sync: src/ now reflects statlens-0.1.7 wheel

src/LICENSE

@@ -0,0 +1,202 @@
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src/README.md

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+# statLens
+
+> Self-hosted DEA method selector. Upload a TSV plus a few sentences of study
+> context; statLens extracts a structured study-design summary, lets you
+> review it, then runs the matching DESeq2 / limma pipeline on your own GPU.
+
+statLens is a single-binary tool that wraps a fine-tuned **Qwen3-32B + LoRA**
+classifier in front of 13 differential-expression analysis pipelines. It runs
+fully on your machine — no external API calls, no data leaves the box.
+
+---
+
+## How it works
+
+```
+TSV + study context
+        │
+        ▼
+[Stage 1] LLM reads a pandas-built data preview + your study context,
+          extracts a 21-field SchemaSummary describing the study design
+          (column names, group levels, time/batch presence, reference
+          level, etc.).
+        │
+        ▼
+[Stage 2] You see the SchemaSummary in the browser. Edit any field
+          you disagree with. (No backend call — purely review.)
+        │
+        ▼
+[Stage 3] LLM looks at the (edited) SchemaSummary and picks one of
+          13 DEA pipelines, or declines with `none_of_these`. The
+          adapter then uses the column names from your confirmed
+          schema to build the pipeline inputs.
+        │
+        ▼
+        DESeq2 / limma runs → 5 plots, 2 result tables, a markdown report,
+        and a downloadable result.zip.
+```
+
+Two LLM calls, one user review in the middle. End-to-end ≈ 35–45 seconds
+per request, with the schema review bracketed by the user's reading time.
+
+---
+
+## Prerequisite (you do this once)
+
+You need **Qwen3-32B** (~64 GB BF16) on local disk. If you don't have it:
+
+```bash
+huggingface-cli download Qwen/Qwen3-32B --local-dir ~/models/qwen3-32b
+```
+
+> Mainland China: prefix the command with `HF_ENDPOINT=https://hf-mirror.com`,
+> or use ModelScope:
+> `modelscope download --model Qwen/Qwen3-32B --local_dir ~/models/qwen3-32b`
+
+statLens auto-discovers the base model in any of these locations:
+
+- `~/models/qwen3-32b/`
+- `/root/autodl-tmp/models/qwen3-32b/` (AutoDL)
+- `/workspace/models/qwen3-32b/` (RunPod / Lambda)
+- `/data/models/qwen3-32b/`, `/mnt/models/qwen3-32b/`
+- the HuggingFace Hub cache
+
+If yours is elsewhere, pass `--base-model PATH` or set
+`STATLENS_BASE_MODEL=/path/to/qwen3-32b`.
+
+---
+
+## Quick start
+
+```bash
+# (Mainland China only — set once, persists across sessions)
+grep -q HF_ENDPOINT ~/.bashrc \
+  || echo 'export HF_ENDPOINT=https://hf-mirror.com' >> ~/.bashrc
+source ~/.bashrc
+
+# 1. Install statLens (~5 min on first run; pulls ~2 GB of CUDA dependencies)
+pip install ${HF_ENDPOINT:-https://huggingface.co}/domizzz2025/statLens/resolve/main/statlens-0.1.7-py3-none-any.whl
+
+# 2. Launch
+statlens serve
+```
+
+After ~80 s you should see:
+
+```
+══════════════════════════════════════════════════════
+  ✅ statLens ready
+     open in browser:  http://localhost:7860/
+     Ctrl+C to stop.
+══════════════════════════════════════════════════════
+```
+
+Open `http://localhost:7860`, drop a TSV, write a short study-context
+description, click **Classify & run**, review the extracted schema, then
+**Run pipeline →**.
+
+---
+
+## What happens when you click *Classify & run*
+
+```
+[Browser]
+  POST /api/extract  (TSV + context)
+        │
+        ▼  (~20–30 s)
+  Schema card appears in the browser:
+    • 21 structured fields the LLM extracted from your data
+    • 8 "decision-driving" fields (highlighted) plus 13 context fields
+      including column names (group / subject / time / batch) and
+      reference (control) level
+    • All editable
+
+[You]
+  Review and edit any field that looks wrong.
+  Example: the LLM read your TSV and inferred a `seq_run` column
+  is a batch effect; if you know it isn't, flip the toggle.
+
+[Browser]
+  POST /api/run_pipeline  (the edited schema)
+        │
+        ▼  (~5 s LLM + ~15 s pipeline)
+  Result card appears:
+    • Final pipeline label and the LLM's reasoning
+    • 5 plots (volcano, MA, PCA, heatmap, top-DE barplot)
+    • 2 CSVs (full results + significant features)
+    • Download all → result.zip
+```
+
+Crucially, the label is decided by the LLM in stage 3, **after** you've had a
+chance to correct the schema. The middle layer is your audit point, not a
+black-box pass-through.
+
+---
+
+## What `statlens serve` does for you
+
+Auto-handled (you don't think about any of these):
+
+1. checks for an NVIDIA GPU with ≥ 22 GB VRAM
+2. auto-detects the Qwen3-32B base model on disk
+3. fetches the 1 GB statLens LoRA from HuggingFace if not already cached
+4. spawns the LLM backend (LLaMA-Factory + bitsandbytes 4-bit + LoRA)
+5. spawns the FastAPI web app on `:7860`
+6. opens your browser if there is one
+
+Verbose backend logs go to `~/.cache/statlens/llm.log`; tail it only when
+something fails.
+
+---
+
+## Hardware
+
+| | |
+|---|---|
+| GPU | NVIDIA, **≥ 22 GB VRAM** (RTX 3090 / 4090 / A40 / A100 / 5090 …) |
+| OS  | Linux x86_64 |
+| RAM | 32 GB+ |
+| Disk | 75 GB free (64 GB Qwen + 1 GB LoRA + working space) |
+
+Mac / Windows / AMD: not supported (vLLM + bitsandbytes are CUDA-only).
+
+---
+
+## TSV format
+
+Wide format, one row per sample. Required columns:
+
+| column | meaning |
+|---|---|
+| `sample_id` | unique per row |
+| `subject_id` | repeats for paired or longitudinal samples |
+| a **design column** | `group` / `condition` / `treatment` / `clinical_group` / `tumor_stage` / `subtype` / `arm` / … (fuzzy-matched) |
+| **feature columns** | prefixed **exactly** one of: `gene_`, `asv_`, `prot_`, `metab_`, `otu_`, `feat_`. Other prefixes are not recognised and the adapter will report `No feature columns found`. |
+
+Optional: time-like columns (`time_day`, `collection_day`, `time_week`, …)
+and batch-like columns (`batch`, `site`, `run`, `ms_batch`, …).
+
+13 demonstration TSVs ship with the package — list them with:
+
+```bash
+ls $(python3 -c 'import statlens, os; print(os.path.dirname(statlens.__file__))')/data/examples/
+```
+
+---
+
+## Output
+
+Each run lands under `~/.cache/statlens/runs/<run_id>/`:
+
+```
+out/
+├── statlens_report.md              # human-readable summary + reasoning
+├── statlens_report.json            # machine-readable sidecar
+└── pipeline_output/
+    ├── volcano_plot.png
+    ├── PCA_plot.png
+    ├── MA_plot.png
+    ├── top_DE_genes_heatmap.png
+    ├── top20_DE_genes_barplot.png
+    ├── results.csv
+    ├── significant_genes.csv
+    └── run.log
+result.zip                          # everything above, packaged
+```
+
+The **Download all** button in the web UI returns `result.zip`.
+
+---
+
+## Pipeline labels
+
+statLens classifies a study into one of **13** DEA scenarios, or returns
+`none_of_these` (a 14th "kill-switch" output) when the design falls outside
+its training space.
+
+| family | label | when |
+|---|---|---|
+| Count (DESeq2) | `Count_DESeq2_basic` | 2 groups, no batch / time / pairing |
+|     | `Count_DESeq2_with_batch` | 2 groups + batch covariate |
+|     | `Count_DESeq2_paired_or_repeated` | matched samples within subject |
+|     | `Count_DESeq2_multi_group` | ≥ 3 independent groups |
+|     | `Count_DESeq2_time_course` | single cohort, ≥ 3 time points |
+|     | `Count_DESeq2_group_time_interaction` | ≥ 2 groups × multiple time points |
+|     | `Count_DESeq2_ZINB` | counts dominated by zeros (>40 %), e.g. 16S |
+| Continuous (limma) | `Continuous_limma_basic` | 2 groups, no batch / time / pairing |
+|     | `Continuous_limma_with_batch` | 2 groups + batch covariate |
+|     | `Continuous_limma_paired_or_repeated` | pre/post or matched samples |
+|     | `Continuous_limma_multi_group` | ≥ 3 independent groups |
+|     | `Continuous_limma_time_course` | single cohort, ≥ 3 time points |
+|     | `Continuous_limma_group_time_interaction` | groups × time within subject |
+| (decline) | `none_of_these` | survival / network inference / single-sample / non-omics — no forced fit |
+
+---
+
+## Schema review is your responsibility
+
+Stage 1 extracts a 21-field SchemaSummary from your TSV; you review and
+optionally edit it before stage 3 picks the pipeline. statLens trusts
+the schema you confirm:
+
+- If a column-name field (e.g. `subject_id_column`, `time_variable_column`,
+  `batch_variables`, `primary_group_variable`) **points to a column that
+  doesn't exist in your TSV**, the adapter raises `ValueError` rather
+  than silently falling back to a similar-named column. Fix the typo or
+  the missing column before re-running.
+- If `reference_level` is set but isn't one of the observed levels, the
+  adapter likewise raises.
+- If a column-name field is **left empty**, the adapter falls back to its
+  built-in canonical names (`subject_id`, `time_day`, `batch`, …) and a
+  regex pattern. So leave fields blank when you want auto-detection;
+  only fill them in for strict matching.
+
+The `manifest` written under `<run>/_cache/adapt_manifest.json` records
+the exact resolved column names (`resolved_design_col`,
+`resolved_time_col`, `resolved_subject_col`, `resolved_batch_col`,
+`resolved_ref_level`, `feature_prefix_used`) — handy for debugging.
+
+---
+
+## Headless server: reaching `localhost:7860` from elsewhere
+
+If `statlens serve` cannot open a browser (AutoDL, RunPod, Lambda, …), use
+one of these:
+
+| | command | works for |
+|---|---|---|
+| **Public URL** | `cloudflared tunnel --url http://localhost:7860` | any device, any network |
+| **SSH tunnel** | `ssh -fNL 7860:localhost:7860 user@server` (run on your laptop) | quick local dev |
+| **curl only** | `curl -X POST http://localhost:7860/api/run -F "context=..." -F "tsv=@data.tsv"` | scripting / no browser |
+
+---
+
+## Subcommands
+
+```
+statlens serve                                   # main entry point
+statlens download                                # pre-fetch the LoRA only (~1 GB)
+statlens info                                    # show GPU / cache / paths
+statlens classify --tsv DATA --context CTX --out DIR
+                                                 # one-shot CLI mode (no browser)
+statlens --version
+```
+
+`statlens classify` runs both LLM stages back-to-back without a review pause —
+useful for batch processing.
+
+---
+
+## API endpoints
+
+| route | method | purpose |
+|---|---|---|
+| `/` | GET | serve the web UI |
+| `/api/extract` | POST (multipart: `tsv`, `context`) | stage 1 — return a SchemaSummary |
+| `/api/run_pipeline` | POST (form: `run_id`, `schema` JSON) | stage 3 — pick label + run pipeline |
+| `/api/run` | POST (multipart: `tsv`, `context`) | legacy single-shot path (no review) |
+| `/api/artifact/{run_id}/{filename}` | GET | fetch a single PNG/CSV |
+| `/api/zip/{run_id}` | GET | fetch the packaged result |
+| `/api/csv_preview/{run_id}/{filename}` | GET | first N rows of a result CSV as JSON |
+
+---
+
+## Models
+
+| component | source | size | license |
+|---|---|---|---|
+| base | [`Qwen/Qwen3-32B`](https://huggingface.co/Qwen/Qwen3-32B) (BF16) | 64 GB | Apache-2.0 |
+| LoRA | [`domizzz2025/statLens`](https://huggingface.co/domizzz2025/statLens) | 1 GB | Apache-2.0 |
+
+The LoRA is auto-downloaded on first run; the base model is yours to provide.
+
+---
+
+## Troubleshooting
+
+| symptom | fix |
+|---|---|
+| `Network is unreachable` during `pip install` (mainland China) | `export HF_ENDPOINT=https://hf-mirror.com` and retry |
+| `LocalEntryNotFoundError` when LoRA auto-fetches | same as above — set `HF_ENDPOINT` before `statlens serve` |
+| `no base model found` | put Qwen3-32B in one of the auto-search paths, or pass `--base-model PATH` |
+| `CUDA out of memory` on startup | a previous `statlens serve` is still holding GPU memory: `pkill -9 -f statlens; nvidia-smi --query-compute-apps=pid --format=csv,noheader \| xargs -r kill -9` |
+| `address already in use` | a previous instance is bound — kill it first |
+| LLM never becomes ready | tail `~/.cache/statlens/llm.log` to see the LLaMA-Factory error |
+| schema field looks wrong in the browser | edit it directly; the LLM picks the label from your edits, not the original extraction |
+| `Schema specified <field>=…, but no such column in TSV` | a column-name field in the schema doesn't match your TSV. Either fix the field, or clear it to use auto-detection. |
+| `Schema reference_level=… not in observed levels` | `reference_level` doesn't match any actual group level. Set it to one of the values shown in `group_levels`, or clear it. |
+| `No feature columns found. Expected one of these prefixes: …` | rename your feature columns to start with `gene_` / `asv_` / `prot_` / `metab_` / `otu_` / `feat_`. |
+| `upload exceeds N MB limit` | raise the cap with `STATLENS_MAX_UPLOAD_MB=500 statlens serve` (default 100 MB). |
+
+---
+
+## Source · License
+
+- Wheel + LoRA + source : <https://huggingface.co/domizzz2025/statLens>
+- License : Apache-2.0

src/pyproject.toml

@@ -0,0 +1,82 @@
+[build-system]
+requires = ["hatchling>=1.21"]
+build-backend = "hatchling.build"
+
+[project]
+name = "statlens"
+version = "0.1.7"
+description = "Self-hosted DEA method selector — point a TSV at a Qwen3-32B + LoRA classifier and get the right DESeq2/limma pipeline auto-run."
+readme = "README.md"
+requires-python = ">=3.10"
+license = { text = "Apache-2.0" }
+authors = [
+    { name = "statLens contributors" },
+]
+keywords = ["bioinformatics", "differential expression", "DESeq2", "limma", "LLM", "Qwen3", "vLLM"]
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Topic :: Scientific/Engineering :: Bio-Informatics",
+]
+
+dependencies = [
+    # web
+    "fastapi>=0.115",
+    "uvicorn[standard]>=0.30",
+    "python-multipart>=0.0.10",
+    "httpx>=0.27",
+
+    # serving — LLaMA-Factory wraps transformers + bitsandbytes for streamable bnb 4-bit
+    "llamafactory>=0.9,<1.0",
+    "bitsandbytes>=0.43",
+    "torch>=2.1",
+    # torchaudio 2.11+ requires CUDA 13; pin <2.11 to keep CUDA 12.x users happy.
+    # (llamafactory imports torchaudio unconditionally for multimodal templates.)
+    "torchaudio<2.11",
+    "transformers>=4.45",
+    "peft>=0.12",
+    "huggingface_hub>=0.25",
+    "pyyaml>=6.0",
+
+    # data + plots used by V4 pipelines
+    "pandas>=2.0",
+    "numpy>=1.26",
+    "matplotlib>=3.8",
+    "seaborn>=0.13",
+    "scipy>=1.13",
+    "statsmodels>=0.14",
+    "scikit-learn>=1.4",
+
+    # DESeq2 (pure Python; no R)
+    "pydeseq2>=0.5",
+
+    # CLI niceties
+    "rich>=13.0",
+    "click>=8.0",
+]
+
+[project.urls]
+Homepage   = "https://github.com/domi-fdz-zz/statLens"
+Repository = "https://github.com/domi-fdz-zz/statLens"
+Issues     = "https://github.com/domi-fdz-zz/statLens/issues"
+
+[project.scripts]
+statlens = "statlens.cli:main"
+
+[tool.hatch.build.targets.wheel]
+packages = ["statlens"]
+# data/ + pipelines/ are inside the package dir and picked up automatically;
+# we only need to make sure they're not excluded.
+include = [
+    "statlens/**/*.py",
+    "statlens/**/*.html",
+    "statlens/**/*.tsv",
+    "statlens/**/*.sh",
+    "statlens/**/*.md",
+]

src/statlens/__init__.py

@@ -0,0 +1,24 @@
+"""statLens — DEA method selector backed by Qwen3-32B + LoRA via LLaMA-Factory."""
+
+__version__ = "0.1.7"
+
+# Resource lookups inside the wheel
+from importlib.resources import files
+
+PKG_DATA = files(__name__) / "data"
+PKG_PIPELINES = files(__name__) / "pipelines"
+
+# Reference HF coordinates (informational; we DO NOT auto-download the base).
+# Users must supply a local copy via --base-model PATH (or STATLENS_BASE_MODEL).
+DEFAULT_BASE_MODEL_REF = "Qwen/Qwen3-32B"          # 64 GB BF16
+DEFAULT_LORA_REPO = "domizzz2025/statLens"
+DEFAULT_LORA_SUBFOLDER = "qwen3_32b_lora_v1"
+DEFAULT_LORA_RANK = 32
+
+# Quantization: matches training (bitsandbytes NF4 4-bit).
+# ~16 GB weights + KV cache → fits on a single 24 GB GPU.
+DEFAULT_QUANTIZATION = "bitsandbytes"
+
+# Local server defaults
+LOCAL_LLM_PORT = 8000
+LOCAL_WEB_PORT = 7860

src/statlens/adapter.py

@@ -0,0 +1,466 @@
+"""
+adapter.py — Convert one wide-format TSV into the pair of CSVs the
+statLens-V4 pipelines expect, using a SchemaSummary (when given) to
+resolve column names + reference level.
+
+For DESeq2-family labels this writes:
+    <outdir>/metadata.csv  (index=sample_id, cols=design)
+    <outdir>/counts.csv    (index=gene_id,   cols=sample_id)  -- transposed
+
+For limma-family labels this writes:
+    <outdir>/metadata.csv
+    <outdir>/expression.csv (same shape as counts but log-scale continuous)
+
+Resolution rules (locked, see plan v0.1.7):
+
+  Rule A  Schema field non-empty → strict match in TSV; raise if not found.
+          Schema field empty (or no schema given) → fall back to the
+          canonical name from LABEL_SPEC, then to the regex pattern.
+
+  Rule B  Reference level is resolved by _resolve_ref_level() with this
+          fallback chain:
+            1. schema.reference_level (if non-empty AND ∈ group_levels)
+            2. _infer_value_map() heuristic (2-group case)
+            3. LABEL_SPEC[label].ref_level (multi-group canonical)
+            4. sorted(group_levels)[0]
+
+  Rule C  force_label callers should call _synth_schema_for_force_label()
+          (in statlens_run.py) — schema is mostly empty, fallback chain
+          carries the load.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import re as _re
+from pathlib import Path
+
+import pandas as pd
+
+
+# Per-label mapping: TSV columns/values → pipeline expectations.
+# Used as canonical-name + ref_level fallback when schema doesn't supply.
+LABEL_SPEC = {
+    "Count_DESeq2_basic": {
+        "family": "deseq2", "pipeline": "deseq2_basic",
+        "design_col": "group", "value_map": {"Control": "control", "Treated": "case"},
+        "ref_level": "control", "test_level": "case",
+    },
+    "Count_DESeq2_with_batch": {
+        "family": "deseq2", "pipeline": "deseq2_with_batch",
+        "design_col": "group", "value_map": {"Control": "control", "Treated": "case"},
+        "batch_col": "batch",
+        "ref_level": "control", "test_level": "case",
+    },
+    "Count_DESeq2_paired_or_repeated": {
+        "family": "deseq2", "pipeline": "deseq2_paired",
+        "design_col": "condition", "value_map": {"Normal": "control", "Tumor": "case"},
+        "subject_col": "subject_id",
+        "ref_level": "control", "test_level": "case",
+    },
+    "Count_DESeq2_multi_group": {
+        "family": "deseq2", "pipeline": "deseq2_multi_group",
+        "design_col": "tumor_stage", "value_map": None,
+        "ref_level": "Stage_I",
+    },
+    "Count_DESeq2_time_course": {
+        "family": "deseq2", "pipeline": "deseq2_time_course",
+        "time_col": "time_day", "baseline_time": "0",
+    },
+    "Count_DESeq2_group_time_interaction": {
+        "family": "deseq2", "pipeline": "deseq2_group_time_interaction",
+        "design_col": "group", "value_map": {"NonResponder": "control", "Responder": "case"},
+        "time_col": "time_day",
+        "ref_level": "control", "test_level": "case",
+    },
+    "Count_DESeq2_ZINB": {
+        "family": "deseq2", "pipeline": "deseq2_zinb",
+        "design_col": "condition", "value_map": {"Healthy": "control", "IBD": "case"},
+        "ref_level": "control", "test_level": "case",
+    },
+    "Continuous_limma_basic": {
+        "family": "limma", "pipeline": "limma_basic",
+        "design_col": "group", "value_map": {"Control": "control", "Case": "case"},
+        "ref_level": "control", "test_level": "case",
+    },
+    "Continuous_limma_with_batch": {
+        "family": "limma", "pipeline": "limma_with_batch",
+        "design_col": "group", "value_map": {"Control": "control", "Case": "case"},
+        "batch_col": "batch",
+        "ref_level": "control", "test_level": "case",
+    },
+    "Continuous_limma_paired_or_repeated": {
+        "family": "limma", "pipeline": "limma_paired",
+        "design_col": "timepoint", "value_map": {"Pre": "control", "Post": "case"},
+        "subject_col": "subject_id",
+        "ref_level": "control", "test_level": "case",
+    },
+    "Continuous_limma_multi_group": {
+        "family": "limma", "pipeline": "limma_multi_group",
+        "design_col": "subtype", "value_map": None,
+        "ref_level": "Subtype_A",
+    },
+    "Continuous_limma_time_course": {
+        "family": "limma", "pipeline": "limma_time_course",
+        "time_col": "time_hr", "baseline_time": "0",
+    },
+    "Continuous_limma_group_time_interaction": {
+        "family": "limma", "pipeline": "limma_group_time_interaction",
+        "design_col": "group", "value_map": {"Placebo": "control", "Drug": "case"},
+        "time_col": "time_week",
+        "ref_level": "control", "test_level": "case",
+    },
+}
+
+# Synced with raw_preview.py FEATURE_PREFIXES — must be 6 entries.
+FEATURE_PREFIXES = ("gene_", "asv_", "prot_", "metab_", "otu_", "feat_")
+
+DESIGN_PATTERNS = _re.compile(
+    r"(?i)(group|arm|cohort|status|condition|treatment|trtmt|drug|response|stage|"
+    r"subtype|class|phenotype|disease|case|outcome|label)"
+)
+TIME_PATTERNS = _re.compile(
+    r"(?i)(^time|time$|_time_|day|week|hour|hr|visit|tp_|baseline|collection_day|"
+    r"day_post|days_post|month|year|period)"
+)
+BATCH_PATTERNS = _re.compile(
+    r"(?i)(batch|site|run|plate|center|lane|lab|ms_batch|seq_run|library)"
+)
+SUBJECT_PATTERNS = _re.compile(
+    r"(?i)(subject|patient|participant|donor|individual|case_id)"
+)
+CONTROL_VALUE_KEYWORDS = (
+    "control", "ctrl", "healthy", "normal", "wt", "wildtype", "wild_type",
+    "placebo", "vehicle", "untreated", "baseline", "pre", "non", "no_",
+    "negative", "neg",
+)
+
+
+# ─────────────────────────────────────────────────────────────────────
+# Resolution helpers (Rule A / Rule B)
+# ─────────────────────────────────────────────────────────────────────
+def _resolve_with_schema(
+    meta_cols: list[str],
+    schema_value: str,
+    canonical: str | None,
+    pattern: _re.Pattern,
+    exclude=None,
+    field_name: str = "column",
+) -> str | None:
+    """Rule A — schema-first column name resolution.
+
+    Priority:
+      1. schema_value non-empty:
+           - if it matches a column in meta_cols (case-insensitive) → return that column
+           - else → raise ValueError (don't silently fall back)
+      2. schema_value empty:
+           - try canonical (case-insensitive equality)
+           - try pattern.search()
+           - return None on miss (caller decides if that's fatal)
+    """
+    exclude = set(exclude or ())
+    cols = [c for c in meta_cols if c not in exclude]
+    if schema_value:
+        # Strict mode: user/LLM said this column should exist.
+        for c in cols:
+            if c.lower() == schema_value.lower():
+                return c
+        raise ValueError(
+            f"Schema specified {field_name}={schema_value!r}, but no such column in TSV. "
+            f"Available metadata columns: {cols}"
+        )
+    if canonical:
+        for c in cols:
+            if c.lower() == canonical.lower():
+                return c
+    for c in cols:
+        if pattern.search(c):
+            return c
+    return None
+
+
+def _infer_value_map(values, label):
+    """For 2-level design, decide which value is 'control' vs 'case' via keywords."""
+    vals = [str(v) for v in pd.unique(pd.Series(values).dropna())]
+    if len(vals) != 2:
+        return None
+
+    def is_control(v):
+        vl = v.lower()
+        return any(kw in vl for kw in CONTROL_VALUE_KEYWORDS)
+
+    ctrl = next((v for v in vals if is_control(v)), None)
+    if ctrl is None:
+        ctrl = sorted(vals)[0]
+    case = [v for v in vals if v != ctrl][0]
+    return {ctrl: "control", case: "case"}
+
+
+def _resolve_ref_level(
+    schema_ref: str,
+    group_levels: list[str],
+    label: str,
+) -> str:
+    """Rule B — fallback chain for the reference (control) level.
+
+    Priority:
+      1. schema_ref non-empty + in group_levels → use it
+         (non-empty + not in levels → raise)
+      2. _infer_value_map() heuristic for 2-group
+      3. LABEL_SPEC[label].ref_level if present in group_levels
+      4. sorted(group_levels)[0]
+      5. raise if no levels at all
+    """
+    if schema_ref:
+        if schema_ref in group_levels:
+            return schema_ref
+        raise ValueError(
+            f"Schema reference_level={schema_ref!r} not in observed levels {group_levels}"
+        )
+
+    if len(group_levels) == 2:
+        vm = _infer_value_map(group_levels, label)
+        if vm:
+            ctrl = next((k for k, v in vm.items() if v == "control"), None)
+            if ctrl:
+                return ctrl
+
+    spec_ref = LABEL_SPEC.get(label, {}).get("ref_level")
+    if spec_ref and spec_ref in group_levels:
+        return spec_ref
+
+    if group_levels:
+        return sorted(group_levels)[0]
+
+    raise ValueError(f"No group levels found in TSV for label {label}")
+
+
+def split_meta_feat(df: pd.DataFrame):
+    """Return (meta_df, feat_df). Feat cols detected by FEATURE_PREFIXES."""
+    feat_cols = [c for c in df.columns if c.startswith(FEATURE_PREFIXES)]
+    meta_cols = [c for c in df.columns if c not in feat_cols]
+    return df[meta_cols], df[feat_cols]
+
+
+def _detect_feature_prefix(feat_cols: list[str]) -> str | None:
+    """Return the dominant prefix among feat_cols (or None if empty)."""
+    if not feat_cols:
+        return None
+    counts = {p: sum(c.startswith(p) for c in feat_cols) for p in FEATURE_PREFIXES}
+    return max(counts, key=lambda p: counts[p]) if any(counts.values()) else None
+
+
+# ─────────────────────────────────────────────────────────────────────
+# Main entry point
+# ─────────────────────��───────────────────────────────────────────────
+def adapt(
+    tsv_path: Path,
+    label: str,
+    outdir: Path,
+    schema: dict | None = None,
+):
+    spec = LABEL_SPEC[label]
+    outdir.mkdir(parents=True, exist_ok=True)
+
+    df = pd.read_csv(tsv_path, sep="\t")
+    meta, feat = split_meta_feat(df)
+
+    if "sample_id" not in meta.columns:
+        raise ValueError(f"{tsv_path.name} missing sample_id column")
+
+    # ───── Early validation: feature columns ─────
+    if feat.shape[1] == 0:
+        cols_preview = list(df.columns)[:10]
+        more = "..." if len(df.columns) > 10 else ""
+        raise ValueError(
+            f"No feature columns found. Expected one of these prefixes: "
+            f"{FEATURE_PREFIXES}. Got columns: {cols_preview}{more}"
+        )
+
+    feature_prefix_used = _detect_feature_prefix(list(feat.columns))
+
+    # ───── Resolve column names (Rule A) ─────
+    meta_out = meta.set_index("sample_id").copy()
+    meta_cols_avail = list(meta_out.columns)
+
+    s = schema or {}
+    resolved_design = None
+    resolved_time = None
+    resolved_subject = None
+    resolved_batch = None
+    resolved_ref = None
+
+    if "design_col" in spec:
+        resolved_design = _resolve_with_schema(
+            meta_cols_avail,
+            schema_value=s.get("primary_group_variable", ""),
+            canonical=spec.get("design_col"),
+            pattern=DESIGN_PATTERNS,
+            exclude={"sample_id", "subject_id"},
+            field_name="primary_group_variable",
+        )
+        if resolved_design is None:
+            raise ValueError(
+                f"Label {label!r} requires a design (group/condition) column "
+                f"but none found. Available metadata cols: {meta_cols_avail}"
+            )
+        if resolved_design != "condition":
+            if "condition" in meta_out.columns:
+                meta_out = meta_out.drop(columns=["condition"])
+            meta_out = meta_out.rename(columns={resolved_design: "condition"})
+
+        # Resolve ref_level (Rule B) and remap values to control/case as needed.
+        # NOTE: _resolve_ref_level raises if schema.reference_level is non-empty
+        #       but not in observed levels — this raise must propagate so the
+        #       caller sees the strict-match violation, per plan Rule A.
+        if "condition" in meta_out.columns:
+            unique_user = [str(v) for v in pd.unique(meta_out["condition"])]
+            resolved_ref = _resolve_ref_level(
+                s.get("reference_level", ""),
+                unique_user,
+                label,
+            )
+            # Build value_map: pin resolved_ref → "control"
+            vm = None
+            if len(unique_user) == 2 and resolved_ref:
+                case_val = next((v for v in unique_user if v != resolved_ref), None)
+                if case_val:
+                    vm = {resolved_ref: "control", case_val: "case"}
+            if vm:
+                meta_out["condition"] = (
+                    meta_out["condition"].map(vm).fillna(meta_out["condition"])
+                )
+
+    if "time_col" in spec:
+        resolved_time = _resolve_with_schema(
+            meta_cols_avail,
+            schema_value=s.get("time_variable_column", ""),
+            canonical=spec.get("time_col"),
+            pattern=TIME_PATTERNS,
+            exclude={"sample_id", "subject_id", resolved_design},
+            field_name="time_variable_column",
+        )
+        if resolved_time is None:
+            raise ValueError(
+                f"Label {label!r} requires a time column but none found. "
+                f"Available metadata cols: {meta_cols_avail}"
+            )
+        if resolved_time != "time":
+            meta_out = meta_out.rename(columns={resolved_time: "time"})
+
+    if "subject_col" in spec:
+        resolved_subject = _resolve_with_schema(
+            meta_cols_avail,
+            schema_value=s.get("subject_id_column", ""),
+            canonical=spec.get("subject_col"),
+            pattern=SUBJECT_PATTERNS,
+            exclude={"sample_id"},
+            field_name="subject_id_column",
+        )
+        if resolved_subject is None:
+            raise ValueError(
+                f"Label {label!r} requires a subject id column but none found. "
+                f"Available metadata cols: {meta_cols_avail}"
+            )
+        if resolved_subject != "subject":
+            meta_out = meta_out.rename(columns={resolved_subject: "subject"})
+
+    if "batch_col" in spec:
+        # batch_variables is a list — take the first non-empty
+        bv_list = s.get("batch_variables") or []
+        bv_first = bv_list[0] if bv_list else ""
+        resolved_batch = _resolve_with_schema(
+            meta_cols_avail,
+            schema_value=bv_first,
+            canonical=spec.get("batch_col"),
+            pattern=BATCH_PATTERNS,
+            exclude={"sample_id", "subject_id", resolved_design, resolved_time},
+            field_name="batch_variables[0]",
+        )
+        if resolved_batch is None:
+            raise ValueError(
+                f"Label {label!r} requires a batch column but none found. "
+                f"Available metadata cols: {meta_cols_avail}"
+            )
+        if resolved_batch != "batch":
+            meta_out = meta_out.rename(columns={resolved_batch: "batch"})
+
+    # Pattern A fix: time-course pipelines' PCA script demands a 'condition'
+    # column for coloring, but time-course data is single-cohort. Inject a
+    # dummy condition so PCA can render (single-color scatter).
+    if "condition" not in meta_out.columns and "time_col" in spec:
+        meta_out["condition"] = "cohort"
+
+    meta_out.to_csv(outdir / "metadata.csv")
+
+    # ───── 2. counts.csv / expression.csv ─────
+    feat_t = feat.copy()
+    feat_t.index = pd.Index(meta["sample_id"].tolist())
+    feat_t = feat_t.T
+    feat_t.index.name = "feature_id"
+
+    if spec["family"] == "deseq2":
+        feat_t = feat_t.astype(int).clip(lower=0)
+        feat_t.to_csv(outdir / "counts.csv")
+        data_filename = "counts.csv"
+    else:
+        feat_t.to_csv(outdir / "expression.csv")
+        data_filename = "expression.csv"
+
+    # ───── 3. extras for run_pipeline.sh ─────
+    extras = []
+    if "batch_col" in spec:
+        extras.append("batch")
+    if "subject_col" in spec:
+        extras.append("subject")
+    if "time_col" in spec:
+        extras.append("time")
+    if "baseline_time" in spec:
+        extras.append(str(spec["baseline_time"]))
+    if spec.get("ref_level") and spec["pipeline"] in (
+        "deseq2_multi_group", "limma_multi_group"
+    ):
+        # multi_group takes ref_level as positional. Use resolved_ref if available.
+        ref_for_pipeline = resolved_ref or spec["ref_level"]
+        extras.append(ref_for_pipeline)
+
+    manifest = {
+        "label": label,
+        "pipeline": spec["pipeline"],
+        "family": spec["family"],
+        "data_file": data_filename,
+        "extras_positional": extras,
+        "n_samples": len(meta),
+        "n_features": feat.shape[1],
+        # Debug fields (Codex review v0.1.7):
+        "resolved_design_col": resolved_design,
+        "resolved_time_col": resolved_time,
+        "resolved_subject_col": resolved_subject,
+        "resolved_batch_col": resolved_batch,
+        "resolved_ref_level": resolved_ref,
+        "feature_prefix_used": feature_prefix_used,
+        "schema_used": schema is not None,
+    }
+    (outdir / "adapt_manifest.json").write_text(json.dumps(manifest, indent=2))
+    return manifest
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--tsv", required=True)
+    ap.add_argument("--label", required=True)
+    ap.add_argument("--outdir", required=True)
+    ap.add_argument("--schema", default=None,
+                    help="optional JSON file with SchemaSummary")
+    args = ap.parse_args()
+
+    schema = None
+    if args.schema:
+        schema = json.loads(Path(args.schema).read_text())
+
+    manifest = adapt(Path(args.tsv), args.label, Path(args.outdir), schema=schema)
+    print(json.dumps(manifest, indent=2))
+
+
+if __name__ == "__main__":
+    main()

src/statlens/classifier.py

@@ -0,0 +1,176 @@
+"""
+classifier.py — two LLM calls for the 0.1.6 two-stage flow.
+
+  extract_schema(study_context, preview)       → SchemaSummary (no label)
+  pick_label   (schema, study_context)         → (label, reasoning)
+
+The orchestrator (statlens_run.py) drives both. Each call is independent —
+the user can edit the schema between them, and the label decision uses the
+edited schema.
+"""
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import dataclass, field
+
+import httpx
+
+from .prompts import (
+    LABELS,
+    build_extract_messages,
+    build_label_messages,
+)
+from .schema_spec import coerce_and_validate, default_schema
+
+
+@dataclass
+class ExtractResult:
+    """Output of stage 1: just the schema, no label."""
+    schema: dict
+    raw: str
+    schema_warnings: list[str] = field(default_factory=list)
+
+
+@dataclass
+class LabelResult:
+    """Output of stage 3: a label picked from a (confirmed) schema."""
+    label: str
+    reasoning: str
+    raw: str
+    valid: bool   # whether label is in LABELS
+
+
+def _extract_json_object(text: str) -> dict | None:
+    """Find the largest balanced JSON object in a blob of text."""
+    try:
+        return json.loads(text.strip())
+    except Exception:
+        pass
+    starts = [i for i, c in enumerate(text) if c == "{"]
+    for s in starts:
+        depth = 0
+        for i in range(s, len(text)):
+            if text[i] == "{":
+                depth += 1
+            elif text[i] == "}":
+                depth -= 1
+                if depth == 0:
+                    cand = text[s:i+1]
+                    try:
+                        return json.loads(cand)
+                    except Exception:
+                        break
+    matches = re.findall(r"\{[^{}]*\}", text, re.DOTALL)
+    for m in sorted(matches, key=len, reverse=True):
+        try:
+            return json.loads(m)
+        except Exception:
+            continue
+    return None
+
+
+def _post_chat(endpoint: str, messages: list[dict], model: str,
+               api_key: str, timeout: float, max_tokens: int) -> str:
+    url = endpoint.rstrip("/") + "/chat/completions"
+    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+    body = {"model": model, "messages": messages,
+            "temperature": 0.0, "max_tokens": max_tokens}
+    with httpx.Client(timeout=timeout) as client:
+        r = client.post(url, json=body, headers=headers)
+        r.raise_for_status()
+        data = r.json()
+    return data["choices"][0]["message"]["content"]
+
+
+# ─────────────────────────────────────────────────────────────────────
+# Stage 1 — extract schema only
+# ─────────────────────────────────────────────────────────────────────
+def extract_schema(
+    study_context: str,
+    preview: str,
+    endpoint: str,
+    model: str = "statlens",
+    api_key: str = "dummy",
+    timeout: float = 120.0,
+) -> ExtractResult:
+    """Call LLM, parse the structured SchemaSummary. No label here."""
+    messages = build_extract_messages(study_context, preview)
+    raw = _post_chat(endpoint, messages, model, api_key, timeout, max_tokens=1200)
+
+    obj = _extract_json_object(raw)
+    if obj is None:
+        return ExtractResult(
+            schema=default_schema(), raw=raw,
+            schema_warnings=["LLM output was not valid JSON; using defaults"],
+        )
+
+    # Some models nest the schema under a "schema" key — accept either.
+    if isinstance(obj.get("schema"), dict):
+        obj = obj["schema"]
+
+    schema, warns = coerce_and_validate(obj)
+    return ExtractResult(schema=schema, raw=raw, schema_warnings=warns)
+
+
+# ─────────────────────────────────────────────────────────────────────
+# Stage 3 — pick label given a (confirmed) schema
+# ─────────────────────────────────────────────────────────────────────
+def pick_label(
+    schema: dict,
+    study_context: str,
+    endpoint: str,
+    model: str = "statlens",
+    api_key: str = "dummy",
+    timeout: float = 60.0,
+) -> LabelResult:
+    """Call LLM with the schema rendered as natural-language bullets."""
+    messages = build_label_messages(study_context, schema)
+    raw = _post_chat(endpoint, messages, model, api_key, timeout, max_tokens=400)
+
+    obj = _extract_json_object(raw)
+    if obj is None:
+        return LabelResult(
+            label="none_of_these",
+            reasoning="(failed to parse JSON from model)",
+            raw=raw, valid=False,
+        )
+
+    label = str(obj.get("label", "")).strip()
+    reasoning = str(obj.get("reasoning", "")).strip()
+    valid = label in LABELS
+    if not valid:
+        # Normalize case-insensitive matches before giving up.
+        for cand in LABELS:
+            if cand.lower() == label.lower():
+                label = cand
+                valid = True
+                break
+    return LabelResult(label=label, reasoning=reasoning, raw=raw, valid=valid)
+
+
+if __name__ == "__main__":
+    import argparse
+    from pathlib import Path
+    from .raw_preview import build_raw_preview
+
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--context", required=True)
+    ap.add_argument("--tsv", required=True)
+    ap.add_argument("--endpoint", required=True)
+    ap.add_argument("--model", default="statlens")
+    ap.add_argument("--api-key", default="dummy")
+    args = ap.parse_args()
+
+    ctx = Path(args.context).read_text()
+    preview = build_raw_preview(Path(args.tsv))
+
+    print("=== Stage 1: extract schema ===")
+    er = extract_schema(ctx, preview, args.endpoint, args.model, args.api_key)
+    print(f"  warnings : {er.schema_warnings}")
+    print(json.dumps(er.schema, indent=2))
+
+    print("\n=== Stage 3: pick label (using LLM-extracted schema verbatim) ===")
+    lr = pick_label(er.schema, ctx, args.endpoint, args.model, args.api_key)
+    print(f"  label    : {lr.label}  (valid={lr.valid})")
+    print(f"  reasoning: {lr.reasoning}")

src/statlens/cli.py

@@ -0,0 +1,212 @@
+"""
+statlens — command-line entry point.
+
+Subcommands:
+    statlens serve     spin up local LLM backend + FastAPI; open browser
+    statlens download  fetch the LoRA adapter into the HF cache
+                       (NOT the 64 GB base — that's the user's job)
+    statlens info      show GPU / cache state / paths
+    statlens classify  one-shot CLI run (TSV + context.txt → result folder)
+"""
+from __future__ import annotations
+
+import os
+import signal
+import sys
+import webbrowser
+
+import click
+
+from . import (
+    __version__,
+    DEFAULT_BASE_MODEL_REF,
+    DEFAULT_LORA_REPO,
+    DEFAULT_LORA_SUBFOLDER,
+    DEFAULT_QUANTIZATION,
+    LOCAL_LLM_PORT,
+    LOCAL_WEB_PORT,
+)
+from . import runtime
+
+
+def _common_serve_opts(f):
+    """Decorator that attaches the model/LoRA/quantization options shared by
+    `serve` and `classify`.
+
+    The LLM backend is LLaMA-Factory (`llamafactory-cli api`); options here
+    map directly to its YAML config.
+    """
+    f = click.option("--quantization", default=DEFAULT_QUANTIZATION,
+                     type=click.Choice(["bitsandbytes", "awq", "gptq", "none"]),
+                     show_default=True,
+                     help="LLM weight-quantization mode (passed to LLaMA-Factory)")(f)
+    f = click.option("--lora-path", default=None, type=click.Path(),
+                     help="local path to the LoRA folder; "
+                          "if omitted, downloaded from HF")(f)
+    f = click.option("--base-model", default=None, type=click.Path(),
+                     help="local path to the BF16 base model directory; "
+                          "if omitted, auto-detect from common paths "
+                          "(~/models/qwen3-32b, /root/autodl-tmp/..., HF cache)")(f)
+    return f
+
+
+@click.group(context_settings={"help_option_names": ["-h", "--help"]})
+@click.version_option(__version__, prog_name="statlens")
+def main():
+    """statLens — DEA method selector. See `statlens serve --help`."""
+
+
+# ─────────────────────────────── serve ───────────────────────────────
+@main.command()
+@_common_serve_opts
+@click.option("--web-port",   default=LOCAL_WEB_PORT, show_default=True, type=int)
+@click.option("--llm-port",   default=LOCAL_LLM_PORT, show_default=True, type=int)
+@click.option("--no-browser", is_flag=True, help="don't auto-open browser")
+@click.option("--no-gpu-check", is_flag=True, help="skip GPU sanity check")
+def serve(base_model, lora_path, quantization,
+          web_port, llm_port, no_browser, no_gpu_check):
+    """Start the LLM backend (LLaMA-Factory) + the web app on http://localhost:7860 ."""
+    if not no_gpu_check:
+        runtime.check_gpu()
+
+    base_path = runtime.resolve_base_model(base_model)
+    lora_path = runtime.ensure_lora_cached(lora_path)
+
+    llm_proc = runtime.start_server(
+        base_path, lora_path,
+        port=llm_port,
+        quantization=quantization,
+    )
+
+    def shutdown(signum=None, frame=None):
+        runtime.stop_server(llm_proc)
+        sys.exit(0)
+    signal.signal(signal.SIGINT, shutdown)
+    signal.signal(signal.SIGTERM, shutdown)
+
+    try:
+        # show animated progress; abort if subprocess dies
+        runtime.wait_for_server(port=llm_port, proc=llm_proc)
+    except (TimeoutError, RuntimeError) as e:
+        click.secho(f"\n[statlens] {e}", fg="red")
+        runtime.stop_server(llm_proc)
+        sys.exit(2)
+
+    os.environ["STATLENS_LLM_ENDPOINT"] = f"http://127.0.0.1:{llm_port}/v1"
+    # LLaMA-Factory's API exposes the model under the name "gpt-3.5-turbo".
+    os.environ["STATLENS_LLM_MODEL"] = "gpt-3.5-turbo"
+
+    web_url = f"http://localhost:{web_port}/"
+
+    # Prominent ready banner — only what end users care about.
+    click.echo("")
+    click.secho("══════════════════════════════════════════════════════", fg="green")
+    click.secho(f"  ✅ statLens ready", fg="green", bold=True)
+    click.secho(f"     open in browser:  {web_url}", fg="green")
+    click.secho(f"     Ctrl+C to stop.", fg="green")
+    click.secho("══════════════════════════════════════════════════════", fg="green")
+    click.echo("")
+
+    if not no_browser:
+        import threading
+        threading.Timer(1.0, lambda: webbrowser.open(web_url)).start()
+
+    import uvicorn
+    from . import server as server_mod
+    try:
+        # Quiet uvicorn so it doesn't drown the ready banner; users see HTTP
+        # requests via the LLM log if they want.
+        uvicorn.run(server_mod.app, host="127.0.0.1", port=web_port, log_level="warning")
+    finally:
+        runtime.stop_server(llm_proc)
+
+
+# ─────────────────────────── download ────────���──────────────────
+@main.command()
+@click.option("--lora-repo",      default=DEFAULT_LORA_REPO,      show_default=True)
+@click.option("--lora-subfolder", default=DEFAULT_LORA_SUBFOLDER, show_default=True)
+def download(lora_repo, lora_subfolder):
+    """Pre-fetch the LoRA adapter (~1 GB) into the HF cache.
+
+    statLens does NOT auto-download the 64 GB base model. Get it yourself with:
+
+        huggingface-cli download Qwen/Qwen3-32B --local-dir ~/models/qwen3-32b
+    """
+    p = runtime.ensure_lora_cached(None, lora_repo, lora_subfolder)
+    click.secho(f"\n✅ LoRA cached at {p}", fg="green")
+    click.echo(
+        "\nReminder: download the BF16 base model separately, e.g.\n"
+        "    huggingface-cli download Qwen/Qwen3-32B --local-dir ~/models/qwen3-32b"
+    )
+
+
+# ─────────────────────────── info ───────────────────────────
+@main.command()
+def info():
+    """Show GPU, cache, and default endpoint information."""
+    click.echo(f"statlens version: {__version__}\n")
+
+    try:
+        import torch
+        if torch.cuda.is_available():
+            for i in range(torch.cuda.device_count()):
+                p = torch.cuda.get_device_properties(i)
+                click.echo(f"  GPU {i}: {p.name} · {p.total_memory/1024**3:.1f} GB")
+        else:
+            click.echo("  GPU: (none)")
+    except Exception as e:
+        click.echo(f"  GPU: (torch not importable: {e})")
+
+    click.echo("")
+    click.echo(f"  base model (reference) : {DEFAULT_BASE_MODEL_REF}  (NOT auto-downloaded)")
+    click.echo(f"  base model (resolved)  : "
+               f"{os.environ.get('STATLENS_BASE_MODEL', '(unset — pass --base-model)')}")
+    click.echo(f"  default quantization   : {DEFAULT_QUANTIZATION}")
+    click.echo(f"  lora repo              : {DEFAULT_LORA_REPO} (subfolder {DEFAULT_LORA_SUBFOLDER})")
+    click.echo("")
+
+    from huggingface_hub.constants import HF_HUB_CACHE
+    click.echo(f"  HF cache root : {HF_HUB_CACHE}")
+    d = runtime.cache_dir_for(DEFAULT_LORA_REPO)
+    if d.exists():
+        sz = sum(p.stat().st_size for p in d.rglob("*") if p.is_file())
+        click.echo(f"    · {DEFAULT_LORA_REPO} ✓ ({sz/1024**3:.2f} GB at {d})")
+    else:
+        click.echo(f"    · {DEFAULT_LORA_REPO} ✗ (run `statlens download` to fetch)")
+
+
+# ─────────────────────────── classify ───────────────────────────
+@main.command()
+@_common_serve_opts
+@click.option("--tsv",      required=True, type=click.Path(exists=True, dir_okay=False))
+@click.option("--context",  required=True, type=click.Path(exists=True, dir_okay=False))
+@click.option("--out",      required=True, type=click.Path(file_okay=False))
+@click.option("--endpoint", default=None,
+              help="OpenAI-compatible endpoint (default: spin up a temporary local LLM backend)")
+@click.option("--model",    default="gpt-3.5-turbo", show_default=True)
+@click.option("--keep-llm", is_flag=True, help="don't shut down the LLM backend after classifying")
+def classify(base_model, lora_path, quantization,
+             tsv, context, out, endpoint, model, keep_llm):
+    """One-shot CLI: classify a single TSV+context, run pipeline, exit."""
+    from pathlib import Path
+    from .statlens_run import run_one
+    if endpoint is None:
+        runtime.check_gpu()
+        base_path = runtime.resolve_base_model(base_model)
+        lora_resolved = runtime.ensure_lora_cached(lora_path)
+        proc = runtime.start_server(base_path, lora_resolved,
+                                  quantization=quantization)
+        try:
+            runtime.wait_for_server()
+            endpoint = f"http://127.0.0.1:{LOCAL_LLM_PORT}/v1"
+            run_one(Path(tsv), Path(context), Path(out), endpoint, model)
+        finally:
+            if not keep_llm:
+                runtime.stop_server(proc)
+    else:
+        from pathlib import Path
+        run_one(Path(tsv), Path(context), Path(out), endpoint, model)
+
+
+if __name__ == "__main__":
+    main()

src/statlens/data/examples/07_Count_DESeq2_ZINB_synthetic.tsv

@@ -0,0 +1,41 @@
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src/statlens/data/examples/08_Continuous_limma_basic_synthetic.tsv

@@ -0,0 +1,25 @@
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src/statlens/data/examples/09_Continuous_limma_with_batch_synthetic.tsv

@@ -0,0 +1,49 @@
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src/statlens/data/examples/10_Continuous_limma_paired_or_repeated_synthetic.tsv

@@ -0,0 +1,41 @@
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src/statlens/data/examples/11_Continuous_limma_multi_group_synthetic.tsv

@@ -0,0 +1,41 @@
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src/statlens/data/index.html

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+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>statLens — DEA Method Selector</title>
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+      content="Upload a TSV and a study description; statLens picks the right differential expression analysis method and runs the full pipeline."
+    />
+    <link
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+        border: 2px dashed #d1d5db;
+        border-radius: 14px;
+        padding: 28px;
+        text-align: center;
+        background: #fafafa;
+        transition: border-color 0.15s, background 0.15s;
+        cursor: pointer;
+      }
+      .dropzone:hover, .dropzone.drag {
+        border-color: var(--brand-green);
+        background: #f0fdf4;
+      }
+      .dropzone.has-file {
+        border-style: solid;
+        border-color: var(--brand-green);
+        background: #f0fdf4;
+      }
+
+      /* loading spinner */
+      .spinner {
+        width: 40px; height: 40px;
+        border: 3px solid #e5e7eb;
+        border-top-color: var(--brand-green);
+        border-radius: 50%;
+        animation: spin 0.8s linear infinite;
+      }
+      @keyframes spin { to { transform: rotate(360deg); } }
+
+      .step-line { display: flex; align-items: center; gap: 10px; padding: 6px 0; font-size: 13px; color: #6B7280; }
+      .step-line.active { color: #14532d; font-weight: 500; }
+      .step-line.done { color: #16a34a; }
+      .step-icon {
+        width: 18px; height: 18px; border-radius: 50%;
+        display: inline-grid; place-items: center;
+        background: #e5e7eb; color: #9ca3af;
+        font-size: 11px;
+      }
+      .step-line.active .step-icon { background: var(--brand-green); color: white; }
+      .step-line.done .step-icon { background: var(--brand-green); color: white; }
+
+      /* image grid lightbox style */
+      .img-thumb {
+        cursor: zoom-in;
+        transition: transform 0.15s, box-shadow 0.15s;
+        border: 1px solid rgba(0,0,0,0.06);
+      }
+      .img-thumb:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 8px 24px rgba(0,0,0,0.12);
+      }
+
+      /* table */
+      .preview-table {
+        font-family: 'JetBrains Mono', monospace;
+        font-size: 11.5px;
+        border-collapse: collapse;
+        width: 100%;
+      }
+      .preview-table th, .preview-table td {
+        padding: 6px 10px;
+        border-bottom: 1px solid #f1f5f9;
+        text-align: left;
+        white-space: nowrap;
+      }
+      .preview-table th {
+        background: #f8fafc;
+        font-weight: 600;
+        color: #475569;
+        position: sticky; top: 0;
+      }
+      .preview-table tr:hover td { background: #f0fdf4; }
+
+      /* label badge huge */
+      .label-badge {
+        display: inline-flex; align-items: center; gap: 8px;
+        background: linear-gradient(90deg, #16a34a, #22c55e);
+        color: white; font-weight: 600; font-size: 14px;
+        padding: 8px 16px; border-radius: 999px;
+        box-shadow: 0 4px 14px rgba(22,163,74,0.35);
+      }
+      .label-badge.decline {
+        background: linear-gradient(90deg, #dc2626, #ef4444);
+        box-shadow: 0 4px 14px rgba(220,38,38,0.35);
+      }
+
+      /* lightbox modal */
+      #lightbox {
+        position: fixed; inset: 0;
+        background: rgba(0,0,0,0.85);
+        display: none; align-items: center; justify-content: center;
+        z-index: 100; cursor: zoom-out;
+      }
+      #lightbox.open { display: flex; }
+      #lightbox img { max-width: 92vw; max-height: 92vh; box-shadow: 0 0 60px rgba(0,0,0,0.5); }
+    </style>
+  </head>
+
+  <body class="font-sans text-slate-800 bg-white">
+    <!-- ─────────────────────────  HERO  ───────────────────────── -->
+    <header class="hero-grad relative overflow-hidden">
+      <nav class="relative max-w-6xl mx-auto px-6 py-3 flex items-center justify-between">
+        <div class="flex items-center gap-3">
+          <span class="inline-grid place-items-center w-9 h-9 rounded-full overflow-hidden ring-1 ring-slate-200 bg-white">
+            <span class="text-xs font-semibold text-[var(--brand-green)]">stat</span>
+          </span>
+          <div>
+            <p class="font-semibold tracking-tight text-slate-900">statLens</p>
+            <p class="text-xs text-slate-500">DEA method selector for biomedical / omics studies</p>
+          </div>
+        </div>
+        <a href="#run" class="inline-flex items-center gap-2 px-3 py-1.5 rounded-full bg-white/80 border border-slate-200 text-xs hover:bg-white transition">
+          <span class="material-symbols-outlined !text-base text-[var(--brand-green)]">arrow_downward</span>
+          Get started
+        </a>
+      </nav>
+
+      <section class="relative max-w-6xl mx-auto px-6 pb-12 pt-6 grid md:grid-cols-[minmax(0,1.5fr),minmax(0,1fr)] gap-10 items-center">
+        <div class="space-y-4">
+          <p class="inline-flex items-center gap-2 text-xs font-semibold text-[var(--brand-green)] bg-white/70 px-3 py-1 rounded-full border border-[var(--accent-gold)] shadow-sm">
+            <span class="material-symbols-outlined !text-base">auto_awesome</span>
+            Qwen3-32B + statLens LoRA · 14-way classifier
+          </p>
+          <h1 class="font-serif text-3xl md:text-4xl font-semibold tracking-tight text-[var(--ink)]">
+            One TSV. One question. One answer.
+          </h1>
+          <p class="text-[15px] leading-relaxed text-slate-700 max-w-xl">
+            Upload your wide-format data and a few sentences of study context.
+            statLens picks one of 13 differential-expression methods (or
+            declines if your study sits outside its training space) and runs
+            the full pipeline — DESeq2 / limma + volcano · MA · PCA · heatmap
+            · top genes — in about 25 seconds.
+          </p>
+          <ul class="text-sm text-slate-700 space-y-1">
+            <li class="flex items-start gap-2">
+              <span class="material-symbols-outlined !text-base text-[var(--brand-green)] mt-[1px]">check_circle</span>
+              <span><span class="font-semibold">Schema-aware ·</span> reads the entire TSV (not the first 50 rows) before classifying.</span>
+            </li>
+            <li class="flex items-start gap-2">
+              <span class="material-symbols-outlined !text-base text-[var(--brand-green)] mt-[1px]">check_circle</span>
+              <span><span class="font-semibold">14-way decision ·</span> 13 DEA scenarios + a “none of these” kill-switch — no forced fits.</span>
+            </li>
+            <li class="flex items-start gap-2">
+              <span class="material-symbols-outlined !text-base text-[var(--brand-green)] mt-[1px]">check_circle</span>
+              <span><span class="font-semibold">Reproducible ·</span> each run is a self-contained folder you can download.</span>
+            </li>
+          </ul>
+          <div class="pt-3">
+            <a href="#run" class="inline-flex items-center gap-2 px-4 py-2 rounded-full bg-[var(--brand-green)] text-white text-sm shadow-subtle hover:bg-emerald-800/90 transition">
+              <span class="material-symbols-outlined !text-base">play_arrow</span>
+              <span>Run on your data ���</span>
+            </a>
+          </div>
+        </div>
+
+        <div class="hidden md:flex items-center justify-end">
+          <div class="glass border border-slate-200/80 rounded-3xl shadow-card p-4 w-full max-w-xs">
+            <div class="flex items-center justify-between mb-3">
+              <p class="text-xs font-semibold tracking-wide text-slate-600 uppercase">14 candidate labels</p>
+              <span class="inline-flex items-center gap-1 px-2 py-0.5 rounded-full bg-[var(--brand-green)]/10 text-[10px] text-[var(--brand-green)]">
+                <span class="material-symbols-outlined !text-sm">verified</span>
+                trained
+              </span>
+            </div>
+            <div class="border border-[rgba(0,0,0,0.05)] rounded-2xl overflow-hidden bg-white/90">
+              <div class="px-3 py-2 bg-[#16a34a] text-white text-[11px] font-serif font-medium">DEA scenarios</div>
+              <div class="divide-y divide-slate-100 text-[11px]">
+                <div class="px-3 py-1.5"><span class="text-slate-500">Count · </span>basic / batch / paired / multi-group / time / interaction / ZINB</div>
+                <div class="px-3 py-1.5"><span class="text-slate-500">Continuous · </span>basic / batch / paired / multi-group / time / interaction</div>
+                <div class="px-3 py-1.5 bg-gradient-to-b from-white to-slate-50 text-slate-500 italic">
+                  + <span class="font-semibold not-italic text-rose-600">none_of_these</span> — kill-switch when nothing fits.
+                </div>
+              </div>
+            </div>
+          </div>
+        </div>
+      </section>
+    </header>
+
+    <!-- ─────────────────────────  RUN FORM  ───────────────────────── -->
+    <main id="run" class="max-w-6xl mx-auto w-full px-6 py-10 space-y-6">
+      <section id="formCard" class="bg-white border border-slate-200 rounded-2xl shadow-subtle p-6 md:p-8">
+        <div class="flex items-center gap-3 mb-5">
+          <span class="inline-grid place-items-center w-9 h-9 rounded-xl bg-[var(--ivory)]">
+            <span class="material-symbols-outlined !text-lg text-[var(--brand-green)]">upload_file</span>
+          </span>
+          <div>
+            <h2 class="font-serif text-xl font-semibold text-[var(--ink)]">New analysis</h2>
+            <p class="text-xs text-slate-500">Two inputs, one click. Result in ~25 seconds.</p>
+          </div>
+        </div>
+
+        <form id="runForm" class="space-y-5">
+          <!-- file dropzone -->
+          <div>
+            <label class="field-label block mb-2">Wide-format TSV</label>
+            <div id="dropzone" class="dropzone">
+              <input type="file" id="fileInput" accept=".tsv,.txt" hidden />
+              <span class="material-symbols-outlined !text-3xl text-slate-400">cloud_upload</span>
+              <p id="dzLabel" class="mt-1 text-sm text-slate-600">
+                Drag &amp; drop your <span class="font-mono">.tsv</span> here, or click to browse
+              </p>
+              <p class="text-[11px] text-slate-400 mt-1">
+                Expected columns: <span class="font-mono">subject_id</span>, <span class="font-mono">sample_id</span>,
+                a design column, and feature columns prefixed
+                <span class="font-mono">gene_/asv_/prot_/metab_</span>.
+              </p>
+            </div>
+          </div>
+
+          <!-- context textarea -->
+          <div>
+            <label class="field-label block mb-2" for="ctxInput">Study context</label>
+            <textarea id="ctxInput" rows="6" class="block w-full resize-y" placeholder="Free-text description of your study. e.g., 'We collected matched tumor and adjacent-normal biopsies from 20 colorectal cancer patients, RNA-seq, looking for paired tumor-vs-normal differentially expressed genes.'"></textarea>
+          </div>
+
+          <div class="flex items-center justify-between gap-4 pt-2">
+            <p id="formHint" class="text-xs text-slate-500">Both inputs required.</p>
+            <button id="submitBtn" type="submit"
+              class="inline-flex items-center gap-2 px-5 py-2.5 rounded-full bg-[var(--brand-green)] text-white text-sm shadow-subtle hover:bg-emerald-800/90 transition disabled:opacity-50 disabled:cursor-not-allowed">
+              <span class="material-symbols-outlined !text-base">bolt</span>
+              <span>Classify &amp; run</span>
+            </button>
+          </div>
+        </form>
+      </section>
+
+      <!-- ─────────  LOADING  ───────── -->
+      <section id="loadingCard" class="bg-white border border-slate-200 rounded-2xl shadow-subtle p-6 md:p-8 hidden">
+        <div class="flex items-center gap-4">
+          <div class="spinner"></div>
+          <div>
+            <h3 class="font-serif text-lg font-semibold text-[var(--ink)]">Working…</h3>
+            <p class="text-xs text-slate-500">Typical end-to-end ~25 s. Don't refresh the page.</p>
+          </div>
+        </div>
+        <div class="mt-4 grid gap-1">
+          <div class="step-line" data-step="upload"><span class="step-icon">1</span> Upload received</div>
+          <div class="step-line" data-step="schema"><span class="step-icon">2</span> Building schema summary from full TSV</div>
+          <div class="step-line" data-step="classify"><span class="step-icon">3</span> Asking the SFT classifier (14-way)</div>
+          <div class="step-line" data-step="pipeline"><span class="step-icon">4</span> Running matched DEA pipeline (DESeq2 / limma + plots)</div>
+          <div class="step-line" data-step="package"><span class="step-icon">5</span> Packaging result folder</div>
+        </div>
+      </section>
+
+      <!-- ─────────  SCHEMA  ───────── -->
+      <section id="schemaCard" class="bg-white border border-slate-200 rounded-2xl shadow-subtle p-6 md:p-8 hidden">
+        <div class="flex items-center gap-3 mb-4">
+          <span class="inline-grid place-items-center w-9 h-9 rounded-xl bg-[var(--ivory)]">
+            <span class="material-symbols-outlined !text-lg text-[var(--brand-green)]">tune</span>
+          </span>
+          <div class="flex-1">
+            <h2 class="font-serif text-xl font-semibold text-[var(--ink)]">Schema we extracted</h2>
+            <p class="text-xs text-slate-500">Review and edit before running the pipeline. Green = decision-driving fields.</p>
+          </div>
+          <button type="button" data-action="reset"
+            class="reset-btn inline-flex items-center gap-2 px-3 py-1.5 rounded-full bg-white border border-slate-200 text-xs text-slate-700 hover:bg-slate-50 transition">
+            <span class="material-symbols-outlined !text-base">close</span>
+            Cancel
+          </button>
+        </div>
+
+        <div id="schemaFields" class="grid md:grid-cols-2 gap-x-6 gap-y-2"></div>
+
+        <div class="flex items-center justify-end gap-3 mt-5">
+          <button id="runPipelineBtn" type="button"
+            class="inline-flex items-center gap-2 px-5 py-2.5 rounded-full bg-[var(--brand-green)] text-white text-sm shadow-subtle hover:bg-emerald-800/90 transition disabled:opacity-50 disabled:cursor-not-allowed">
+            <span class="material-symbols-outlined !text-base">play_arrow</span>
+            <span>Run pipeline →</span>
+          </button>
+        </div>
+      </section>
+
+      <!-- ─────────  ERROR  ───────── -->
+      <section id="errorCard" class="bg-white border border-rose-200 rounded-2xl shadow-subtle p-6 md:p-8 hidden">
+        <div class="flex items-start gap-3">
+          <span class="material-symbols-outlined !text-2xl text-rose-600 mt-1">error</span>
+          <div class="flex-1">
+            <div class="flex items-start justify-between gap-3 mb-2">
+              <h3 class="font-serif text-lg font-semibold text-rose-700">Run failed</h3>
+              <button type="button" data-action="reset"
+                class="reset-btn inline-flex items-center gap-2 px-3 py-1.5 rounded-full bg-white border border-slate-200 text-xs text-slate-700 hover:bg-slate-50 transition">
+                <span class="material-symbols-outlined !text-base">refresh</span>
+                New analysis
+              </button>
+            </div>
+            <pre id="errorMsg" class="text-xs text-rose-700 bg-rose-50 mt-2 p-3 rounded-md whitespace-pre-wrap font-mono"></pre>
+          </div>
+        </div>
+      </section>
+
+      <!-- ─────────  DECLINE  ───────── -->
+      <section id="declineCard" class="bg-white border border-slate-200 rounded-2xl shadow-subtle p-6 md:p-8 hidden">
+        <div class="flex items-center gap-3 mb-4">
+          <span class="label-badge decline">
+            <span class="material-symbols-outlined !text-base">block</span>
+            none_of_these
+          </span>
+          <p class="text-xs text-slate-500">statLens declined to run a pipeline.</p>
+          <button type="button" data-action="reset"
+            class="reset-btn ml-auto inline-flex items-center gap-2 px-3 py-1.5 rounded-full bg-white border border-slate-200 text-xs text-slate-700 hover:bg-slate-50 transition">
+            <span class="material-symbols-outlined !text-base">refresh</span>
+            New analysis
+          </button>
+        </div>
+        <p class="text-sm text-slate-700 mb-2 font-semibold">Model reasoning</p>
+        <p id="declineReason" class="text-sm text-slate-600 leading-relaxed bg-slate-50 p-3 rounded-md"></p>
+        <p class="text-[12px] text-slate-500 mt-3">
+          The model's 14 options cover only the <em>differential expression</em> scenarios listed in the hero card.
+          For survival, single-sample, network inference or other questions, you'll need a different tool.
+        </p>
+      </section>
+
+      <!-- ─────────  RESULT  ───────── -->
+      <section id="resultCard" class="bg-white border border-slate-200 rounded-2xl shadow-subtle p-6 md:p-8 hidden">
+        <div class="flex flex-wrap items-center gap-3 mb-2">
+          <span class="label-badge">
+            <span class="material-symbols-outlined !text-base">verified</span>
+            <span id="resLabel">label_here</span>
+          </span>
+          <span id="resPipeline" class="text-xs text-slate-500"></span>
+          <span id="resElapsed" class="text-xs text-slate-400"></span>
+          <a id="dlZip" href="#" class="ml-auto inline-flex items-center gap-2 px-3 py-1.5 rounded-full bg-[var(--ivory)] border border-[var(--accent-gold)] text-xs text-[var(--ink)] hover:bg-emerald-100 transition">
+            <span class="material-symbols-outlined !text-base">download</span>
+            Download all
+          </a>
+          <button type="button" data-action="reset"
+            class="reset-btn inline-flex items-center gap-2 px-3 py-1.5 rounded-full bg-white border border-slate-200 text-xs text-slate-700 hover:bg-slate-50 transition">
+            <span class="material-symbols-outlined !text-base">refresh</span>
+            New analysis
+          </button>
+        </div>
+
+        <div class="mt-4">
+          <p class="field-label mb-1">Why this label</p>
+          <p id="resReason" class="text-sm text-slate-700 leading-relaxed bg-slate-50 p-3 rounded-md"></p>
+        </div>
+
+        <div class="mt-6">
+          <p class="field-label mb-2">Plots</p>
+          <div id="imgGrid" class="grid grid-cols-2 md:grid-cols-3 gap-3"></div>
+        </div>
+
+        <div class="mt-6">
+          <p class="field-label mb-2">Top results <span id="tableTotal" class="ml-2 text-slate-400 normal-case font-normal text-[11px]"></span></p>
+          <div class="flex items-center gap-2 mb-2 flex-wrap" id="tableTabs"></div>
+          <div id="tableBox" class="border border-slate-200 rounded-lg overflow-x-auto max-h-[420px]"></div>
+        </div>
+      </section>
+    </main>
+
+    <!-- ─────────────────────────  FOOTER  ───────────────────────── -->
+    <footer class="border-t border-slate-200 mt-10 py-6">
+      <div class="max-w-6xl mx-auto px-6 flex items-center justify-between text-xs text-slate-500">
+        <span>statLens v1 · Qwen3-32B + statLens LoRA → DESeq2/limma pipelines</span>
+        <span class="font-mono">localhost:7860</span>
+      </div>
+    </footer>
+
+    <!-- lightbox -->
+    <div id="lightbox"><img id="lightboxImg" src="" alt="" /></div>
+
+    <!-- ─────────────────────────  JS  ───────────────────────── -->
+    <script>
+      // ── DOM refs ──
+      const $ = (id) => document.getElementById(id);
+      const dz = $('dropzone'), fileInput = $('fileInput'), dzLabel = $('dzLabel');
+      const ctxInput = $('ctxInput'), submitBtn = $('submitBtn'), formHint = $('formHint');
+      const formCard = $('formCard'), loadingCard = $('loadingCard');
+      const resultCard = $('resultCard'), declineCard = $('declineCard'), errorCard = $('errorCard');
+      const schemaCard = $('schemaCard');
+      const lightbox = $('lightbox'), lightboxImg = $('lightboxImg');
+
+      let chosenFile = null;
+      let currentRunId = null;
+      let currentSchema = null;
+
+      // ── SchemaSummary spec (mirror of schema_spec.py) ──
+      const PLATFORMS = ['bulk_RNA-seq','16S','scRNA_pseudobulk','proteomics','metabolomics','microarray','other'];
+      const DATA_TYPES = ['count','continuous'];
+      const SCHEMA_FIELDS = [
+        // group A = decision-driving (highlighted green)
+        { name:'omics_data_type',                          type:'enum',  values:DATA_TYPES, label:'Data type',                 group:'A' },
+        { name:'zero_fraction',                            type:'float', label:'Zero rate',                                    group:'A', readonly:true },
+        { name:'number_of_groups',                         type:'int',   label:'# of groups',                                  group:'A' },
+        { name:'paired_or_repeated',                       type:'bool',  label:'Paired/repeated?',                             group:'A' },
+        { name:'time_variable_available',                  type:'bool',  label:'Time variable?',                               group:'A' },
+        { name:'number_of_timepoints',                     type:'int',   label:'# of timepoints',                              group:'A' },
+        { name:'explicit_group_time_interaction_question', type:'bool',  label:'Group×time interaction Q?',                    group:'A' },
+        { name:'batch_variable_available',                 type:'bool',  label:'Batch variable?',                              group:'A' },
+        // group B = context (subtle)
+        { name:'omics_platform',                           type:'enum',  values:PLATFORMS, label:'Platform',                   group:'B' },
+        { name:'is_raw_count',                             type:'bool',  label:'Raw counts?',                                  group:'B' },
+        { name:'is_log_or_normalized',                     type:'bool',  label:'Log/normalized?',                              group:'B' },
+        { name:'primary_group_variable',                   type:'str',   label:'Group column',                                 group:'B' },
+        { name:'group_levels',                             type:'list',  label:'Group levels',                                 group:'B' },
+        { name:'reference_level',                          type:'str',   label:'Reference (control) level',                    group:'B' },
+        { name:'is_cross_sectional',                       type:'bool',  label:'Cross-sectional?',                             group:'B' },
+        { name:'subject_id_available',                     type:'bool',  label:'Subject IDs?',                                 group:'B' },
+        { name:'subject_id_column',                        type:'str',   label:'Subject ID column',                            group:'B' },
+        { name:'time_variable_column',                     type:'str',   label:'Time column',                                  group:'B' },
+        { name:'explicit_time_course_question',            type:'bool',  label:'Time-course Q?',                               group:'B' },
+        { name:'batch_variables',                          type:'list',  label:'Batch columns',                                group:'B' },
+        { name:'primary_analysis_goal',                    type:'str',   label:'Goal (free text)',                             group:'B' },
+      ];
+
+      // (0.1.6: schemaCard is purely informational here — no client-side
+      // label prediction. The LLM picks the label in stage 3 when the user
+      // clicks "Run pipeline →".)
+
+      // ── dropzone wiring ──
+      dz.addEventListener('click', () => fileInput.click());
+      fileInput.addEventListener('change', (e) => {
+        if (e.target.files && e.target.files[0]) attachFile(e.target.files[0]);
+      });
+      ['dragenter','dragover'].forEach(ev => dz.addEventListener(ev, (e) => {
+        e.preventDefault(); dz.classList.add('drag');
+      }));
+      ['dragleave','drop'].forEach(ev => dz.addEventListener(ev, (e) => {
+        e.preventDefault(); dz.classList.remove('drag');
+      }));
+      dz.addEventListener('drop', (e) => {
+        if (e.dataTransfer.files && e.dataTransfer.files[0]) attachFile(e.dataTransfer.files[0]);
+      });
+
+      function attachFile(f) {
+        chosenFile = f;
+        dz.classList.add('has-file');
+        const sizeMB = (f.size/1024/1024).toFixed(2);
+        dzLabel.innerHTML = `<span class="material-symbols-outlined !text-base align-bottom text-[var(--brand-green)]">check_circle</span>
+          <span class="font-semibold">${escapeHtml(f.name)}</span> · ${sizeMB} MB
+          <span class="ml-2 text-xs text-slate-500">(click to replace)</span>`;
+      }
+
+      // ── lightbox ──
+      lightbox.addEventListener('click', () => lightbox.classList.remove('open'));
+
+      // ── reset (any "+ New analysis" button) ──
+      function resetForm() {
+        chosenFile = null;
+        fileInput.value = '';
+        dz.classList.remove('has-file', 'drag');
+        dzLabel.innerHTML =
+          'Drag &amp; drop your <span class="font-mono">.tsv</span> here, or click to browse';
+        ctxInput.value = '';
+        formHint.textContent = 'Both inputs required.';
+        for (const c of [loadingCard, schemaCard, resultCard, declineCard, errorCard]) {
+          c.classList.add('hidden');
+        }
+        currentRunId = null;
+        currentSchema = null;
+        // clear previously-rendered tables / images so the cards don't flash old data
+        $('imgGrid').innerHTML = '';
+        $('tableTabs').innerHTML = '';
+        $('tableBox').innerHTML = '';
+        document.getElementById('run').scrollIntoView({ behavior: 'smooth', block: 'start' });
+      }
+      document.body.addEventListener('click', (e) => {
+        const btn = e.target.closest('[data-action="reset"]');
+        if (btn) resetForm();
+      });
+
+      // ── form submit ──
+      $('runForm').addEventListener('submit', async (e) => {
+        e.preventDefault();
+        const ctx = ctxInput.value.trim();
+        if (!chosenFile) { formHint.textContent = 'Please attach a TSV file.'; return; }
+        if (!ctx)        { formHint.textContent = 'Please write a study context.'; return; }
+        submitBtn.disabled = true;
+        formHint.textContent = '';
+        showOnly(loadingCard);
+        markStep('upload', 'done');
+        markStep('schema', 'active');
+
+        // step animation timer (cosmetic — driven by typical timings)
+        const stepTimer = animateSteps();
+
+        const fd = new FormData();
+        fd.append('tsv', chosenFile);
+        fd.append('context', ctx);
+
+        try {
+          const r = await fetch('/api/extract', { method: 'POST', body: fd });
+          clearInterval(stepTimer);
+          const data = await r.json();
+          if (!r.ok || !data.ok) {
+            renderError(data.error || `HTTP ${r.status}`);
+            return;
+          }
+          markStep('classify', 'done');
+          renderSchemaCard(data);
+        } catch (err) {
+          clearInterval(stepTimer);
+          renderError(String(err));
+        } finally {
+          submitBtn.disabled = false;
+        }
+      });
+
+      // ── stage 2 — pipeline run after the user reviews / edits the schema ──
+      $('runPipelineBtn').addEventListener('click', async () => {
+        if (!currentRunId || !currentSchema) return;
+        const btn = $('runPipelineBtn');
+        btn.disabled = true;
+        showOnly(loadingCard);
+        markStep('upload', 'done'); markStep('schema', 'done');
+        markStep('classify', 'done'); markStep('pipeline', 'active');
+
+        const fd = new FormData();
+        fd.append('run_id', currentRunId);
+        fd.append('schema', JSON.stringify(currentSchema));
+
+        try {
+          const r = await fetch('/api/run_pipeline', { method: 'POST', body: fd });
+          const data = await r.json();
+          if (!r.ok || !data.ok) {
+            renderError(data.error || `HTTP ${r.status}`);
+            return;
+          }
+          markStep('package', 'done');
+          if (!data.valid || data.label === 'none_of_these') renderDecline(data);
+          else renderResult(data);
+        } catch (err) {
+          renderError(String(err));
+        } finally {
+          btn.disabled = false;
+        }
+      });
+
+      // ── render: schema card ──
+      function renderSchemaCard(d) {
+        showOnly(schemaCard);
+        currentRunId = d.run_id;
+        currentSchema = { ...d.schema };
+
+        const root = $('schemaFields');
+        root.innerHTML = '';
+        const groups = { A: [], B: [] };
+        for (const f of SCHEMA_FIELDS) groups[f.group].push(f);
+
+        function addGroup(items, header) {
+          if (!items.length) return;
+          const h = document.createElement('div');
+          h.className = 'md:col-span-2 mt-3 first:mt-0 text-[10px] font-semibold uppercase tracking-wider text-slate-400';
+          h.textContent = header;
+          root.appendChild(h);
+          for (const f of items) root.appendChild(makeRow(f));
+        }
+        addGroup(groups.A, 'Decision-driving fields');
+        addGroup(groups.B, 'Context fields');
+      }
+
+      function makeRow(f) {
+        const row = document.createElement('div');
+        row.className = 'flex items-center gap-2 py-1';
+        const lbl = document.createElement('label');
+        lbl.className = 'text-xs w-44 shrink-0 ' + (f.group === 'A' ? 'text-[var(--brand-green)] font-semibold' : 'text-slate-500');
+        lbl.textContent = f.label;
+        row.appendChild(lbl);
+        row.appendChild(makeControl(f, currentSchema[f.name]));
+        return row;
+      }
+
+      function makeControl(f, v) {
+        if (f.type === 'bool') {
+          const wrap = document.createElement('div');
+          wrap.className = 'flex items-center gap-3 text-xs text-slate-700';
+          wrap.innerHTML = `
+            <label class="cursor-pointer"><input type="radio" name="${f.name}" data-field="${f.name}" data-bool="true" ${v===true?'checked':''}> Yes</label>
+            <label class="cursor-pointer"><input type="radio" name="${f.name}" data-field="${f.name}" data-bool="false" ${v===false?'checked':''}> No</label>
+          `;
+          wrap.querySelectorAll('input').forEach(i => i.addEventListener('change', onSchemaEdit));
+          return wrap;
+        }
+        if (f.type === 'enum') {
+          const sel = document.createElement('select');
+          sel.dataset.field = f.name;
+          sel.className = 'text-xs border border-slate-200 rounded px-2 py-1 bg-white';
+          for (const opt of f.values) {
+            const o = document.createElement('option');
+            o.value = opt; o.textContent = opt;
+            if (v === opt) o.selected = true;
+            sel.appendChild(o);
+          }
+          sel.addEventListener('change', onSchemaEdit);
+          return sel;
+        }
+        if (f.type === 'int' || f.type === 'float') {
+          const inp = document.createElement('input');
+          inp.type = 'number';
+          if (f.type === 'float') inp.step = '0.01';
+          inp.value = v ?? 0;
+          inp.dataset.field = f.name;
+          inp.className = 'text-xs border border-slate-200 rounded px-2 py-1 w-24 bg-white';
+          if (f.readonly) inp.disabled = true;
+          inp.addEventListener('input', onSchemaEdit);
+          return inp;
+        }
+        if (f.type === 'list') {
+          const inp = document.createElement('input');
+          inp.type = 'text';
+          inp.value = (v || []).join(', ');
+          inp.dataset.field = f.name;
+          inp.dataset.list = 'true';
+          inp.className = 'text-xs border border-slate-200 rounded px-2 py-1 flex-1 bg-white';
+          inp.placeholder = 'comma-separated';
+          inp.addEventListener('input', onSchemaEdit);
+          return inp;
+        }
+        const inp = document.createElement('input');
+        inp.type = 'text';
+        inp.value = v ?? '';
+        inp.dataset.field = f.name;
+        inp.className = 'text-xs border border-slate-200 rounded px-2 py-1 flex-1 bg-white';
+        inp.addEventListener('input', onSchemaEdit);
+        return inp;
+      }
+
+      function readSchemaFromUI() {
+        const out = { ...currentSchema };
+        for (const f of SCHEMA_FIELDS) {
+          if (f.type === 'bool') {
+            const checked = document.querySelector(`#schemaFields input[name="${f.name}"]:checked`);
+            if (checked) out[f.name] = checked.dataset.bool === 'true';
+          } else {
+            const el = document.querySelector(`#schemaFields [data-field="${f.name}"]`);
+            if (!el) continue;
+            if (el.dataset.list === 'true') {
+              out[f.name] = el.value.split(',').map(s => s.trim()).filter(Boolean);
+            } else if (f.type === 'int') {
+              out[f.name] = parseInt(el.value || '0', 10);
+            } else if (f.type === 'float') {
+              out[f.name] = parseFloat(el.value || '0');
+            } else {
+              out[f.name] = el.value;
+            }
+          }
+        }
+        return out;
+      }
+
+      function onSchemaEdit() {
+        // 0.1.6: edits update currentSchema only. No live label prediction —
+        // the LLM picks the label when the user clicks "Run pipeline →".
+        currentSchema = readSchemaFromUI();
+      }
+
+      // step pacing (rough, falls back to "active" until reply arrives)
+      function animateSteps() {
+        const sequence = ['schema','classify','pipeline','package'];
+        let i = 0;
+        return setInterval(() => {
+          if (i < sequence.length - 1) {
+            markStep(sequence[i], 'done');
+            markStep(sequence[i+1], 'active');
+          }
+          i++;
+        }, 6000);
+      }
+
+      function markStep(name, klass) {
+        const el = document.querySelector(`.step-line[data-step="${name}"]`);
+        if (!el) return;
+        el.classList.remove('active','done');
+        el.classList.add(klass);
+        const icon = el.querySelector('.step-icon');
+        if (klass === 'done') icon.textContent = '✓';
+      }
+
+      function showOnly(card) {
+        for (const c of [loadingCard, schemaCard, resultCard, declineCard, errorCard]) {
+          c.classList.toggle('hidden', c !== card);
+        }
+        // never hide formCard — user keeps it as reference / for next run
+      }
+
+      // ── render: result ──
+      async function renderResult(d) {
+        showOnly(resultCard);
+        $('resLabel').textContent = d.label;
+        $('resPipeline').textContent = `pipeline: ${d.pipeline}`;
+        $('resElapsed').textContent  = `· ${d.elapsed}s`;
+        $('resReason').textContent   = d.reasoning || '(no reasoning)';
+        $('dlZip').href              = `/api/zip/${d.run_id}`;
+
+        // images
+        const grid = $('imgGrid'); grid.innerHTML = '';
+        for (const name of d.images) {
+          const url = `/api/artifact/${d.run_id}/${encodeURIComponent(name)}`;
+          const wrap = document.createElement('div');
+          wrap.className = 'img-thumb rounded-lg overflow-hidden bg-white';
+          wrap.innerHTML = `<img src="${url}" alt="${escapeHtml(name)}" class="w-full h-44 object-cover" />
+            <p class="px-2 py-1 text-[11px] text-slate-500 font-mono truncate">${escapeHtml(name)}</p>`;
+          wrap.querySelector('img').addEventListener('click', () => {
+            lightboxImg.src = url; lightbox.classList.add('open');
+          });
+          grid.appendChild(wrap);
+        }
+
+        // tables — render tabs then load first one
+        const tabs = $('tableTabs'); tabs.innerHTML = '';
+        const tableBox = $('tableBox'); tableBox.innerHTML = '';
+        $('tableTotal').textContent = '';
+        if (!d.tables.length) {
+          tableBox.innerHTML = '<p class="text-xs text-slate-400 p-4">No tables produced.</p>';
+          return;
+        }
+        d.tables.forEach((name, i) => {
+          const btn = document.createElement('button');
+          btn.className = 'px-3 py-1.5 rounded-full text-xs border border-slate-200 hover:bg-slate-50 transition';
+          btn.dataset.idx = i;
+          btn.textContent = name;
+          btn.addEventListener('click', () => loadTable(d.run_id, name, btn));
+          tabs.appendChild(btn);
+        });
+        loadTable(d.run_id, d.tables[0], tabs.children[0]);
+      }
+
+      async function loadTable(run, name, btnEl) {
+        for (const b of $('tableTabs').children) {
+          b.classList.remove('bg-[var(--brand-green)]','text-white','border-transparent');
+        }
+        if (btnEl) btnEl.classList.add('bg-[var(--brand-green)]','text-white','border-transparent');
+
+        const r = await fetch(`/api/csv_preview/${run}/${encodeURIComponent(name)}?limit=20`);
+        const data = await r.json();
+        const total = data.total || 0;
+        $('tableTotal').textContent = `· ${name} · showing ${data.shown} of ${total} rows`;
+
+        let html = '<table class="preview-table"><thead><tr>';
+        for (const h of data.headers) html += `<th>${escapeHtml(h)}</th>`;
+        html += '</tr></thead><tbody>';
+        for (const row of data.rows) {
+          html += '<tr>' + row.map(c => `<td>${escapeHtml(c)}</td>`).join('') + '</tr>';
+        }
+        html += '</tbody></table>';
+        $('tableBox').innerHTML = html;
+      }
+
+      // ── render: decline ──
+      function renderDecline(d) {
+        showOnly(declineCard);
+        $('declineReason').textContent = d.reasoning || '(no reasoning)';
+      }
+
+      // ── render: error ──
+      function renderError(msg) {
+        showOnly(errorCard);
+        $('errorMsg').textContent = msg;
+      }
+
+      // ── helpers ──
+      function escapeHtml(s) {
+        return String(s ?? '').replace(/[&<>"']/g, (m) => ({
+          '&':'&amp;','<':'&lt;','>':'&gt;','"':'&quot;',"'":'&#39;'
+        }[m]));
+      }
+    </script>
+  </body>
+</html>

src/statlens/pipelines/deseq2_basic_pipeline/01_deseq2_basic_analysis.py

@@ -0,0 +1,145 @@
+"""
+01_deseq2_basic_analysis.py
+
+Count_DESeq2_basic workflow: two independent groups, cross-sectional design, no batch effect.
+
+Input:
+    metadata.csv: samples x annotations; must contain a condition column with exactly two groups.
+    counts.csv: genes/features x samples; raw non-negative integer counts.
+
+Output:
+    DESeq2_basic_results_case_vs_control.csv
+    DESeq2_basic_significant_genes.csv
+    normalized_counts.csv
+    log_normalized_counts.csv
+    metadata_used_for_analysis.csv
+"""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+from pydeseq2.dds import DeseqDataSet
+from pydeseq2.ds import DeseqStats
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Run Count_DESeq2_basic analysis using PyDESeq2.")
+    parser.add_argument("--metadata", default="metadata.csv", help="Path to metadata CSV file.")
+    parser.add_argument("--counts", default="counts.csv", help="Path to raw count matrix CSV file.")
+    parser.add_argument("--condition_col", default="condition", help="Condition column in metadata.")
+    parser.add_argument("--ref_level", default="control", help="Reference condition level.")
+    parser.add_argument("--test_level", default="case", help="Test condition level.")
+    parser.add_argument("--min_total_count", type=int, default=10, help="Minimum total count required to keep a gene.")
+    parser.add_argument("--padj_cutoff", type=float, default=0.05, help="Adjusted p-value cutoff.")
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0, help="Absolute log2 fold-change cutoff.")
+    parser.add_argument("--output_dir", default=".", help="Directory for output files.")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    metadata = pd.read_csv(args.metadata, index_col=0)
+    counts = pd.read_csv(args.counts, index_col=0)
+
+    if args.condition_col not in metadata.columns:
+        raise ValueError(f"Metadata must contain column: {args.condition_col}")
+
+    shared_samples = metadata.index.intersection(counts.columns)
+    if len(shared_samples) == 0:
+        raise ValueError("No shared sample IDs between metadata index and count matrix columns.")
+
+    metadata = metadata.loc[shared_samples].copy()
+    counts = counts.loc[:, shared_samples].copy()
+
+    metadata = metadata.dropna(subset=[args.condition_col])
+    counts = counts.loc[:, metadata.index]
+
+    observed_groups = metadata[args.condition_col].astype(str).unique().tolist()
+    expected_groups = {args.ref_level, args.test_level}
+    if set(observed_groups) != expected_groups:
+        raise ValueError(
+            f"Count_DESeq2_basic requires exactly two groups: {expected_groups}. "
+            f"Observed groups: {observed_groups}"
+        )
+
+    group_counts = metadata[args.condition_col].value_counts()
+    print("Sample counts by condition:")
+    print(group_counts)
+
+    if (group_counts < 2).any():
+        raise ValueError("Each group should contain at least two biological replicates.")
+
+    metadata[args.condition_col] = pd.Categorical(
+        metadata[args.condition_col],
+        categories=[args.ref_level, args.test_level],
+        ordered=False,
+    )
+
+    if (counts < 0).any().any():
+        raise ValueError("Count matrix contains negative values.")
+
+    if not np.all(np.equal(counts.values, np.floor(counts.values))):
+        raise ValueError("Counts must be raw integer counts, not TPM/FPKM/CPM/log-normalized values.")
+
+    counts = counts.astype(int)
+
+    keep_genes = counts.sum(axis=1) >= args.min_total_count
+    counts = counts.loc[keep_genes].copy()
+    if counts.shape[0] == 0:
+        raise ValueError("No genes remain after low-count filtering.")
+
+    print(f"Genes retained after filtering: {counts.shape[0]}")
+
+    counts_for_deseq = counts.T
+    metadata = metadata.loc[counts_for_deseq.index]
+
+    dds = DeseqDataSet(
+        counts=counts_for_deseq,
+        metadata=metadata,
+        design_factors=args.condition_col,
+        refit_cooks=True,
+    )
+    dds.deseq2()
+
+    stat_res = DeseqStats(
+        dds,
+        contrast=[args.condition_col, args.test_level, args.ref_level],
+        alpha=args.padj_cutoff,
+    )
+    stat_res.summary()
+
+    # LFC shrinkage stabilizes fold-change estimates for lowly expressed genes
+    coeff = f"{args.condition_col}_{args.test_level}_vs_{args.ref_level}"
+    try:
+        stat_res.lfc_shrink(coeff=coeff)
+        print(f"LFC shrinkage applied (coeff: {coeff}).")
+    except Exception as e:
+        print(f"Warning: LFC shrinkage failed ({e}). Using unshrunken LFC.")
+
+    res = stat_res.results_df.copy().sort_values("padj")
+    res["significant"] = (res["padj"] < args.padj_cutoff) & (res["log2FoldChange"].abs() >= args.lfc_cutoff)
+    res["direction"] = "not_significant"
+    res.loc[(res["significant"]) & (res["log2FoldChange"] > 0), "direction"] = f"up_in_{args.test_level}"
+    res.loc[(res["significant"]) & (res["log2FoldChange"] < 0), "direction"] = f"down_in_{args.test_level}"
+
+    res.to_csv(os.path.join(args.output_dir, "DESeq2_basic_results_case_vs_control.csv"))
+    res[res["significant"]].to_csv(os.path.join(args.output_dir, "DESeq2_basic_significant_genes.csv"))
+
+    normalized_counts = pd.DataFrame(dds.layers["normed_counts"], index=dds.obs_names, columns=dds.var_names)
+    normalized_counts.to_csv(os.path.join(args.output_dir, "normalized_counts.csv"))
+    np.log2(normalized_counts + 1).to_csv(os.path.join(args.output_dir, "log_normalized_counts.csv"))
+    metadata.to_csv(os.path.join(args.output_dir, "metadata_used_for_analysis.csv"))
+
+    print("Analysis complete.")
+    print(f"Full result table: {os.path.join(args.output_dir, 'DESeq2_basic_results_case_vs_control.csv')}")
+    print(f"Significant result table: {os.path.join(args.output_dir, 'DESeq2_basic_significant_genes.csv')}")
+    print(f"Normalized counts: {os.path.join(args.output_dir, 'normalized_counts.csv')}")
+    print(f"Log-normalized counts: {os.path.join(args.output_dir, 'log_normalized_counts.csv')}")
+    print(f"Number of significant genes: {int(res['significant'].sum())}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_basic_pipeline/02_volcano_plot.py

@@ -0,0 +1,72 @@
+"""Create a volcano plot from DESeq2 results."""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create a volcano plot from DESeq2 results.")
+    parser.add_argument("--results", default="DESeq2_basic_results_case_vs_control.csv")
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output", default="volcano_plot.png")
+    parser.add_argument("--output_dir", default=".", help="Directory for output files.")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    plot_df = res.dropna(subset=["log2FoldChange", "padj"]).copy()
+    plot_df["safe_padj"] = plot_df["padj"].replace(0, np.nextafter(0, 1))
+    plot_df["neg_log10_padj"] = -np.log10(plot_df["safe_padj"])
+    plot_df["category"] = "Not significant"
+    plot_df.loc[
+        (plot_df["padj"] < args.padj_cutoff) & (plot_df["log2FoldChange"] >= args.lfc_cutoff),
+        "category",
+    ] = "Up in test group"
+    plot_df.loc[
+        (plot_df["padj"] < args.padj_cutoff) & (plot_df["log2FoldChange"] <= -args.lfc_cutoff),
+        "category",
+    ] = "Down in test group"
+
+    color_map = {
+        "Not significant": "#AAAAAA",
+        "Up in test group": "#D62728",
+        "Down in test group": "#1F77B4",
+    }
+
+    plt.figure(figsize=(7, 6))
+    for category in ["Not significant", "Up in test group", "Down in test group"]:
+        subset = plot_df[plot_df["category"] == category]
+        plt.scatter(
+            subset["log2FoldChange"],
+            subset["neg_log10_padj"],
+            s=8,
+            alpha=0.6,
+            label=f"{category} (n={len(subset)})",
+            color=color_map[category],
+        )
+
+    plt.axvline(args.lfc_cutoff, linestyle="--", linewidth=1, color="black")
+    plt.axvline(-args.lfc_cutoff, linestyle="--", linewidth=1, color="black")
+    plt.axhline(-np.log10(args.padj_cutoff), linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log2 fold change")
+    plt.ylabel("-log10 adjusted p-value")
+    plt.title("Volcano plot")
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+
+    out_path = os.path.join(args.output_dir, args.output)
+    plt.savefig(out_path, dpi=300)
+    plt.close()
+    print(f"Saved: {out_path}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_basic_pipeline/03_ma_plot.py

@@ -0,0 +1,67 @@
+"""Create an MA plot from DESeq2 results."""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create an MA plot from DESeq2 results.")
+    parser.add_argument("--results", default="DESeq2_basic_results_case_vs_control.csv")
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output", default="MA_plot.png")
+    parser.add_argument("--output_dir", default=".", help="Directory for output files.")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    plot_df = res.dropna(subset=["baseMean", "log2FoldChange", "padj"]).copy()
+    plot_df = plot_df[plot_df["baseMean"] > 0].copy()
+
+    # Consistent with script 01: significant requires both padj and |LFC| cutoffs
+    plot_df["significant"] = (
+        (plot_df["padj"] < args.padj_cutoff) &
+        (plot_df["log2FoldChange"].abs() >= args.lfc_cutoff)
+    )
+
+    plt.figure(figsize=(7, 6))
+    nonsig = plot_df[~plot_df["significant"]]
+    sig = plot_df[plot_df["significant"]]
+    plt.scatter(
+        np.log10(nonsig["baseMean"]),
+        nonsig["log2FoldChange"],
+        s=8,
+        alpha=0.4,
+        label=f"Not significant (n={len(nonsig)})",
+        color="#AAAAAA",
+    )
+    plt.scatter(
+        np.log10(sig["baseMean"]),
+        sig["log2FoldChange"],
+        s=8,
+        alpha=0.8,
+        label=f"Significant (n={len(sig)})",
+        color="#D62728",
+    )
+    plt.axhline(0, linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log10 mean normalized expression")
+    plt.ylabel("log2 fold change")
+    plt.title("MA plot")
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+
+    out_path = os.path.join(args.output_dir, args.output)
+    plt.savefig(out_path, dpi=300)
+    plt.close()
+    print(f"Saved: {out_path}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_basic_pipeline/04_pca_plot.py

@@ -0,0 +1,79 @@
+"""Create a PCA plot from log-normalized counts."""
+
+import argparse
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create a PCA plot from log-normalized counts.")
+    parser.add_argument("--log_counts", default="log_normalized_counts.csv")
+    parser.add_argument("--metadata", default="metadata_used_for_analysis.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--output", default="PCA_plot.png")
+    parser.add_argument("--output_dir", default=".", help="Directory for output files.")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    log_counts = pd.read_csv(args.log_counts, index_col=0)
+    metadata = pd.read_csv(args.metadata, index_col=0)
+    shared_samples = log_counts.index.intersection(metadata.index)
+    log_counts = log_counts.loc[shared_samples]
+    metadata = metadata.loc[shared_samples]
+
+    if args.condition_col not in metadata.columns:
+        raise ValueError(f"Metadata must contain column: {args.condition_col}")
+
+    # No per-gene scaling: let naturally variable genes drive separation,
+    # which is standard for log-normalized RNA-seq PCA.
+    pca = PCA(n_components=2)
+    pcs = pca.fit_transform(log_counts)
+    pca_df = pd.DataFrame(pcs, index=log_counts.index, columns=["PC1", "PC2"])
+    pca_df = pca_df.join(metadata[[args.condition_col]])
+
+    plt.figure(figsize=(6, 5))
+    groups = sorted(pca_df[args.condition_col].astype(str).unique())
+    colors = plt.cm.Set1.colors
+    for i, group in enumerate(groups):
+        subset = pca_df[pca_df[args.condition_col].astype(str) == group]
+        plt.scatter(
+            subset["PC1"],
+            subset["PC2"],
+            s=60,
+            alpha=0.85,
+            label=group,
+            color=colors[i % len(colors)],
+            edgecolors="white",
+            linewidths=0.5,
+        )
+
+    for sample in pca_df.index:
+        plt.annotate(
+            sample,
+            (pca_df.loc[sample, "PC1"], pca_df.loc[sample, "PC2"]),
+            fontsize=6,
+            alpha=0.7,
+            xytext=(3, 3),
+            textcoords="offset points",
+        )
+
+    plt.xlabel(f"PC1 ({pca.explained_variance_ratio_[0] * 100:.1f}%)")
+    plt.ylabel(f"PC2 ({pca.explained_variance_ratio_[1] * 100:.1f}%)")
+    plt.title("PCA of log-normalized counts")
+    plt.legend(title=args.condition_col)
+    plt.tight_layout()
+
+    out_path = os.path.join(args.output_dir, args.output)
+    plt.savefig(out_path, dpi=300)
+    plt.close()
+    print(f"Saved: {out_path}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_basic_pipeline/05_heatmap_top_de_genes.py

@@ -0,0 +1,105 @@
+"""Create a heatmap of the top differentially expressed genes."""
+
+import argparse
+import os
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create a heatmap of top DE genes.")
+    parser.add_argument("--results", default="DESeq2_basic_results_case_vs_control.csv")
+    parser.add_argument("--log_counts", default="log_normalized_counts.csv")
+    parser.add_argument("--metadata", default="metadata_used_for_analysis.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--top_n", type=int, default=50)
+    parser.add_argument("--output", default="top_DE_genes_heatmap.png")
+    parser.add_argument("--output_dir", default=".", help="Directory for output files.")
+    return parser.parse_args()
+
+
+def zscore_by_gene(df):
+    gene_means = df.mean(axis=1)
+    gene_stds = df.std(axis=1).replace(0, 1)
+    return df.sub(gene_means, axis=0).div(gene_stds, axis=0)
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    log_counts = pd.read_csv(args.log_counts, index_col=0)
+    metadata = pd.read_csv(args.metadata, index_col=0)
+
+    # Prefer significant genes; fall back to top N by padj if not enough
+    if "significant" in res.columns:
+        sig_res = res[res["significant"] == True].dropna(subset=["padj"]).sort_values("padj")
+    else:
+        sig_res = res.dropna(subset=["padj"]).sort_values("padj")
+
+    if len(sig_res) >= args.top_n:
+        top_genes = sig_res.head(args.top_n).index
+    elif len(sig_res) > 0:
+        print(f"Only {len(sig_res)} significant genes found; using all of them.")
+        top_genes = sig_res.index
+    else:
+        print("No significant genes found; falling back to top genes by padj.")
+        top_genes = res.dropna(subset=["padj"]).sort_values("padj").head(args.top_n).index
+
+    available_genes = [gene for gene in top_genes if gene in log_counts.columns]
+    if len(available_genes) == 0:
+        raise ValueError("None of the selected top genes are present in log_normalized_counts.csv.")
+
+    shared_samples = log_counts.index.intersection(metadata.index)
+    log_counts = log_counts.loc[shared_samples]
+    metadata = metadata.loc[shared_samples]
+
+    # Condition color bar for columns
+    groups = sorted(metadata[args.condition_col].astype(str).unique())
+    palette_colors = [plt.cm.Set1.colors[i % 9] for i in range(len(groups))]
+    condition_palette = dict(zip(groups, palette_colors))
+    col_colors = metadata[args.condition_col].astype(str).map(condition_palette)
+
+    heatmap_data = log_counts[available_genes].T
+    heatmap_data_z = zscore_by_gene(heatmap_data)
+
+    cluster = sns.clustermap(
+        heatmap_data_z,
+        figsize=(max(8, len(shared_samples) * 0.4 + 4), max(10, len(available_genes) * 0.2 + 2)),
+        col_cluster=True,
+        row_cluster=True,
+        col_colors=col_colors,
+        xticklabels=True,
+        yticklabels=True,
+        cmap="vlag",
+    )
+
+    # Rotate sample labels for readability
+    plt.setp(cluster.ax_heatmap.get_xticklabels(), rotation=45, ha="right", fontsize=8)
+    plt.setp(cluster.ax_heatmap.get_yticklabels(), fontsize=7)
+
+    # Add condition legend
+    legend_patches = [
+        mpatches.Patch(color=condition_palette[g], label=g) for g in groups
+    ]
+    cluster.ax_heatmap.legend(
+        handles=legend_patches,
+        title=args.condition_col,
+        loc="upper right",
+        bbox_to_anchor=(1.25, 1.1),
+        fontsize=8,
+    )
+
+    cluster.fig.suptitle(f"Top {len(available_genes)} differentially expressed genes", y=1.02)
+
+    out_path = os.path.join(args.output_dir, args.output)
+    cluster.savefig(out_path, dpi=300, bbox_inches="tight")
+    plt.close(cluster.fig)
+    print(f"Saved: {out_path}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_basic_pipeline/06_top_genes_barplot.py

@@ -0,0 +1,57 @@
+"""Create a horizontal bar plot of top differentially expressed genes."""
+
+import argparse
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create a bar plot of top DE genes.")
+    parser.add_argument("--results", default="DESeq2_basic_results_case_vs_control.csv")
+    parser.add_argument("--top_n", type=int, default=20)
+    parser.add_argument("--output", default="top20_DE_genes_barplot.png")
+    parser.add_argument("--output_dir", default=".", help="Directory for output files.")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    top_df = (
+        res.dropna(subset=["padj", "log2FoldChange"])
+        .sort_values("padj")
+        .head(args.top_n)
+        .copy()
+    )
+    if top_df.empty:
+        raise ValueError("No genes available for plotting after removing missing padj/log2FoldChange values.")
+
+    top_df = top_df.iloc[::-1]
+    colors = ["#D62728" if v > 0 else "#1F77B4" for v in top_df["log2FoldChange"]]
+
+    plt.figure(figsize=(8, max(6, len(top_df) * 0.3)))
+    bars = plt.barh(top_df.index, top_df["log2FoldChange"], color=colors)
+    plt.axvline(0, linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log2 fold change")
+    plt.ylabel("Gene")
+    plt.title(f"Top {args.top_n} differentially expressed genes")
+
+    from matplotlib.patches import Patch
+    legend_elements = [
+        Patch(facecolor="#D62728", label="Up in test group"),
+        Patch(facecolor="#1F77B4", label="Down in test group"),
+    ]
+    plt.legend(handles=legend_elements, fontsize=8)
+    plt.tight_layout()
+
+    out_path = os.path.join(args.output_dir, args.output)
+    plt.savefig(out_path, dpi=300)
+    plt.close()
+    print(f"Saved: {out_path}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_basic_pipeline/README.md

@@ -0,0 +1,56 @@
+# Count_DESeq2_basic Python Pipeline
+
+This folder contains six Python scripts for two-group differential expression analysis.
+
+1. `01_deseq2_basic_analysis.py`
+2. `02_volcano_plot.py`
+3. `03_ma_plot.py`
+4. `04_pca_plot.py`
+5. `05_heatmap_top_de_genes.py`
+6. `06_top_genes_barplot.py`
+
+## Expected input
+
+`metadata.csv`
+
+- Rows: samples
+- Must contain a column named `condition`
+- Example values: `control`, `case`
+
+`counts.csv`
+
+- Rows: genes or features
+- Columns: samples
+- Values: raw non-negative integer counts
+
+## Quick start (recommended)
+
+```bash
+bash run_pipeline.sh metadata.csv counts.csv output/
+```
+
+All outputs (CSVs and plots) will be written to the `output/` directory.
+
+## Manual run order
+
+```bash
+python 01_deseq2_basic_analysis.py \
+  --metadata metadata.csv \
+  --counts counts.csv \
+  --condition_col condition \
+  --ref_level control \
+  --test_level case \
+  --output_dir output/
+
+python 02_volcano_plot.py --results output/DESeq2_basic_results_case_vs_control.csv --output_dir output/
+python 03_ma_plot.py     --results output/DESeq2_basic_results_case_vs_control.csv --output_dir output/
+python 04_pca_plot.py    --log_counts output/log_normalized_counts.csv --metadata output/metadata_used_for_analysis.csv --output_dir output/
+python 05_heatmap_top_de_genes.py --results output/DESeq2_basic_results_case_vs_control.csv --log_counts output/log_normalized_counts.csv --metadata output/metadata_used_for_analysis.csv --output_dir output/
+python 06_top_genes_barplot.py --results output/DESeq2_basic_results_case_vs_control.csv --output_dir output/
+```
+
+## Required packages
+
+```bash
+pip install pandas numpy matplotlib seaborn scikit-learn pydeseq2
+```

src/statlens/pipelines/deseq2_basic_pipeline/run_pipeline.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+# Run the full DESeq2 basic pipeline in order.
+#
+# Usage:
+#   bash run_pipeline.sh [metadata.csv] [counts.csv] [output_dir]
+#
+# Defaults: metadata.csv, counts.csv, ./output
+
+set -euo pipefail
+
+METADATA=${1:-metadata.csv}
+COUNTS=${2:-counts.csv}
+OUTPUT_DIR=${3:-output}
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+echo "=== Step 1: DESeq2 analysis ==="
+python "$SCRIPT_DIR/01_deseq2_basic_analysis.py" \
+    --metadata "$METADATA" \
+    --counts "$COUNTS" \
+    --output_dir "$OUTPUT_DIR"
+
+RESULTS="$OUTPUT_DIR/DESeq2_basic_results_case_vs_control.csv"
+LOG_COUNTS="$OUTPUT_DIR/log_normalized_counts.csv"
+METADATA_OUT="$OUTPUT_DIR/metadata_used_for_analysis.csv"
+
+echo "=== Step 2: Volcano plot ==="
+python "$SCRIPT_DIR/02_volcano_plot.py" \
+    --results "$RESULTS" \
+    --output_dir "$OUTPUT_DIR"
+
+echo "=== Step 3: MA plot ==="
+python "$SCRIPT_DIR/03_ma_plot.py" \
+    --results "$RESULTS" \
+    --output_dir "$OUTPUT_DIR"
+
+echo "=== Step 4: PCA plot ==="
+python "$SCRIPT_DIR/04_pca_plot.py" \
+    --log_counts "$LOG_COUNTS" \
+    --metadata "$METADATA_OUT" \
+    --output_dir "$OUTPUT_DIR"
+
+echo "=== Step 5: Heatmap ==="
+python "$SCRIPT_DIR/05_heatmap_top_de_genes.py" \
+    --results "$RESULTS" \
+    --log_counts "$LOG_COUNTS" \
+    --metadata "$METADATA_OUT" \
+    --output_dir "$OUTPUT_DIR"
+
+echo "=== Step 6: Top genes bar plot ==="
+python "$SCRIPT_DIR/06_top_genes_barplot.py" \
+    --results "$RESULTS" \
+    --output_dir "$OUTPUT_DIR"
+
+echo ""
+echo "Pipeline complete. All outputs written to: $OUTPUT_DIR"

src/statlens/pipelines/deseq2_group_time_interaction_pipeline/01_deseq2_group_time_interaction_analysis.py

@@ -0,0 +1,254 @@
+"""
+01_deseq2_group_time_interaction_analysis.py
+
+Count_DESeq2_group_time_interaction workflow: ≥2 groups × ≥2 time points,
+where the PRIMARY question is whether the time trajectory DIFFERS between groups
+(i.e., the group × time interaction effect).
+
+Strategy:
+  - A combined factor group_time is created (e.g. "control_T0", "case_T0", …).
+  - Design: ~ group_time  (equivalent to full factorial ~ group + time + group:time).
+  - The interaction contrast is built from the fitted coefficient vector:
+      interaction_LFC = LFC(test@T_end vs test@T_start)
+                       - LFC(ref@T_end  vs ref@T_start)
+  - The contrast is expressed as a numpy coefficient array applied via DeseqStats.
+  - For ≥3 time points the interaction is computed for every adjacent pair and
+    the minimum-padj contrast is reported as the primary result.
+  - Individual pairwise and delta-LFC files are also saved.
+
+Input:
+    metadata.csv  – samples × annotations with condition and time columns.
+    counts.csv    – genes/features × samples; raw non-negative integer counts.
+
+Output:
+    results.csv                       (interaction effect, primary)
+    significant_genes.csv
+    interaction_{T1}_to_{T2}.csv     (one file per adjacent time-step)
+    normalized_counts.csv
+    log_normalized_counts.csv
+    metadata_used_for_analysis.csv
+"""
+
+import argparse
+import os
+import warnings
+import numpy as np
+import pandas as pd
+from pydeseq2.dds import DeseqDataSet
+from pydeseq2.ds import DeseqStats
+from statsmodels.stats.multitest import multipletests
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="DESeq2 group × time interaction analysis."
+    )
+    parser.add_argument("--metadata", default="metadata.csv")
+    parser.add_argument("--counts", default="counts.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--time_col", default="time")
+    parser.add_argument("--ref_level", default="control")
+    parser.add_argument("--test_level", default="case")
+    parser.add_argument("--min_total_count", type=int, default=10)
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def build_interaction_contrast(dds, comb_col, ref_grp, test_grp, t_start, t_end):
+    """
+    Return a numpy contrast vector for the interaction effect:
+        LFC(test@t_end vs test@t_start) - LFC(ref@t_end vs ref@t_start)
+    = coef[test_t_end] - coef[test_t_start] - coef[ref_t_end]
+    (all relative to the reference level ref_t_start which is absorbed in the intercept)
+    """
+    design_cols = dds.obsm["design_matrix"].columns.tolist()
+    contrast = np.zeros(len(design_cols))
+
+    # pydeseq2 names non-reference factor levels as "{factor}_{level}"
+    targets = {
+        f"{comb_col}_{test_grp}_{t_end}":   +1.0,
+        f"{comb_col}_{test_grp}_{t_start}": -1.0,
+        f"{comb_col}_{ref_grp}_{t_end}":    -1.0,
+    }
+    # [statLens-V4-patch-0424] support both pydeseq2<0.5 ('{c}_{l}') and
+    # pydeseq2>=0.5 formulaic ('{c}[T.{l}]') column naming.
+    missing = []
+    for col, val in targets.items():
+        # primary name (legacy)
+        candidates = [col]
+        # formulaic alt: 'group_time_case_14' -> 'group_time[T.case_14]'
+        # split once on comb_col prefix
+        if col.startswith(comb_col + '_'):
+            level = col[len(comb_col) + 1:]
+            candidates.append(f'{comb_col}[T.{level}]')
+        found = False
+        for cand in candidates:
+            if cand in design_cols:
+                contrast[design_cols.index(cand)] = val
+                found = True
+                break
+        if not found:
+            missing.append(col)
+
+    if missing:
+        raise ValueError(
+            f"Expected design matrix columns not found: {missing}.\n"
+            f"Available columns: {design_cols}\n"
+            f"Hint: check that --ref_level, --test_level, and time labels match "
+            f"the metadata exactly (case-sensitive)."
+        )
+    return contrast
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    metadata = pd.read_csv(args.metadata, index_col=0)
+    counts = pd.read_csv(args.counts, index_col=0)
+
+    for col in [args.condition_col, args.time_col]:
+        if col not in metadata.columns:
+            raise ValueError(f"Metadata must contain column: {col}")
+
+    shared = metadata.index.intersection(counts.columns)
+    if len(shared) == 0:
+        raise ValueError("No shared sample IDs.")
+    metadata = metadata.loc[shared].copy()
+    counts = counts.loc[:, shared].copy()
+
+    metadata = metadata.dropna(subset=[args.condition_col, args.time_col])
+    counts = counts.loc[:, metadata.index]
+
+    observed_cond = set(metadata[args.condition_col].astype(str).unique())
+    if {args.ref_level, args.test_level} != observed_cond:
+        raise ValueError(
+            f"Condition column must have exactly two levels "
+            f"{{{args.ref_level}, {args.test_level}}}; found {observed_cond}."
+        )
+
+    time_points = sorted(metadata[args.time_col].astype(str).unique())
+    if len(time_points) < 2:
+        raise ValueError("Need at least 2 time points for an interaction analysis.")
+    print(f"Groups: ref='{args.ref_level}', test='{args.test_level}'")
+    print(f"Time points (sorted): {time_points}")
+
+    if (counts < 0).any().any():
+        raise ValueError("Count matrix contains negative values.")
+    if not np.all(np.equal(counts.values, np.floor(counts.values))):
+        raise ValueError("Counts must be raw integers.")
+    counts = counts.astype(int)
+
+    counts = counts.loc[counts.sum(axis=1) >= args.min_total_count].copy()
+    if counts.shape[0] == 0:
+        raise ValueError("No genes remain after low-count filtering.")
+    print(f"Genes retained after filtering: {counts.shape[0]}")
+
+    # Create combined group_time factor; reference = ref_level + first time point
+    comb_col = "group_time"
+    metadata[comb_col] = (
+        metadata[args.condition_col].astype(str) + "_" +
+        metadata[args.time_col].astype(str)
+    )
+    ref_combo = f"{args.ref_level}_{time_points[0]}"
+    all_combos = sorted(metadata[comb_col].unique())
+    if ref_combo not in all_combos:
+        raise ValueError(
+            f"Reference combo '{ref_combo}' not found in data. "
+            f"Available combos: {all_combos}"
+        )
+    # Put reference first so it becomes the baseline in the design matrix
+    non_ref = [c for c in all_combos if c != ref_combo]
+    metadata[comb_col] = pd.Categorical(
+        metadata[comb_col],
+        categories=[ref_combo] + non_ref,
+        ordered=False,
+    )
+
+    counts_t = counts.T
+    metadata = metadata.loc[counts_t.index]
+
+    dds = DeseqDataSet(
+        counts=counts_t,
+        metadata=metadata,
+        design_factors=comb_col,
+        refit_cooks=True,
+    )
+    dds.deseq2()
+
+    # Compute interaction for each adjacent time pair
+    adjacent_pairs = list(zip(time_points[:-1], time_points[1:]))
+    interaction_results = {}
+
+    for t_start, t_end in adjacent_pairs:
+        label = f"{t_start}_to_{t_end}"
+        try:
+            contrast_vec = build_interaction_contrast(
+                dds, comb_col, args.ref_level, args.test_level,
+                str(t_start), str(t_end)
+            )
+        except ValueError as e:
+            warnings.warn(str(e))
+            continue
+
+        stat_res = DeseqStats(dds, contrast=contrast_vec, alpha=args.padj_cutoff)
+        stat_res.summary()
+        iw = stat_res.results_df.copy()
+        iw.to_csv(os.path.join(args.output_dir, f"interaction_{label}.csv"))
+        interaction_results[label] = iw
+        print(f"Interaction {label}: done.")
+
+    if not interaction_results:
+        raise RuntimeError(
+            "No interaction contrasts could be computed. "
+            "Check that group and time labels in metadata match the arguments exactly."
+        )
+
+    # Combine: minimum padj across adjacent-pair interactions
+    all_pvals = pd.concat({k: v["pvalue"] for k, v in interaction_results.items()}, axis=1)
+    all_lfc = pd.concat({k: v["log2FoldChange"] for k, v in interaction_results.items()}, axis=1)
+
+    min_pval = all_pvals.min(axis=1)
+    valid = min_pval.notna()
+    global_padj = pd.Series(np.nan, index=min_pval.index)
+    _, padj_vals, _, _ = multipletests(min_pval[valid], method="fdr_bh")
+    global_padj[valid] = padj_vals
+
+    best_pair = all_pvals.idxmin(axis=1)
+    rep_lfc = pd.Series(
+        [all_lfc.loc[g, best_pair[g]] if pd.notna(best_pair[g]) else np.nan
+         for g in all_lfc.index],
+        index=all_lfc.index,
+    )
+    base_mean = list(interaction_results.values())[0]["baseMean"]
+
+    res = pd.DataFrame({
+        "baseMean": base_mean,
+        "log2FoldChange": rep_lfc,   # interaction LFC
+        "padj": global_padj,
+        "peak_interaction_window": best_pair,
+    }).sort_values("padj")
+
+    res["significant"] = (res["padj"] < args.padj_cutoff) & (res["log2FoldChange"].abs() >= args.lfc_cutoff)
+    res["direction"] = "not_significant"
+    res.loc[(res["significant"]) & (res["log2FoldChange"] > 0), "direction"] = "interaction_up"
+    res.loc[(res["significant"]) & (res["log2FoldChange"] < 0), "direction"] = "interaction_down"
+
+    res.to_csv(os.path.join(args.output_dir, "results.csv"))
+    res[res["significant"]].to_csv(os.path.join(args.output_dir, "significant_genes.csv"))
+
+    norm = pd.DataFrame(dds.layers["normed_counts"], index=dds.obs_names, columns=dds.var_names)
+    norm.to_csv(os.path.join(args.output_dir, "normalized_counts.csv"))
+    np.log2(norm + 1).to_csv(os.path.join(args.output_dir, "log_normalized_counts.csv"))
+    metadata[[args.condition_col, args.time_col]].to_csv(
+        os.path.join(args.output_dir, "metadata_used_for_analysis.csv")
+    )
+
+    print(f"Significant interaction genes: {int(res['significant'].sum())}")
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_group_time_interaction_pipeline/02_volcano_plot.py

@@ -0,0 +1,66 @@
+"""Create a volcano plot from analysis results."""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create a volcano plot from analysis results.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output", default="volcano_plot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    plot_df = res.dropna(subset=["log2FoldChange", "padj"]).copy()
+    plot_df["safe_padj"] = plot_df["padj"].replace(0, np.nextafter(0, 1))
+    plot_df["neg_log10_padj"] = -np.log10(plot_df["safe_padj"])
+    plot_df["category"] = "Not significant"
+    plot_df.loc[
+        (plot_df["padj"] < args.padj_cutoff) & (plot_df["log2FoldChange"] >= args.lfc_cutoff),
+        "category",
+    ] = "Up"
+    plot_df.loc[
+        (plot_df["padj"] < args.padj_cutoff) & (plot_df["log2FoldChange"] <= -args.lfc_cutoff),
+        "category",
+    ] = "Down"
+
+    color_map = {"Not significant": "#AAAAAA", "Up": "#D62728", "Down": "#1F77B4"}
+
+    plt.figure(figsize=(7, 6))
+    for cat in ["Not significant", "Up", "Down"]:
+        sub = plot_df[plot_df["category"] == cat]
+        plt.scatter(
+            sub["log2FoldChange"], sub["neg_log10_padj"],
+            s=8, alpha=0.6,
+            label=f"{cat} (n={len(sub)})",
+            color=color_map[cat],
+        )
+
+    plt.axvline(args.lfc_cutoff, linestyle="--", linewidth=1, color="black")
+    plt.axvline(-args.lfc_cutoff, linestyle="--", linewidth=1, color="black")
+    plt.axhline(-np.log10(args.padj_cutoff), linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log2 fold change")
+    plt.ylabel("-log10 adjusted p-value")
+    plt.title("Volcano plot")
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_group_time_interaction_pipeline/03_ma_plot.py

@@ -0,0 +1,57 @@
+"""Create an MA plot from analysis results."""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create an MA plot from analysis results.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output", default="MA_plot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    plot_df = res.dropna(subset=["baseMean", "log2FoldChange", "padj"]).copy()
+    plot_df = plot_df[plot_df["baseMean"] > 0].copy()
+    plot_df["significant"] = (
+        (plot_df["padj"] < args.padj_cutoff) &
+        (plot_df["log2FoldChange"].abs() >= args.lfc_cutoff)
+    )
+
+    plt.figure(figsize=(7, 6))
+    nonsig = plot_df[~plot_df["significant"]]
+    sig = plot_df[plot_df["significant"]]
+    plt.scatter(
+        np.log10(nonsig["baseMean"]), nonsig["log2FoldChange"],
+        s=8, alpha=0.4, label=f"Not significant (n={len(nonsig)})", color="#AAAAAA",
+    )
+    plt.scatter(
+        np.log10(sig["baseMean"]), sig["log2FoldChange"],
+        s=8, alpha=0.8, label=f"Significant (n={len(sig)})", color="#D62728",
+    )
+    plt.axhline(0, linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log10 mean expression")
+    plt.ylabel("log2 fold change")
+    plt.title("MA plot")
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_group_time_interaction_pipeline/04_pca_plot.py

@@ -0,0 +1,64 @@
+"""Create a PCA plot from log-normalised expression / count data."""
+
+import argparse
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="PCA plot from log-normalised data.")
+    parser.add_argument("--log_counts", default="log_normalized_counts.csv")
+    parser.add_argument("--metadata", default="metadata_used_for_analysis.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--output", default="PCA_plot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    log_counts = pd.read_csv(args.log_counts, index_col=0)
+    metadata = pd.read_csv(args.metadata, index_col=0)
+    shared = log_counts.index.intersection(metadata.index)
+    log_counts = log_counts.loc[shared]
+    metadata = metadata.loc[shared]
+
+    if args.condition_col not in metadata.columns:
+        raise ValueError(f"Metadata must contain column: {args.condition_col}")
+
+    pca = PCA(n_components=2)
+    pcs = pca.fit_transform(log_counts)
+    pca_df = pd.DataFrame(pcs, index=log_counts.index, columns=["PC1", "PC2"])
+    pca_df = pca_df.join(metadata[[args.condition_col]])
+
+    plt.figure(figsize=(6, 5))
+    groups = sorted(pca_df[args.condition_col].astype(str).unique())
+    colors = plt.cm.Set1.colors
+    for i, grp in enumerate(groups):
+        sub = pca_df[pca_df[args.condition_col].astype(str) == grp]
+        plt.scatter(sub["PC1"], sub["PC2"], s=60, alpha=0.85,
+                    label=grp, color=colors[i % len(colors)],
+                    edgecolors="white", linewidths=0.5)
+
+    for sample in pca_df.index:
+        plt.annotate(sample, (pca_df.loc[sample, "PC1"], pca_df.loc[sample, "PC2"]),
+                     fontsize=6, alpha=0.7, xytext=(3, 3), textcoords="offset points")
+
+    plt.xlabel(f"PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)")
+    plt.ylabel(f"PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)")
+    plt.title("PCA")
+    plt.legend(title=args.condition_col)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_group_time_interaction_pipeline/05_heatmap_top_de_genes.py

@@ -0,0 +1,101 @@
+"""Create a heatmap of the top differentially expressed genes/features."""
+
+import argparse
+import os
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Heatmap of top DE genes.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--log_counts", default="log_normalized_counts.csv")
+    parser.add_argument("--metadata", default="metadata_used_for_analysis.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--top_n", type=int, default=50)
+    parser.add_argument("--output", default="top_DE_genes_heatmap.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def zscore_by_gene(df):
+    means = df.mean(axis=1)
+    stds = df.std(axis=1).replace(0, 1)
+    return df.sub(means, axis=0).div(stds, axis=0)
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    log_counts = pd.read_csv(args.log_counts, index_col=0)
+    metadata = pd.read_csv(args.metadata, index_col=0)
+
+    # Select top genes: prefer significant ones
+    if "significant" in res.columns:
+        sig_res = res[res["significant"] == True].dropna(subset=["padj"]).sort_values("padj")
+    else:
+        sig_res = res.dropna(subset=["padj"]).sort_values("padj")
+
+    if len(sig_res) >= args.top_n:
+        top_genes = sig_res.head(args.top_n).index
+    elif len(sig_res) > 0:
+        print(f"Only {len(sig_res)} significant features; using all.")
+        top_genes = sig_res.index
+    else:
+        print("No significant features; falling back to top N by padj.")
+        top_genes = res.dropna(subset=["padj"]).sort_values("padj").head(args.top_n).index
+
+    available = [g for g in top_genes if g in log_counts.columns]
+    if not available:
+        raise ValueError("None of the top features are in log_normalized_counts.csv.")
+
+    shared = log_counts.index.intersection(metadata.index)
+    log_counts = log_counts.loc[shared]
+    metadata = metadata.loc[shared]
+
+    if args.condition_col not in metadata.columns:
+        col_colors = None
+        legend_patches = []
+    else:
+        groups = sorted(metadata[args.condition_col].astype(str).unique())
+        palette = [plt.cm.Set1.colors[i % 9] for i in range(len(groups))]
+        cond_pal = dict(zip(groups, palette))
+        col_colors = metadata[args.condition_col].astype(str).map(cond_pal)
+        legend_patches = [mpatches.Patch(color=cond_pal[g], label=g) for g in groups]
+
+    hm = log_counts[available].T
+    hm_z = zscore_by_gene(hm)
+
+    cluster = sns.clustermap(
+        hm_z,
+        figsize=(max(8, len(shared) * 0.4 + 4), max(10, len(available) * 0.2 + 2)),
+        col_cluster=True,
+        row_cluster=True,
+        col_colors=col_colors,
+        xticklabels=True,
+        yticklabels=True,
+        cmap="vlag",
+    )
+    plt.setp(cluster.ax_heatmap.get_xticklabels(), rotation=45, ha="right", fontsize=8)
+    plt.setp(cluster.ax_heatmap.get_yticklabels(), fontsize=7)
+
+    if legend_patches:
+        cluster.ax_heatmap.legend(
+            handles=legend_patches, title=args.condition_col,
+            loc="upper right", bbox_to_anchor=(1.25, 1.1), fontsize=8,
+        )
+
+    cluster.fig.suptitle(f"Top {len(available)} DE features", y=1.02)
+
+    out = os.path.join(args.output_dir, args.output)
+    cluster.savefig(out, dpi=300, bbox_inches="tight")
+    plt.close(cluster.fig)
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_group_time_interaction_pipeline/06_top_genes_barplot.py

@@ -0,0 +1,57 @@
+"""Create a horizontal bar plot of top differentially expressed genes/features."""
+
+import argparse
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Bar plot of top DE genes.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--top_n", type=int, default=20)
+    parser.add_argument("--output", default="top_DE_genes_barplot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    top_df = (
+        res.dropna(subset=["padj", "log2FoldChange"])
+        .sort_values("padj")
+        .head(args.top_n)
+        .copy()
+    )
+    if top_df.empty:
+        raise ValueError("No features available for plotting.")
+
+    top_df = top_df.iloc[::-1]
+    colors = ["#D62728" if v > 0 else "#1F77B4" for v in top_df["log2FoldChange"]]
+
+    plt.figure(figsize=(8, max(6, len(top_df) * 0.3)))
+    plt.barh(top_df.index, top_df["log2FoldChange"], color=colors)
+    plt.axvline(0, linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log2 fold change")
+    plt.ylabel("Feature")
+    plt.title(f"Top {args.top_n} differentially expressed features")
+
+    legend_elements = [
+        Patch(facecolor="#D62728", label="Up"),
+        Patch(facecolor="#1F77B4", label="Down"),
+    ]
+    plt.legend(handles=legend_elements, fontsize=8)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_group_time_interaction_pipeline/README.md

@@ -0,0 +1,27 @@
+# Count_DESeq2_group_time_interaction
+
+**Decision rule:** Two groups x two or more time points, group x time interaction is the primary question
+
+## Required input files
+
+`metadata.csv counts.csv [output_dir] [time_col]`
+
+## Quick start
+
+```bash
+bash run_pipeline.sh metadata.csv counts.csv [output_dir] [time_col] output/
+```
+
+## Key design notes
+
+Full factorial via combined group_time factor; numpy contrast vector for interaction term. Saves interaction_T1_to_T2.csv per adjacent window.
+
+## Standardised output columns
+
+All pipelines write `results.csv` with: `log2FoldChange`, `baseMean`, `padj`, `significant`, `direction`.
+
+## Required packages
+
+```bash
+pip install pandas numpy matplotlib seaborn scikit-learn scipy statsmodels pydeseq2
+```

src/statlens/pipelines/deseq2_group_time_interaction_pipeline/run_pipeline.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+# Count_DESeq2_group_time_interaction pipeline — group × time interaction.
+# Usage: bash run_pipeline.sh [metadata.csv] [counts.csv] [output_dir] [time_col]
+set -euo pipefail
+METADATA=${1:-metadata.csv}; COUNTS=${2:-counts.csv}; OUTPUT_DIR=${3:-output}; TIME_COL=${4:-time}
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+echo "=== Step 1: DESeq2 group×time interaction ==="
+python "$SCRIPT_DIR/01_deseq2_group_time_interaction_analysis.py" \
+    --metadata "$METADATA" --counts "$COUNTS" --time_col "$TIME_COL" --output_dir "$OUTPUT_DIR"
+
+R="$OUTPUT_DIR/results.csv"; L="$OUTPUT_DIR/log_normalized_counts.csv"; M="$OUTPUT_DIR/metadata_used_for_analysis.csv"
+echo "=== Step 2: Volcano ===" && python "$SCRIPT_DIR/02_volcano_plot.py" --results "$R" --output_dir "$OUTPUT_DIR"
+echo "=== Step 3: MA ===" && python "$SCRIPT_DIR/03_ma_plot.py" --results "$R" --output_dir "$OUTPUT_DIR"
+echo "=== Step 4: PCA ===" && python "$SCRIPT_DIR/04_pca_plot.py" --log_counts "$L" --metadata "$M" --output_dir "$OUTPUT_DIR"
+echo "=== Step 5: Heatmap ===" && python "$SCRIPT_DIR/05_heatmap_top_de_genes.py" --results "$R" --log_counts "$L" --metadata "$M" --output_dir "$OUTPUT_DIR"
+echo "=== Step 6: Barplot ===" && python "$SCRIPT_DIR/06_top_genes_barplot.py" --results "$R" --output_dir "$OUTPUT_DIR"
+echo "" && echo "Pipeline complete. Outputs: $OUTPUT_DIR"

src/statlens/pipelines/deseq2_multi_group_pipeline/01_deseq2_multi_group_analysis.py

@@ -0,0 +1,185 @@
+"""
+01_deseq2_multi_group_analysis.py
+
+Count_DESeq2_multi_group workflow: three or more independent groups in a
+cross-sectional design.
+
+Strategy:
+  1. Fit a single DESeq2 model with condition as the sole factor.
+  2. Run all pairwise comparisons (each non-reference group vs the reference).
+  3. A gene is called significant if it passes padj + |LFC| thresholds in AT
+     LEAST ONE pairwise comparison (BH correction applied jointly across all
+     comparisons × genes).
+  4. results.csv contains the per-gene minimum padj (across comparisons) and
+     the LFC from the comparison with the smallest padj.
+  5. Individual pairwise result files are also saved.
+
+Input:
+    metadata.csv  – samples × annotations; must contain a condition column
+                    with ≥ 3 groups.
+    counts.csv    – genes/features × samples; raw non-negative integer counts.
+
+Output:
+    results.csv                    (combined: min padj, representative LFC)
+    significant_genes.csv
+    pairwise_{X}_vs_{ref}.csv      (one file per non-reference group)
+    normalized_counts.csv
+    log_normalized_counts.csv
+    metadata_used_for_analysis.csv
+"""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+from pydeseq2.dds import DeseqDataSet
+from pydeseq2.ds import DeseqStats
+from statsmodels.stats.multitest import multipletests
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="DESeq2 multi-group analysis (≥3 groups).")
+    parser.add_argument("--metadata", default="metadata.csv")
+    parser.add_argument("--counts", default="counts.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--ref_level", default="control",
+                        help="Reference group for all pairwise comparisons.")
+    parser.add_argument("--min_total_count", type=int, default=10)
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    metadata = pd.read_csv(args.metadata, index_col=0)
+    counts = pd.read_csv(args.counts, index_col=0)
+
+    if args.condition_col not in metadata.columns:
+        raise ValueError(f"Metadata must contain column: {args.condition_col}")
+
+    shared = metadata.index.intersection(counts.columns)
+    if len(shared) == 0:
+        raise ValueError("No shared sample IDs between metadata and count matrix.")
+    metadata = metadata.loc[shared].copy()
+    counts = counts.loc[:, shared].copy()
+
+    metadata = metadata.dropna(subset=[args.condition_col])
+    counts = counts.loc[:, metadata.index]
+
+    groups = metadata[args.condition_col].astype(str).unique().tolist()
+    if len(groups) < 3:
+        raise ValueError(f"multi_group pipeline requires ≥3 groups; found: {groups}. "
+                         "Use the basic pipeline for two-group comparisons.")
+    if args.ref_level not in groups:
+        raise ValueError(f"Reference group '{args.ref_level}' not found in {groups}.")
+    test_groups = [g for g in sorted(groups) if g != args.ref_level]
+    print(f"Groups: ref='{args.ref_level}', test={test_groups}")
+
+    if (metadata[args.condition_col].value_counts() < 2).any():
+        raise ValueError("Each group needs at least two biological replicates.")
+
+    if (counts < 0).any().any():
+        raise ValueError("Count matrix contains negative values.")
+    if not np.all(np.equal(counts.values, np.floor(counts.values))):
+        raise ValueError("Counts must be raw integers.")
+    counts = counts.astype(int)
+
+    counts = counts.loc[counts.sum(axis=1) >= args.min_total_count].copy()
+    if counts.shape[0] == 0:
+        raise ValueError("No genes remain after low-count filtering.")
+    print(f"Genes retained after filtering: {counts.shape[0]}")
+
+    all_categories = [args.ref_level] + test_groups
+    metadata[args.condition_col] = pd.Categorical(
+        metadata[args.condition_col],
+        categories=all_categories,
+        ordered=False,
+    )
+
+    counts_t = counts.T
+    metadata = metadata.loc[counts_t.index]
+
+    dds = DeseqDataSet(
+        counts=counts_t,
+        metadata=metadata,
+        design_factors=args.condition_col,
+        refit_cooks=True,
+    )
+    dds.deseq2()
+
+    # Pairwise comparisons: each test group vs reference
+    pairwise_results = {}
+    for tg in test_groups:
+        stat_res = DeseqStats(
+            dds,
+            contrast=[args.condition_col, tg, args.ref_level],
+            alpha=args.padj_cutoff,
+        )
+        stat_res.summary()
+        coeff = f"{args.condition_col}_{tg}_vs_{args.ref_level}"
+        try:
+            stat_res.lfc_shrink(coeff=coeff)
+        except Exception:
+            pass
+        pw = stat_res.results_df.copy()
+        pw.to_csv(os.path.join(args.output_dir, f"pairwise_{tg}_vs_{args.ref_level}.csv"))
+        pairwise_results[tg] = pw
+        print(f"Pairwise {tg} vs {args.ref_level}: done.")
+
+    # Combine: re-apply BH correction jointly across all pairwise p-values
+    all_pvals = pd.concat(
+        {tg: pw["pvalue"] for tg, pw in pairwise_results.items()},
+        axis=1,
+    )
+    all_lfc = pd.concat(
+        {tg: pw["log2FoldChange"] for tg, pw in pairwise_results.items()},
+        axis=1,
+    )
+
+    # Min raw p-value per gene
+    min_pval = all_pvals.min(axis=1)
+    valid_mask = min_pval.notna()
+    global_padj = pd.Series(np.nan, index=min_pval.index)
+    _, padj_vals, _, _ = multipletests(min_pval[valid_mask], method="fdr_bh")
+    global_padj[valid_mask] = padj_vals
+
+    # Representative LFC: from the comparison with the minimum p-value per gene
+    best_comparison = all_pvals.idxmin(axis=1)
+    rep_lfc = pd.Series(
+        [all_lfc.loc[gene, best_comparison[gene]] if pd.notna(best_comparison[gene]) else np.nan
+         for gene in all_lfc.index],
+        index=all_lfc.index,
+    )
+    # baseMean from the first pairwise result (identical across comparisons)
+    base_mean = pairwise_results[test_groups[0]]["baseMean"]
+
+    res = pd.DataFrame({
+        "baseMean": base_mean,
+        "log2FoldChange": rep_lfc,
+        "padj": global_padj,
+        "best_comparison": best_comparison,
+    }).sort_values("padj")
+
+    res["significant"] = (res["padj"] < args.padj_cutoff) & (res["log2FoldChange"].abs() >= args.lfc_cutoff)
+    res["direction"] = "not_significant"
+    res.loc[(res["significant"]) & (res["log2FoldChange"] > 0), "direction"] = "up"
+    res.loc[(res["significant"]) & (res["log2FoldChange"] < 0), "direction"] = "down"
+
+    res.to_csv(os.path.join(args.output_dir, "results.csv"))
+    res[res["significant"]].to_csv(os.path.join(args.output_dir, "significant_genes.csv"))
+
+    norm = pd.DataFrame(dds.layers["normed_counts"], index=dds.obs_names, columns=dds.var_names)
+    norm.to_csv(os.path.join(args.output_dir, "normalized_counts.csv"))
+    np.log2(norm + 1).to_csv(os.path.join(args.output_dir, "log_normalized_counts.csv"))
+    metadata.to_csv(os.path.join(args.output_dir, "metadata_used_for_analysis.csv"))
+
+    print(f"Significant genes (any comparison): {int(res['significant'].sum())}")
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_multi_group_pipeline/02_volcano_plot.py

@@ -0,0 +1,66 @@
+"""Create a volcano plot from analysis results."""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create a volcano plot from analysis results.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output", default="volcano_plot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    plot_df = res.dropna(subset=["log2FoldChange", "padj"]).copy()
+    plot_df["safe_padj"] = plot_df["padj"].replace(0, np.nextafter(0, 1))
+    plot_df["neg_log10_padj"] = -np.log10(plot_df["safe_padj"])
+    plot_df["category"] = "Not significant"
+    plot_df.loc[
+        (plot_df["padj"] < args.padj_cutoff) & (plot_df["log2FoldChange"] >= args.lfc_cutoff),
+        "category",
+    ] = "Up"
+    plot_df.loc[
+        (plot_df["padj"] < args.padj_cutoff) & (plot_df["log2FoldChange"] <= -args.lfc_cutoff),
+        "category",
+    ] = "Down"
+
+    color_map = {"Not significant": "#AAAAAA", "Up": "#D62728", "Down": "#1F77B4"}
+
+    plt.figure(figsize=(7, 6))
+    for cat in ["Not significant", "Up", "Down"]:
+        sub = plot_df[plot_df["category"] == cat]
+        plt.scatter(
+            sub["log2FoldChange"], sub["neg_log10_padj"],
+            s=8, alpha=0.6,
+            label=f"{cat} (n={len(sub)})",
+            color=color_map[cat],
+        )
+
+    plt.axvline(args.lfc_cutoff, linestyle="--", linewidth=1, color="black")
+    plt.axvline(-args.lfc_cutoff, linestyle="--", linewidth=1, color="black")
+    plt.axhline(-np.log10(args.padj_cutoff), linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log2 fold change")
+    plt.ylabel("-log10 adjusted p-value")
+    plt.title("Volcano plot")
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_multi_group_pipeline/03_ma_plot.py

@@ -0,0 +1,57 @@
+"""Create an MA plot from analysis results."""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create an MA plot from analysis results.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output", default="MA_plot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    plot_df = res.dropna(subset=["baseMean", "log2FoldChange", "padj"]).copy()
+    plot_df = plot_df[plot_df["baseMean"] > 0].copy()
+    plot_df["significant"] = (
+        (plot_df["padj"] < args.padj_cutoff) &
+        (plot_df["log2FoldChange"].abs() >= args.lfc_cutoff)
+    )
+
+    plt.figure(figsize=(7, 6))
+    nonsig = plot_df[~plot_df["significant"]]
+    sig = plot_df[plot_df["significant"]]
+    plt.scatter(
+        np.log10(nonsig["baseMean"]), nonsig["log2FoldChange"],
+        s=8, alpha=0.4, label=f"Not significant (n={len(nonsig)})", color="#AAAAAA",
+    )
+    plt.scatter(
+        np.log10(sig["baseMean"]), sig["log2FoldChange"],
+        s=8, alpha=0.8, label=f"Significant (n={len(sig)})", color="#D62728",
+    )
+    plt.axhline(0, linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log10 mean expression")
+    plt.ylabel("log2 fold change")
+    plt.title("MA plot")
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_multi_group_pipeline/04_pca_plot.py

@@ -0,0 +1,64 @@
+"""Create a PCA plot from log-normalised expression / count data."""
+
+import argparse
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="PCA plot from log-normalised data.")
+    parser.add_argument("--log_counts", default="log_normalized_counts.csv")
+    parser.add_argument("--metadata", default="metadata_used_for_analysis.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--output", default="PCA_plot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    log_counts = pd.read_csv(args.log_counts, index_col=0)
+    metadata = pd.read_csv(args.metadata, index_col=0)
+    shared = log_counts.index.intersection(metadata.index)
+    log_counts = log_counts.loc[shared]
+    metadata = metadata.loc[shared]
+
+    if args.condition_col not in metadata.columns:
+        raise ValueError(f"Metadata must contain column: {args.condition_col}")
+
+    pca = PCA(n_components=2)
+    pcs = pca.fit_transform(log_counts)
+    pca_df = pd.DataFrame(pcs, index=log_counts.index, columns=["PC1", "PC2"])
+    pca_df = pca_df.join(metadata[[args.condition_col]])
+
+    plt.figure(figsize=(6, 5))
+    groups = sorted(pca_df[args.condition_col].astype(str).unique())
+    colors = plt.cm.Set1.colors
+    for i, grp in enumerate(groups):
+        sub = pca_df[pca_df[args.condition_col].astype(str) == grp]
+        plt.scatter(sub["PC1"], sub["PC2"], s=60, alpha=0.85,
+                    label=grp, color=colors[i % len(colors)],
+                    edgecolors="white", linewidths=0.5)
+
+    for sample in pca_df.index:
+        plt.annotate(sample, (pca_df.loc[sample, "PC1"], pca_df.loc[sample, "PC2"]),
+                     fontsize=6, alpha=0.7, xytext=(3, 3), textcoords="offset points")
+
+    plt.xlabel(f"PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)")
+    plt.ylabel(f"PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)")
+    plt.title("PCA")
+    plt.legend(title=args.condition_col)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_multi_group_pipeline/05_heatmap_top_de_genes.py

@@ -0,0 +1,101 @@
+"""Create a heatmap of the top differentially expressed genes/features."""
+
+import argparse
+import os
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Heatmap of top DE genes.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--log_counts", default="log_normalized_counts.csv")
+    parser.add_argument("--metadata", default="metadata_used_for_analysis.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--top_n", type=int, default=50)
+    parser.add_argument("--output", default="top_DE_genes_heatmap.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def zscore_by_gene(df):
+    means = df.mean(axis=1)
+    stds = df.std(axis=1).replace(0, 1)
+    return df.sub(means, axis=0).div(stds, axis=0)
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    log_counts = pd.read_csv(args.log_counts, index_col=0)
+    metadata = pd.read_csv(args.metadata, index_col=0)
+
+    # Select top genes: prefer significant ones
+    if "significant" in res.columns:
+        sig_res = res[res["significant"] == True].dropna(subset=["padj"]).sort_values("padj")
+    else:
+        sig_res = res.dropna(subset=["padj"]).sort_values("padj")
+
+    if len(sig_res) >= args.top_n:
+        top_genes = sig_res.head(args.top_n).index
+    elif len(sig_res) > 0:
+        print(f"Only {len(sig_res)} significant features; using all.")
+        top_genes = sig_res.index
+    else:
+        print("No significant features; falling back to top N by padj.")
+        top_genes = res.dropna(subset=["padj"]).sort_values("padj").head(args.top_n).index
+
+    available = [g for g in top_genes if g in log_counts.columns]
+    if not available:
+        raise ValueError("None of the top features are in log_normalized_counts.csv.")
+
+    shared = log_counts.index.intersection(metadata.index)
+    log_counts = log_counts.loc[shared]
+    metadata = metadata.loc[shared]
+
+    if args.condition_col not in metadata.columns:
+        col_colors = None
+        legend_patches = []
+    else:
+        groups = sorted(metadata[args.condition_col].astype(str).unique())
+        palette = [plt.cm.Set1.colors[i % 9] for i in range(len(groups))]
+        cond_pal = dict(zip(groups, palette))
+        col_colors = metadata[args.condition_col].astype(str).map(cond_pal)
+        legend_patches = [mpatches.Patch(color=cond_pal[g], label=g) for g in groups]
+
+    hm = log_counts[available].T
+    hm_z = zscore_by_gene(hm)
+
+    cluster = sns.clustermap(
+        hm_z,
+        figsize=(max(8, len(shared) * 0.4 + 4), max(10, len(available) * 0.2 + 2)),
+        col_cluster=True,
+        row_cluster=True,
+        col_colors=col_colors,
+        xticklabels=True,
+        yticklabels=True,
+        cmap="vlag",
+    )
+    plt.setp(cluster.ax_heatmap.get_xticklabels(), rotation=45, ha="right", fontsize=8)
+    plt.setp(cluster.ax_heatmap.get_yticklabels(), fontsize=7)
+
+    if legend_patches:
+        cluster.ax_heatmap.legend(
+            handles=legend_patches, title=args.condition_col,
+            loc="upper right", bbox_to_anchor=(1.25, 1.1), fontsize=8,
+        )
+
+    cluster.fig.suptitle(f"Top {len(available)} DE features", y=1.02)
+
+    out = os.path.join(args.output_dir, args.output)
+    cluster.savefig(out, dpi=300, bbox_inches="tight")
+    plt.close(cluster.fig)
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_multi_group_pipeline/06_top_genes_barplot.py

@@ -0,0 +1,57 @@
+"""Create a horizontal bar plot of top differentially expressed genes/features."""
+
+import argparse
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Bar plot of top DE genes.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--top_n", type=int, default=20)
+    parser.add_argument("--output", default="top_DE_genes_barplot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    top_df = (
+        res.dropna(subset=["padj", "log2FoldChange"])
+        .sort_values("padj")
+        .head(args.top_n)
+        .copy()
+    )
+    if top_df.empty:
+        raise ValueError("No features available for plotting.")
+
+    top_df = top_df.iloc[::-1]
+    colors = ["#D62728" if v > 0 else "#1F77B4" for v in top_df["log2FoldChange"]]
+
+    plt.figure(figsize=(8, max(6, len(top_df) * 0.3)))
+    plt.barh(top_df.index, top_df["log2FoldChange"], color=colors)
+    plt.axvline(0, linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log2 fold change")
+    plt.ylabel("Feature")
+    plt.title(f"Top {args.top_n} differentially expressed features")
+
+    legend_elements = [
+        Patch(facecolor="#D62728", label="Up"),
+        Patch(facecolor="#1F77B4", label="Down"),
+    ]
+    plt.legend(handles=legend_elements, fontsize=8)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_multi_group_pipeline/README.md

@@ -0,0 +1,27 @@
+# Count_DESeq2_multi_group
+
+**Decision rule:** Three or more independent groups in a cross-sectional design
+
+## Required input files
+
+`metadata.csv counts.csv [output_dir] [ref_level]`
+
+## Quick start
+
+```bash
+bash run_pipeline.sh metadata.csv counts.csv [output_dir] [ref_level] output/
+```
+
+## Key design notes
+
+Pairwise comparisons (each group vs ref); joint BH FDR; saves pairwise_X_vs_ref.csv per comparison.
+
+## Standardised output columns
+
+All pipelines write `results.csv` with: `log2FoldChange`, `baseMean`, `padj`, `significant`, `direction`.
+
+## Required packages
+
+```bash
+pip install pandas numpy matplotlib seaborn scikit-learn scipy statsmodels pydeseq2
+```

src/statlens/pipelines/deseq2_multi_group_pipeline/run_pipeline.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+# Count_DESeq2_multi_group pipeline — ≥3 independent groups.
+# Usage: bash run_pipeline.sh [metadata.csv] [counts.csv] [output_dir] [ref_level]
+set -euo pipefail
+METADATA=${1:-metadata.csv}; COUNTS=${2:-counts.csv}; OUTPUT_DIR=${3:-output}; REF_LEVEL=${4:-control}
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+echo "=== Step 1: DESeq2 multi-group ==="
+python "$SCRIPT_DIR/01_deseq2_multi_group_analysis.py" \
+    --metadata "$METADATA" --counts "$COUNTS" --ref_level "$REF_LEVEL" --output_dir "$OUTPUT_DIR"
+
+R="$OUTPUT_DIR/results.csv"; L="$OUTPUT_DIR/log_normalized_counts.csv"; M="$OUTPUT_DIR/metadata_used_for_analysis.csv"
+echo "=== Step 2: Volcano ===" && python "$SCRIPT_DIR/02_volcano_plot.py" --results "$R" --output_dir "$OUTPUT_DIR"
+echo "=== Step 3: MA ===" && python "$SCRIPT_DIR/03_ma_plot.py" --results "$R" --output_dir "$OUTPUT_DIR"
+echo "=== Step 4: PCA ===" && python "$SCRIPT_DIR/04_pca_plot.py" --log_counts "$L" --metadata "$M" --output_dir "$OUTPUT_DIR"
+echo "=== Step 5: Heatmap ===" && python "$SCRIPT_DIR/05_heatmap_top_de_genes.py" --results "$R" --log_counts "$L" --metadata "$M" --output_dir "$OUTPUT_DIR"
+echo "=== Step 6: Barplot ===" && python "$SCRIPT_DIR/06_top_genes_barplot.py" --results "$R" --output_dir "$OUTPUT_DIR"
+echo "" && echo "Pipeline complete. Outputs: $OUTPUT_DIR"

src/statlens/pipelines/deseq2_paired_pipeline/01_deseq2_paired_analysis.py

@@ -0,0 +1,148 @@
+"""
+01_deseq2_paired_analysis.py
+
+Count_DESeq2_paired_or_repeated workflow: each individual contributes more than
+one sample (e.g. tumour/normal pairs, pre/post treatment, repeated time points
+from the same patient).
+
+Design formula: ~ subject + condition
+
+The subject term absorbs between-individual variation; the condition coefficient
+captures the within-individual treatment effect.
+
+Requirements:
+  - Every subject must appear in at least two conditions.
+  - The condition column must still have exactly two levels.
+
+Input:
+    metadata.csv  – samples × annotations; must contain condition and subject columns.
+    counts.csv    – genes/features × samples; raw non-negative integer counts.
+
+Output:
+    results.csv
+    significant_genes.csv
+    normalized_counts.csv
+    log_normalized_counts.csv
+    metadata_used_for_analysis.csv
+"""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+from pydeseq2.dds import DeseqDataSet
+from pydeseq2.ds import DeseqStats
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="DESeq2 paired / repeated-measures analysis.")
+    parser.add_argument("--metadata", default="metadata.csv")
+    parser.add_argument("--counts", default="counts.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--subject_col", default="subject",
+                        help="Column in metadata identifying each individual/biological unit.")
+    parser.add_argument("--ref_level", default="control")
+    parser.add_argument("--test_level", default="case")
+    parser.add_argument("--min_total_count", type=int, default=10)
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    metadata = pd.read_csv(args.metadata, index_col=0)
+    counts = pd.read_csv(args.counts, index_col=0)
+
+    for col in [args.condition_col, args.subject_col]:
+        if col not in metadata.columns:
+            raise ValueError(f"Metadata must contain column: {col}")
+
+    shared = metadata.index.intersection(counts.columns)
+    if len(shared) == 0:
+        raise ValueError("No shared sample IDs between metadata and count matrix.")
+    metadata = metadata.loc[shared].copy()
+    counts = counts.loc[:, shared].copy()
+
+    metadata = metadata.dropna(subset=[args.condition_col, args.subject_col])
+    counts = counts.loc[:, metadata.index]
+
+    observed = set(metadata[args.condition_col].astype(str).unique())
+    expected = {args.ref_level, args.test_level}
+    if observed != expected:
+        raise ValueError(f"Requires exactly two condition groups {expected}; observed {observed}.")
+
+    # Warn if any subject contributes only one condition (will reduce power)
+    subject_cond = metadata.groupby(args.subject_col)[args.condition_col].nunique()
+    unpaired = subject_cond[subject_cond < 2]
+    if len(unpaired) > 0:
+        print(f"Warning: {len(unpaired)} subject(s) appear in only one condition: "
+              f"{unpaired.index.tolist()}. They are kept but reduce blocking efficiency.")
+    print(f"Subjects: {metadata[args.subject_col].nunique()}")
+
+    if (counts < 0).any().any():
+        raise ValueError("Count matrix contains negative values.")
+    if not np.all(np.equal(counts.values, np.floor(counts.values))):
+        raise ValueError("Counts must be raw integers.")
+    counts = counts.astype(int)
+
+    counts = counts.loc[counts.sum(axis=1) >= args.min_total_count].copy()
+    if counts.shape[0] == 0:
+        raise ValueError("No genes remain after low-count filtering.")
+    print(f"Genes retained after filtering: {counts.shape[0]}")
+
+    metadata[args.condition_col] = pd.Categorical(
+        metadata[args.condition_col],
+        categories=[args.ref_level, args.test_level],
+        ordered=False,
+    )
+
+    counts_t = counts.T
+    metadata = metadata.loc[counts_t.index]
+
+    # Design: ~ subject + condition
+    dds = DeseqDataSet(
+        counts=counts_t,
+        metadata=metadata,
+        design_factors=[args.subject_col, args.condition_col],
+        refit_cooks=True,
+    )
+    dds.deseq2()
+
+    stat_res = DeseqStats(
+        dds,
+        contrast=[args.condition_col, args.test_level, args.ref_level],
+        alpha=args.padj_cutoff,
+    )
+    stat_res.summary()
+
+    coeff = f"{args.condition_col}_{args.test_level}_vs_{args.ref_level}"
+    try:
+        stat_res.lfc_shrink(coeff=coeff)
+        print(f"LFC shrinkage applied (coeff: {coeff}).")
+    except Exception as e:
+        print(f"Warning: LFC shrinkage failed ({e}). Using unshrunken LFC.")
+
+    res = stat_res.results_df.copy().sort_values("padj")
+    res["significant"] = (res["padj"] < args.padj_cutoff) & (res["log2FoldChange"].abs() >= args.lfc_cutoff)
+    res["direction"] = "not_significant"
+    res.loc[(res["significant"]) & (res["log2FoldChange"] > 0), "direction"] = f"up_in_{args.test_level}"
+    res.loc[(res["significant"]) & (res["log2FoldChange"] < 0), "direction"] = f"down_in_{args.test_level}"
+
+    res.to_csv(os.path.join(args.output_dir, "results.csv"))
+    res[res["significant"]].to_csv(os.path.join(args.output_dir, "significant_genes.csv"))
+
+    norm = pd.DataFrame(dds.layers["normed_counts"], index=dds.obs_names, columns=dds.var_names)
+    norm.to_csv(os.path.join(args.output_dir, "normalized_counts.csv"))
+    np.log2(norm + 1).to_csv(os.path.join(args.output_dir, "log_normalized_counts.csv"))
+    metadata.to_csv(os.path.join(args.output_dir, "metadata_used_for_analysis.csv"))
+
+    print(f"Significant genes: {int(res['significant'].sum())}")
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_paired_pipeline/02_volcano_plot.py

@@ -0,0 +1,66 @@
+"""Create a volcano plot from analysis results."""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create a volcano plot from analysis results.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output", default="volcano_plot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    plot_df = res.dropna(subset=["log2FoldChange", "padj"]).copy()
+    plot_df["safe_padj"] = plot_df["padj"].replace(0, np.nextafter(0, 1))
+    plot_df["neg_log10_padj"] = -np.log10(plot_df["safe_padj"])
+    plot_df["category"] = "Not significant"
+    plot_df.loc[
+        (plot_df["padj"] < args.padj_cutoff) & (plot_df["log2FoldChange"] >= args.lfc_cutoff),
+        "category",
+    ] = "Up"
+    plot_df.loc[
+        (plot_df["padj"] < args.padj_cutoff) & (plot_df["log2FoldChange"] <= -args.lfc_cutoff),
+        "category",
+    ] = "Down"
+
+    color_map = {"Not significant": "#AAAAAA", "Up": "#D62728", "Down": "#1F77B4"}
+
+    plt.figure(figsize=(7, 6))
+    for cat in ["Not significant", "Up", "Down"]:
+        sub = plot_df[plot_df["category"] == cat]
+        plt.scatter(
+            sub["log2FoldChange"], sub["neg_log10_padj"],
+            s=8, alpha=0.6,
+            label=f"{cat} (n={len(sub)})",
+            color=color_map[cat],
+        )
+
+    plt.axvline(args.lfc_cutoff, linestyle="--", linewidth=1, color="black")
+    plt.axvline(-args.lfc_cutoff, linestyle="--", linewidth=1, color="black")
+    plt.axhline(-np.log10(args.padj_cutoff), linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log2 fold change")
+    plt.ylabel("-log10 adjusted p-value")
+    plt.title("Volcano plot")
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_paired_pipeline/03_ma_plot.py

@@ -0,0 +1,57 @@
+"""Create an MA plot from analysis results."""
+
+import argparse
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Create an MA plot from analysis results.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--padj_cutoff", type=float, default=0.05)
+    parser.add_argument("--lfc_cutoff", type=float, default=1.0)
+    parser.add_argument("--output", default="MA_plot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    plot_df = res.dropna(subset=["baseMean", "log2FoldChange", "padj"]).copy()
+    plot_df = plot_df[plot_df["baseMean"] > 0].copy()
+    plot_df["significant"] = (
+        (plot_df["padj"] < args.padj_cutoff) &
+        (plot_df["log2FoldChange"].abs() >= args.lfc_cutoff)
+    )
+
+    plt.figure(figsize=(7, 6))
+    nonsig = plot_df[~plot_df["significant"]]
+    sig = plot_df[plot_df["significant"]]
+    plt.scatter(
+        np.log10(nonsig["baseMean"]), nonsig["log2FoldChange"],
+        s=8, alpha=0.4, label=f"Not significant (n={len(nonsig)})", color="#AAAAAA",
+    )
+    plt.scatter(
+        np.log10(sig["baseMean"]), sig["log2FoldChange"],
+        s=8, alpha=0.8, label=f"Significant (n={len(sig)})", color="#D62728",
+    )
+    plt.axhline(0, linestyle="--", linewidth=1, color="black")
+    plt.xlabel("log10 mean expression")
+    plt.ylabel("log2 fold change")
+    plt.title("MA plot")
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_paired_pipeline/04_pca_plot.py

@@ -0,0 +1,64 @@
+"""Create a PCA plot from log-normalised expression / count data."""
+
+import argparse
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="PCA plot from log-normalised data.")
+    parser.add_argument("--log_counts", default="log_normalized_counts.csv")
+    parser.add_argument("--metadata", default="metadata_used_for_analysis.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--output", default="PCA_plot.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    log_counts = pd.read_csv(args.log_counts, index_col=0)
+    metadata = pd.read_csv(args.metadata, index_col=0)
+    shared = log_counts.index.intersection(metadata.index)
+    log_counts = log_counts.loc[shared]
+    metadata = metadata.loc[shared]
+
+    if args.condition_col not in metadata.columns:
+        raise ValueError(f"Metadata must contain column: {args.condition_col}")
+
+    pca = PCA(n_components=2)
+    pcs = pca.fit_transform(log_counts)
+    pca_df = pd.DataFrame(pcs, index=log_counts.index, columns=["PC1", "PC2"])
+    pca_df = pca_df.join(metadata[[args.condition_col]])
+
+    plt.figure(figsize=(6, 5))
+    groups = sorted(pca_df[args.condition_col].astype(str).unique())
+    colors = plt.cm.Set1.colors
+    for i, grp in enumerate(groups):
+        sub = pca_df[pca_df[args.condition_col].astype(str) == grp]
+        plt.scatter(sub["PC1"], sub["PC2"], s=60, alpha=0.85,
+                    label=grp, color=colors[i % len(colors)],
+                    edgecolors="white", linewidths=0.5)
+
+    for sample in pca_df.index:
+        plt.annotate(sample, (pca_df.loc[sample, "PC1"], pca_df.loc[sample, "PC2"]),
+                     fontsize=6, alpha=0.7, xytext=(3, 3), textcoords="offset points")
+
+    plt.xlabel(f"PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)")
+    plt.ylabel(f"PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)")
+    plt.title("PCA")
+    plt.legend(title=args.condition_col)
+    plt.tight_layout()
+
+    out = os.path.join(args.output_dir, args.output)
+    plt.savefig(out, dpi=300)
+    plt.close()
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()

src/statlens/pipelines/deseq2_paired_pipeline/05_heatmap_top_de_genes.py

@@ -0,0 +1,101 @@
+"""Create a heatmap of the top differentially expressed genes/features."""
+
+import argparse
+import os
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Heatmap of top DE genes.")
+    parser.add_argument("--results", default="results.csv")
+    parser.add_argument("--log_counts", default="log_normalized_counts.csv")
+    parser.add_argument("--metadata", default="metadata_used_for_analysis.csv")
+    parser.add_argument("--condition_col", default="condition")
+    parser.add_argument("--top_n", type=int, default=50)
+    parser.add_argument("--output", default="top_DE_genes_heatmap.png")
+    parser.add_argument("--output_dir", default=".")
+    return parser.parse_args()
+
+
+def zscore_by_gene(df):
+    means = df.mean(axis=1)
+    stds = df.std(axis=1).replace(0, 1)
+    return df.sub(means, axis=0).div(stds, axis=0)
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    res = pd.read_csv(args.results, index_col=0)
+    log_counts = pd.read_csv(args.log_counts, index_col=0)
+    metadata = pd.read_csv(args.metadata, index_col=0)
+
+    # Select top genes: prefer significant ones
+    if "significant" in res.columns:
+        sig_res = res[res["significant"] == True].dropna(subset=["padj"]).sort_values("padj")
+    else:
+        sig_res = res.dropna(subset=["padj"]).sort_values("padj")
+
+    if len(sig_res) >= args.top_n:
+        top_genes = sig_res.head(args.top_n).index
+    elif len(sig_res) > 0:
+        print(f"Only {len(sig_res)} significant features; using all.")
+        top_genes = sig_res.index
+    else:
+        print("No significant features; falling back to top N by padj.")
+        top_genes = res.dropna(subset=["padj"]).sort_values("padj").head(args.top_n).index
+
+    available = [g for g in top_genes if g in log_counts.columns]
+    if not available:
+        raise ValueError("None of the top features are in log_normalized_counts.csv.")
+
+    shared = log_counts.index.intersection(metadata.index)
+    log_counts = log_counts.loc[shared]
+    metadata = metadata.loc[shared]
+
+    if args.condition_col not in metadata.columns:
+        col_colors = None
+        legend_patches = []
+    else:
+        groups = sorted(metadata[args.condition_col].astype(str).unique())
+        palette = [plt.cm.Set1.colors[i % 9] for i in range(len(groups))]
+        cond_pal = dict(zip(groups, palette))
+        col_colors = metadata[args.condition_col].astype(str).map(cond_pal)
+        legend_patches = [mpatches.Patch(color=cond_pal[g], label=g) for g in groups]
+
+    hm = log_counts[available].T
+    hm_z = zscore_by_gene(hm)
+
+    cluster = sns.clustermap(
+        hm_z,
+        figsize=(max(8, len(shared) * 0.4 + 4), max(10, len(available) * 0.2 + 2)),
+        col_cluster=True,
+        row_cluster=True,
+        col_colors=col_colors,
+        xticklabels=True,
+        yticklabels=True,
+        cmap="vlag",
+    )
+    plt.setp(cluster.ax_heatmap.get_xticklabels(), rotation=45, ha="right", fontsize=8)
+    plt.setp(cluster.ax_heatmap.get_yticklabels(), fontsize=7)
+
+    if legend_patches:
+        cluster.ax_heatmap.legend(
+            handles=legend_patches, title=args.condition_col,
+            loc="upper right", bbox_to_anchor=(1.25, 1.1), fontsize=8,
+        )
+
+    cluster.fig.suptitle(f"Top {len(available)} DE features", y=1.02)
+
+    out = os.path.join(args.output_dir, args.output)
+    cluster.savefig(out, dpi=300, bbox_inches="tight")
+    plt.close(cluster.fig)
+    print(f"Saved: {out}")
+
+
+if __name__ == "__main__":
+    main()