Add files using upload-large-folder tool

HAEC185_sQTL_credible_sets_splaire_scores.tsv.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc64aebc14e50d75d69c0976545fe45573c79cf89e0ad2a990c390d26956b462
+size 29976117

README.md

@@ -1,3 +1,124 @@
 ---
 license: cc-by-4.0
+task_categories:
+  - other
+tags:
+  - splicing
+  - genomics
+  - sqtl
+  - variant-effect-prediction
 ---
+
+# SPLAIRE scores for HAEC185 sQTL credible sets
+
+SPLAIRE-ref and SPLAIRE-var variant effect predictions for all variants in the HAEC185 sQTL credible sets.
+
+## Files
+
+- `HAEC185_sQTL_credible_sets_splaire_scores.tsv.gz` — per-credible-set-row summary (146,362 rows × 80 columns, 29 MB)
+- `h5/cs_all.splaire.ref.h5` — full ref/alt per-position scores from SPLAIRE-ref (86,549 unique variants, 26 GB)
+- `h5/cs_all.splaire.var.h5` — full ref/alt per-position scores from SPLAIRE-var (same variants, 26 GB)
+
+The TSV is the summary used for most downstream analysis. The H5s hold the raw per-position score arrays the TSV is derived from — use them if you need scores at custom positions or other aggregations.
+
+## Source data
+
+146,362 variant-phenotype rows in the HAEC185 sQTL credible sets, deduplicated to 86,549 unique (variant, strand) pairs. Each pair scored with both SPLAIRE models. Input window 20,001 bp centered on each variant, 10,001 output positions covering ±5 kb.
+
+## Indel handling
+
+Indels are scored by substituting the alt allele into the genomic sequence and adjusting the window length. After prediction, alt outputs are realigned to ref coordinates following the SpliceAI/Pangolin convention — insertions collapsed by max across extra positions, deletions padded with zeros.
+
+## H5 contents
+
+Each H5 contains five datasets (variant at center index 5000 of the 10,001-position window):
+
+| dataset | shape | dtype | meaning |
+|---|---|---|---|
+| `cls_ref` | (86549, 10001, 3) | float32 | classifier softmax (neither, acceptor, donor), ref allele |
+| `cls_alt` | (86549, 10001, 3) | float32 | same, alt allele |
+| `reg_ref` | (86549, 10001) | float32 | regression SSU (sigmoid, 0-1), ref allele |
+| `reg_alt` | (86549, 10001) | float32 | same, alt allele |
+| `var_key` | (86549,) | vlen UTF-8 string | variant identifier (`chr:pos:ref:alt`), sorted by (chrom, var_pos) |
+
+Use `var_key` to align H5 rows back to `variant_id` in the TSV.
+
+## TSV columns
+
+First 8 from the original credible sets file:
+
+`gene`, `phenotype`, `ensembl_id`, `variant_id`, `credible_set_number`, `posterior_inclusion_probability`, `chr`, `strand`
+
+72 score columns, organized as:
+
+### Max/min delta per variant (36 columns)
+
+For each model (`splaire`, `splaireVar`) and head (`don`, `acc`, `ssu`):
+
+| column | meaning |
+|---|---|
+| `{model}_{head}_max_inc` | largest positive delta (alt − ref) in the 10 kb window |
+| `{model}_{head}_max_inc_off` | offset from the variant (bp) |
+| `{model}_{head}_max_inc_pos` | genomic coordinate of the max increase |
+| `{model}_{head}_max_dec` | largest negative delta (alt − ref) |
+| `{model}_{head}_max_dec_off` | offset from the variant (bp) |
+| `{model}_{head}_max_dec_pos` | genomic coordinate of the max decrease |
+
+### Scores at intron coordinates (36 columns)
+
+For each model, head, and leafcutter junction coordinate (`istart` = intron start, `iend` = intron end):
+
+| column | meaning |
+|---|---|
+| `{model}_{head}_{coord}_ref` | ref score at this boundary |
+| `{model}_{head}_{coord}_alt` | alt score at this boundary |
+| `{model}_{head}_{coord}_delta` | alt − ref at this boundary |
+
+Values are NaN when the intron boundary falls outside the ±5 kb scored window (~87% of rows).
+
+## Models
+
+- `splaire` — SPLAIRE-ref, trained on reference genome sequences
+- `splaireVar` — SPLAIRE-var, trained on sequences containing personal genetic variants
+
+Each is a 5-fold ensemble of dilated CNNs. Predictions are averaged across folds.
+
+## Output heads
+
+- `don` — donor splice site probability (classifier softmax index 2)
+- `acc` — acceptor splice site probability (classifier softmax index 1)
+- `ssu` — splice site usage (regression sigmoid, 0-1)
+
+## Usage
+
+```python
+import pandas as pd
+import h5py
+from huggingface_hub import hf_hub_download
+
+tsv = hf_hub_download(
+    repo_id="mrunyan1/splaire-haec-sqtl-credible-sets",
+    filename="HAEC185_sQTL_credible_sets_splaire_scores.tsv.gz",
+    repo_type="dataset",
+)
+df = pd.read_csv(tsv, sep="\t")
+print(df.shape)  # (146362, 80)
+
+# load full per-position scores for one model
+h5_path = hf_hub_download(
+    repo_id="mrunyan1/splaire-haec-sqtl-credible-sets",
+    filename="h5/cs_all.splaire.ref.h5",
+    repo_type="dataset",
+)
+with h5py.File(h5_path, "r") as f:
+    var_keys = f["var_key"][:]
+    cls = f["cls_ref"][0]   # (10001, 3) softmax track for first variant
+    ssu = f["reg_ref"][0]   # (10001,) SSU track for first variant
+```
+
+## Download
+
+```bash
+pip install huggingface_hub
+hf download --repo-type dataset mrunyan1/splaire-haec-sqtl-credible-sets
+```

h5/cs_all.splaire.ref.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62e277bcc9a214ccd722e8ae66b4b64b92ac18fcd6051bd84cbff180bd27c635
+size 27703311856

h5/cs_all.splaire.var.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1345df3a1cbcab1ffb785e01c6c73040ce25dbad40272ecab2359a282ae3f94
+size 27703311856

upload.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+"""upload splaire-haec-sqtl-credible-sets to huggingface hub. resumable, parallel."""
+
+from pathlib import Path
+from huggingface_hub import HfApi, upload_large_folder
+
+REPO_ID = "mrunyan1/splaire-haec-sqtl-credible-sets"
+STAGING = Path("/scratch/runyan.m/hf_uploads/splaire-haec-sqtl-credible-sets")
+
+api = HfApi()
+
+# expects repo already created on the hub. uncomment to create:
+# api.create_repo(repo_id=REPO_ID, repo_type="dataset", exist_ok=True, private=False)
+
+# upload everything in STAGING. handles LFS automatically, resumable across retries
+upload_large_folder(
+    folder_path=str(STAGING),
+    repo_id=REPO_ID,
+    repo_type="dataset",
+    num_workers=4,
+    print_report=True,
+)

upload.sbatch

@@ -0,0 +1,15 @@
+#!/bin/bash
+#SBATCH --job-name=hf_upload_sqtl_cs
+#SBATCH --partition=short
+#SBATCH --time=24:00:00
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --mem=8G
+#SBATCH --output=/scratch/runyan.m/logs/hf_upload_sqtl_cs_%j.out
+#SBATCH --error=/scratch/runyan.m/logs/hf_upload_sqtl_cs_%j.err
+
+set -e
+
+cd /scratch/runyan.m/hf_uploads/splaire-haec-sqtl-credible-sets
+/scratch/runyan.m/conda_envs/splaire_env/bin/python upload.py