File size: 8,748 Bytes
32e52c8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | from __future__ import annotations
import json
import re
import sys
from pathlib import Path
import numpy as np
import pandas as pd
ROOT = Path(sys.argv[1]).resolve()
OUT = Path(sys.argv[2]).resolve()
APP_ROOT = ROOT
sys.path.insert(0, str(APP_ROOT))
from app import ANNOTATION_EXAMPLES, ASSEMBLY_SUMMARY, SPECIES_METRICS # noqa: E402
from explorer.dimensionality import DimensionalityRepository, compute_projection # noqa: E402
SPECIES = {
"Species 01": "species01",
"Species 02": "species02",
"Species 03": "species03",
}
BASE_SOURCES = [
"EggNOG_OG", "GO_EggNOG", "GO_ESM2_150M_MF",
"GO_ESMC_600M_ProteinFunction", "ESM_localization", "GO_union",
"Pfam_HMMER", "EC_EggNOG", "KEGG_KO_EggNOG",
"KEGG_KO_KofamScan", "KEGG_KO_union",
]
HIERARCHIES = {
"EggNOG_OG": ["Direct terms"],
"GO_EggNOG": ["Direct terms", "GO ancestors"],
"GO_ESM2_150M_MF": ["Direct terms", "GO ancestors"],
"GO_ESMC_600M_ProteinFunction": ["Direct terms", "GO ancestors"],
"ESM_localization": ["Direct terms"],
"GO_union": ["Direct terms", "GO ancestors"],
"Pfam_HMMER": ["Direct terms"],
"EC_EggNOG": ["Direct terms", "EC level 1 class"],
"KEGG_KO_EggNOG": ["Direct terms", "KEGG pathways", "KEGG level 1", "KEGG level 2"],
"KEGG_KO_KofamScan": ["Direct terms", "KEGG pathways", "KEGG level 1", "KEGG level 2"],
"KEGG_KO_union": ["Direct terms", "KEGG pathways", "KEGG level 1", "KEGG level 2"],
}
def clean(value):
if isinstance(value, str):
return value.replace("TRINITY_", "").replace("TRINITY", "")
return value
def slug(value: str) -> str:
return re.sub(r"[^a-zA-Z0-9]+", "_", value).strip("_").lower()
def write_json(path: Path, value) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(value, ensure_ascii=False, separators=(",", ":")))
def frame_payload(frame: pd.DataFrame, columns: list[str]) -> dict:
frame = frame[columns].copy()
for column in frame.columns:
if column in {"gene_id", "genes", "term", "name", "direction"}:
frame[column] = frame[column].map(clean)
if pd.api.types.is_numeric_dtype(frame[column]):
frame[column] = pd.to_numeric(frame[column], errors="coerce").round(8)
return json.loads(frame.to_json(orient="split", index=False))
def source_for_hierarchy(base: str, hierarchy: str) -> str:
if hierarchy == "Direct terms":
return base
if hierarchy == "GO ancestors":
return f"{base}__GO_ANCESTORS"
if hierarchy == "EC level 1 class":
return "EC_EggNOG__EC_LEVEL1"
prefixes = {
"KEGG pathways": "KEGG_PATHWAY",
"KEGG level 1": "KEGG_LEVEL1",
"KEGG level 2": "KEGG_LEVEL2",
}
if hierarchy in prefixes:
method_name = {
"KEGG_KO_EggNOG": "EggNOG",
"KEGG_KO_KofamScan": "KofamScan",
"KEGG_KO_union": "consensus",
}.get(base)
if method_name:
return f"{prefixes[hierarchy]}_{method_name}"
return base
def main() -> None:
OUT.mkdir(parents=True, exist_ok=True)
(OUT / "de").mkdir(exist_ok=True)
(OUT / "enrichment").mkdir(exist_ok=True)
app_data = {
"title": "Transcriptomes Explorer",
"assemblySummary": ASSEMBLY_SUMMARY.to_dict(orient="records"),
"annotationExamples": {
species: [
dict(zip([
"Approach", "Tool / result", "Database or type", "Proteins", "Genes",
"Protein -> gene", "Example annotation", "Description / evidence",
], [clean(x) for x in row]))
for row in rows
]
for species, rows in ANNOTATION_EXAMPLES.items()
},
"species": [],
"deIndex": {},
"enrichmentIndex": {},
"sourceOrder": BASE_SOURCES,
"hierarchies": HIERARCHIES,
}
for species_label, species_slug in SPECIES.items():
data_dir = ROOT / "data" / species_slug
manifest = json.loads((data_dir / "manifest.json").read_text())
contrasts = pd.read_parquet(data_dir / "contrast_summary.parquet")
de = pd.read_parquet(data_dir / "de_significant.parquet")
enrichment = pd.read_parquet(data_dir / "enrichment.parquet")
species_meta = SPECIES_METRICS[species_label]
app_data["species"].append({
"label": species_label,
"slug": species_slug,
"genes": species_meta["genes"],
"proteins": species_meta["proteins"],
"contrasts": len(contrasts),
"samples": manifest.get("sample_count", 0),
})
app_data["deIndex"][species_slug] = []
app_data["enrichmentIndex"][species_slug] = []
contrast_meta = contrasts.set_index("contrast_id").to_dict(orient="index")
for contrast_id, meta in contrast_meta.items():
contrast_id = str(contrast_id)
for method in ("DESeq2", "edgeR"):
method_frame = de[(de["contrast_id"].astype(str) == contrast_id) & (de["method"] == method)].copy()
if method == "DESeq2":
columns = ["gene_id", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "direction"]
else:
columns = ["gene_id", "logFC", "logCPM", "F", "PValue", "FDR", "direction"]
columns = [c for c in columns if c in method_frame.columns]
filename = f"{species_slug}_{slug(contrast_id)}_{method.lower()}.json"
write_json(OUT / "de" / filename, frame_payload(method_frame, columns))
app_data["deIndex"][species_slug].append({
"contrast_id": contrast_id,
"method": method,
"file": f"de/{filename}",
"group_a": str(meta.get("group_a", "group 1")),
"group_b": str(meta.get("group_b", "group 2")),
"n_group_a": int(meta.get("n_group_a", 0)),
"n_group_b": int(meta.get("n_group_b", 0)),
"n_genes_input": int(meta.get("n_genes_input", 0)),
"n_genes_tested": int(meta.get("n_genes_tested", 0)),
})
eframe = enrichment[
(enrichment["contrast_id"].astype(str) == contrast_id)
& (enrichment["method"] == method)
].copy()
ecolumns = [
"source_id", "direction", "term", "name", "overlap",
"foreground_size", "term_size", "background_size",
"fold_enrichment", "pvalue", "padj", "genes",
]
rows = []
for source_id, source_frame in eframe.groupby("source_id", sort=False):
source_frame = source_frame.sort_values(["padj", "pvalue"], na_position="last")
rows.append(source_frame.head(40))
app_data["enrichmentIndex"][species_slug].append({
"contrast_id": contrast_id,
"method": method,
"source_id": str(source_id),
"file": f"enrichment/{species_slug}_{slug(contrast_id)}_{method.lower()}.json",
"universe_terms": int(source_frame["term"].nunique()),
})
eweb = pd.concat(rows, ignore_index=True) if rows else eframe.head(0)
write_json(OUT / "enrichment" / f"{species_slug}_{slug(contrast_id)}_{method.lower()}.json", frame_payload(eweb, ecolumns))
# Static 3D coordinates are precomputed once for a stable browser-only demo.
dim_root = ROOT / "data" / "dimensionality"
dim_repo = DimensionalityRepository(dim_root)
projections = {}
for method in ("PCA", "UMAP"):
for label, limit in (("all", None), ("5000", 5000), ("1000", 1000)):
expression, metadata = dim_repo.load(species_label)
result = compute_projection(expression, metadata, method, limit)
projections[f"{method}_{label}"] = {
"axes": list(result.axis_columns),
"selected_gene_count": result.selected_gene_count,
"expressed_gene_count": result.expressed_gene_count,
"explained_variance": list(result.explained_variance) if result.explained_variance else None,
"rows": json.loads(result.coordinates.to_json(orient="records")),
}
write_json(OUT / "dimensionality" / f"{species_slug}.json", projections)
write_json(OUT / "app-data.json", app_data)
if __name__ == "__main__":
main()
|