Add Malinois MPRA dataset card

README.md

@@ -1,67 +1,94 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: split
-    dtype: string
-  - name: chromosome
-    dtype: string
-  - name: data_project
-    dtype: string
-  - name: oligo
-    dtype: string
-  - name: variant_class
-    dtype: string
-  - name: sequence
-    dtype: string
-  - name: sequence_length
-    dtype: int32
-  - name: reverse_complement
-    dtype: string
-  - name: forward_rc_concat
-    dtype: string
-  - name: K562_log2FC
-    dtype: float32
-  - name: HepG2_log2FC
-    dtype: float32
-  - name: SKNSH_log2FC
-    dtype: float32
-  - name: K562_lfcSE
-    dtype: float32
-  - name: HepG2_lfcSE
-    dtype: float32
-  - name: SKNSH_lfcSE
-    dtype: float32
-  - name: K562_log2FC_train_zscore
-    dtype: float32
-  - name: HepG2_log2FC_train_zscore
-    dtype: float32
-  - name: SKNSH_log2FC_train_zscore
-    dtype: float32
-  - name: all_se_le_1
-    dtype: bool
-  - name: any_log2fc_gt_0_5
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 627457079
-    num_examples: 668946
-  - name: validation
-    num_bytes: 58894846
-    num_examples: 62406
-  - name: test
-    num_bytes: 62485771
-    num_examples: 66712
-  download_size: 256932482
-  dataset_size: 748837696
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+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("edbeeching/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 edbeeching/malinois-mpra-regression --push
+```