
| Train z-score statistics: |
|
|
| | Target | Mean | Std | |
| |---|---:|---:| |
| | K562_log2FC | 0.49943020 | 1.17725282 | |
| | HepG2_log2FC | 0.46267671 | 1.05124023 | |
| | SKNSH_log2FC | 0.41405871 | 1.16609108 | |
| |
| ## Usage |
| |
| ```py |
| from datasets import load_dataset |
|
|
| ds = load_dataset("HuggingFaceBio/malinois-mpra-regression") |
| train = ds["train"] |
| validation_metric = ds["validation"].filter(lambda row: row["all_se_le_1"]) |
| |
| example = train[0] |
| sequence = example["forward_rc_concat"] |
| labels = [ |
| example["K562_log2FC_train_zscore"], |
| example["HepG2_log2FC_train_zscore"], |
| example["SKNSH_log2FC_train_zscore"], |
| ] |
| ``` |
| |
| To recreate the dataset: |
|
|
| ```sh |
| python create_dataset.py --repo-id HuggingFaceBio/malinois-mpra-regression --push |
| ``` |
|
|
