diff --git a/.claude/skills/perturbNMF-runner/references/02-evaluation.md b/.claude/skills/perturbNMF-runner/references/02-evaluation.md
index 89f8ce8..c0eef7c 100644
--- a/.claude/skills/perturbNMF-runner/references/02-evaluation.md
+++ b/.claude/skills/perturbNMF-runner/references/02-evaluation.md
@@ -37,7 +37,8 @@ Reconstruction error and stability are computed automatically.
 | `--guide_annotation_path` | None | TSV with a `targeting` column to identify non-targeting controls (alternative to `--guide_annotation_key`) |
 | `--gene_names_key` | `symbol` | Column in data_guide["rna"].var with gene names |
 | `--FDR_method` | `StoreyQ` | FDR correction: `StoreyQ` or `BH` |
-| `--organism` | `human` | Species for enrichment |
+| `--organism` | `human` | Species for enrichment (`human` or `mouse`; mouse genes are mapped to human orthologs) |
+| `--ortholog_table` | None | Mouse-human ortholog TSV; default MGI + Ensembl, cached in `Resources/` |
 | `--n_top` | `300` | Top genes for enrichment tests |
 | `--guide_annotation_key` | `["non-targeting"]` | Non-targeting guide identifiers (accepts multiple values) |
 | `--use_cache` | flag | Load enrichr gene set libraries from cached JSON in `Resources/` instead of downloading; falls back to download + cache on miss |
diff --git a/.claude/skills/perturbNMF-runner/references/04-visualization.md b/.claude/skills/perturbNMF-runner/references/04-visualization.md
index ddc50c0..b3dd193 100644
--- a/.claude/skills/perturbNMF-runner/references/04-visualization.md
+++ b/.claude/skills/perturbNMF-runner/references/04-visualization.md
@@ -29,6 +29,8 @@
 | `--selected_k` | None | K value to highlight with a red dashed line |
 | `--stability_file` | None | Pre-computed stability/error TSV/NPZ (bypasses `cnmf.consensus()`; for torch-cNMF runs) |
 | `--run_program_dotplot` | off | Enable per-(K, sel_thresh) program dotplots (default off; requires the inference h5mu artifact per K/threshold) |
+| `--no_trait` | off | Skip trait (GWAS) enrichment inputs and plots (non-human data) |
+| `--no_perturbation` | off | Skip perturbation inputs and regulator plots (non-perturbed data, no guides) |
 
 Enrichment file patterns use `{k}` placeholder (and `{sample}` for perturbation). See `references/parameter-catalog.md` Section 4 for all optional params.
 
diff --git a/.claude/skills/perturbNMF-runner/references/05-annotation-summary.md b/.claude/skills/perturbNMF-runner/references/05-annotation-summary.md
index c8c0c18..d0634ca 100644
--- a/.claude/skills/perturbNMF-runner/references/05-annotation-summary.md
+++ b/.claude/skills/perturbNMF-runner/references/05-annotation-summary.md
@@ -120,6 +120,7 @@ other stage (no notebook required). Submit one job per K to cover multiple K val
 | `--perturbation_file_name` | `perturbation_association_results` | Perturbation file stem (e.g. `CRT`) |
 | `--effect_size` | `log2FC` | Effect-size column (e.g. `approx_log2FC`) |
 | `--gene_names_key` | `symbol` | var column with gene symbols |
+| `--organism` | `human` | Species of the data; `mouse` annotates genes with their human orthologs' summaries |
 | `--non_targeting_key` | `non-targeting` | Negative-control target label(s) |
 
 See `references/parameter-catalog.md` (Section 12) for the full flag list, including
diff --git a/.claude/skills/perturbNMF-runner/references/parameter-catalog.md b/.claude/skills/perturbNMF-runner/references/parameter-catalog.md
index 8d5e58d..4d8b1b0 100644
--- a/.claude/skills/perturbNMF-runner/references/parameter-catalog.md
+++ b/.claude/skills/perturbNMF-runner/references/parameter-catalog.md
@@ -219,7 +219,8 @@ Same as sk-cNMF except:
 | `--gwas_data_path` | str | None | Path to GWAS data (required only for trait enrichment) |
 | `--X_normalized_path` | str | None | Normalized counts h5ad (needed for explained variance). Path pattern: `<out_dir>/<run_name>/Inference/cnmf_tmp/Inference.norm_counts.h5ad` (prefix is always `Inference`, not the run_name) |
 | `--guide_annotation_path` | str | None | Guide annotation TSV |
-| `--organism` | str | `human` | Species for enrichment |
+| `--organism` | str | `human` | Species for enrichment (`human` or `mouse`; mouse genes are mapped to human orthologs) |
+| `--ortholog_table` | str | None | Mouse-human ortholog TSV; default MGI + Ensembl, cached in `Resources/` |
 | `--FDR_method` | str | `StoreyQ` | FDR method: `StoreyQ` or `BH` |
 | `--n_top` | int | `300` | Top genes for enrichment tests |
 | `--gene_names_key` | str | `symbol` | Column in data_guide["rna"].var with gene names |
@@ -271,6 +272,8 @@ Same as sk-cNMF except:
 | `--variance_col` | str | `Total` | No | Variance column name |
 | `--stability_file` | str | None | No | Pre-computed stability/error file (TSV or NPZ) |
 | `--run_program_dotplot` | flag | False | No | Enable per-(K, sel_thresh) program dotplots; requires `{output_dir}/{run_name}/Inference/adata/cNMF_{K}_{thresh}.h5mu` to exist for each pair |
+| `--no_trait` | flag | False | No | Skip trait (GWAS) enrichment files and plots (non-human data) |
+| `--no_perturbation` | flag | False | No | Skip perturbation files and regulator plots (non-perturbed data, no guides) |
 
 ---
 
@@ -616,6 +619,8 @@ cover multiple K values.
 | `--save_path` | str | `{out_dir}/{run_name}/Interpretation/Summary_table/{K}_{thresh}` | Output dir for the `.xlsx`, TSVs, and specificity/correlation/KD sidecars |
 | `--mdata_path` | str | `{out_dir}/{run_name}/Inference/adata/cNMF_{K}_{thresh}.h5mu` | Input MuData |
 | `--num_gene` | int | `300` | Top genes per program / per enriched term to keep |
+| `--organism` | str | `human` | Species of the data; with `mouse`, each gene gets the MyGene summaries of its human orthologs (listed in a `Human ortholog` column) |
+| `--ortholog_table` | str | None | Mouse-human ortholog TSV; default MGI + Ensembl, cached in `Resources/` |
 | `--perturbation_file_name` | str | `perturbation_association_results` | Perturbation result file stem (between `{K}_` and `_{Condition}.txt`); e.g. `CRT` |
 | `--Conditions` | str (nargs=\*) | all labels found in the data | Condition labels (values of obs[categorical_key]); `--Sample` is a deprecated alias |
 | `--effect_size` | str | `log2FC` | Effect-size column in perturbation files (e.g. `approx_log2FC`) |
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5d6c4e0..c2a2ab7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -25,10 +25,29 @@ Entries marked ⚠️ change pipeline output — re-run affected analyses.
 - Annotation runners locate their scripts via `PIPELINE_ROOT` (fixing paths into a
   nonexistent `AGeneTic/` folder); `gpe_export.py` reads the GeneProgramExplorer
   location from `GPE_DIR`.
+- ⚠️ Mouse data (`--organism mouse`): genes are matched to the human gene-set
+  libraries (GO, Reactome) through curated mouse-human orthologs (MGI + Ensembl)
+  instead of upper-cased symbols. Mouse genes without a human ortholog are left
+  out of enrichment. Re-run gene set and GO enrichment for mouse datasets.
+- `--organism` accepts only `human` or `mouse` (case-insensitive) in evaluation
+  and the Excel summary. Trait (GWAS) enrichment is skipped, with a message, for
+  mouse data.
+- `ensure_umap` (program and perturbed-gene plots) reuses embeddings stored as
+  `obsm['UMAP']` / `obsm['PCA']` (e.g. Seurat conversions) instead of computing
+  a new UMAP.
 
 ### Added
 - `tools/check_no_lab_specific_content.py` guard (CI + pre-commit) that blocks
   lab/project-specific content in this public repo.
+- Support for scRNA-seq data without perturbations (no guide information):
+  K-selection `--no_perturbation` / `--no_trait`; program plots and the Excel
+  summary run without `uns['guide_targets']` (the Targets Summary sheet is
+  skipped); evaluation stops early when `--Perform_perturbation` is requested
+  without guides. See the README section "Non-perturbed and mouse data".
+- Mouse-human ortholog table (`src/Stage2_Evaluation/A_Metrics/src/orthologs.py`),
+  downloaded once and cached in `src/Stage2_Evaluation/Resources/`, or supplied
+  with `--ortholog_table`. With `--organism mouse`, the Excel summary annotates
+  genes with their human orthologs' summaries in a new `Human ortholog` column.
 
 ## [0.1.1] - 2026-08-10
 
diff --git a/README.md b/README.md
index 8491c7a..930df55 100644
--- a/README.md
+++ b/README.md
@@ -87,6 +87,29 @@ See [`src/Stage2_Evaluation/B_Calibration/README.md`](src/Stage2_Evaluation/B_Ca
 
 See [`src/Stage3_Interpretation/README.md`](src/Stage3_Interpretation/README.md) for detailed parameters and output format.
 
+## Non-perturbed and mouse data
+
+The pipeline also runs on scRNA-seq data without perturbations (no guide information in the
+input) and on mouse data. Per stage:
+
+| Stage | Non-perturbed data (no guides) | Mouse data |
+|---|---|---|
+| 1. Inference | Leave out the `--guide_*` options | `--species mouse` |
+| 2a. Metrics | Leave out `--Perform_perturbation` and `--guide_annotation_path` | `--organism mouse`: genes are mapped to human orthologs for gene set and GO enrichment. Trait (GWAS) enrichment is human-only and is skipped with a message if requested |
+| 2b. Calibration | Not applicable (needs non-targeting guides); skip | No change |
+| 3a. K-selection plots | `--no_perturbation` | `--no_trait` |
+| 3a. Program plots | Leave out `--perturb_path_base` and use `--output_format PDF` or `SVG` (HTML requires `--perturb_path_base`); perturbed-gene annotations are left empty | No change |
+| 3a. Perturbed-gene plots | Not applicable; skip | No change |
+| 3b. Excel summary | The Targets Summary sheet is skipped automatically | `--organism mouse`: genes are annotated with their human orthologs' summaries |
+| 3c. Annotation | No change | `--taxid 10090` (NCBI, UniProt, OmniPath search), `--species mouse` (MyGene search), `--organism mouse` (program extraction) |
+
+Mouse genes are matched to human gene-set libraries and gene summaries through curated
+orthologs (union of MGI and Ensembl), not by upper-casing symbols. The table is downloaded
+on first use and cached in `src/Stage2_Evaluation/Resources/orthologs_mouse_human.tsv`;
+pass `--ortholog_table` to use a fixed copy. Mouse genes without a human ortholog are left
+out of enrichment, and a gene with several human orthologs contributes to all of them.
+Custom gene sets used with mouse data must therefore use human gene symbols.
+
 ## Claude Code Skills
 
 This repo ships four Claude Code skills under `.claude/skills/` for guided pipeline execution, validation, and maintenance. Open the linked `SKILL.md` for trigger phrases, detailed usage, and helper script references.
diff --git a/src/Stage2_Evaluation/A_Metrics/README.md b/src/Stage2_Evaluation/A_Metrics/README.md
index 301f502..61927cf 100644
--- a/src/Stage2_Evaluation/A_Metrics/README.md
+++ b/src/Stage2_Evaluation/A_Metrics/README.md
@@ -40,7 +40,8 @@ The evaluation pipeline tests cNMF programs against various criteria to assess t
 | X_normalized_path | str | None | Path to normalized cell x gene matrix (.h5ad), required for explained variance |
 | guide_annotation_path | str | None | Path to tab-separated guide annotation file with "targeting" column |
 | gwas_data_path | str | None | Path to GWAS data file for trait enrichment (required when --Perform_trait is set) |
-| organism | str | "human" | Organism/species for enrichment analysis |
+| organism | str | "human" | Organism/species for enrichment analysis (`human` or `mouse`; mouse genes are mapped to human orthologs to match the human gene-set libraries) |
+| ortholog_table | str | None | For mouse: mouse-human ortholog TSV (columns `mouse_symbol`, `mouse_ensembl`, `human_symbol`, `source`). Default: MGI + Ensembl table, downloaded once and cached in `src/Stage2_Evaluation/Resources/` |
 | FDR_method | str | "StoreyQ" | FDR correction method for perturbation association |
 | n_top | int | 300 | Number of top loaded genes to use for enrichment tests |
 | guide_annotation_key | list of str | ["non-targeting"] | Name(s) of non-targeting guide target labels |
diff --git a/src/Stage2_Evaluation/A_Metrics/Slurm_Version/cNMF_evaluation_pipeline.py b/src/Stage2_Evaluation/A_Metrics/Slurm_Version/cNMF_evaluation_pipeline.py
index d41296c..43dbfc9 100644
--- a/src/Stage2_Evaluation/A_Metrics/Slurm_Version/cNMF_evaluation_pipeline.py
+++ b/src/Stage2_Evaluation/A_Metrics/Slurm_Version/cNMF_evaluation_pipeline.py
@@ -47,6 +47,23 @@ def _read_samples_from_h5mu(h5mu_path, data_key, categorical_key):
         return None
 
 
+def _h5mu_has_uns_key(h5mu_path, mod_key, uns_key):
+    """True if mod/<mod_key>/uns/<uns_key> exists in the h5mu; None if unreadable.
+
+    Raises ValueError if the modality itself is missing (e.g. a wrong --prog_key).
+    """
+    try:
+        f = h5py.File(h5mu_path, "r")
+    except OSError:
+        return None
+    with f:
+        if f"mod/{mod_key}" not in f:
+            raise ValueError(
+                f"Modality '{mod_key}' not found in {h5mu_path}; check --prog_key."
+            )
+        return f"mod/{mod_key}/uns/{uns_key}" in f
+
+
 # Make the repo's src/ importable regardless of the current working directory
 from pathlib import Path
 
@@ -203,10 +220,20 @@ def main():
 
     parser.add_argument(
         "--organism",
-        help="Organism/species for enrichment analysis (default: human)",
-        type=str,
+        help="Organism/species for enrichment analysis: human or mouse (default: human)",
+        type=str.lower,
+        choices=["human", "mouse"],
         default="human",
     )
+    parser.add_argument(
+        "--ortholog_table",
+        type=str,
+        default=None,
+        help="For --organism mouse: TSV of mouse-human orthologs used to match genes "
+        "to the human gene-set libraries (columns: mouse_symbol, mouse_ensembl, "
+        "human_symbol, source). Default: MGI + Ensembl table, downloaded once and "
+        "cached in src/Stage2_Evaluation/Resources/",
+    )
     parser.add_argument(
         "--FDR_method",
         help="Method for FDR correction in perturbation association (default: StoreyQ)",
@@ -235,6 +262,15 @@ def main():
 
     args = parser.parse_args()
 
+    # The bundled GWAS resource (OpenTargets L2G) is human-only.
+    if args.Perform_trait and args.organism != "human":
+        print(
+            f"--organism {args.organism}: skipping trait (GWAS) enrichment, which is "
+            "only available for human data. Pass --no_trait to K-selection plotting.",
+            flush=True,
+        )
+        args.Perform_trait = False
+
     if args.Perform_trait and args.gwas_data_path is None:
         parser.error("--gwas_data_path is required when --Perform_trait is set")
 
@@ -284,6 +320,25 @@ def main():
     else:
         reference_targets = args.guide_annotation_key
 
+    # Check for guide information before computing any metric.
+    if args.Perform_perturbation:
+        for sel_thresh in args.sel_threshs:
+            for k in args.K:
+                h5mu_path = (
+                    f"{args.out_dir}/{args.run_name}/Inference/adata/"
+                    f"cNMF_{k}_{str(sel_thresh).replace('.', '_')}.h5mu"
+                )
+                if (
+                    _h5mu_has_uns_key(h5mu_path, args.prog_key, args.guide_targets_key)
+                    is False
+                ):
+                    raise ValueError(
+                        f"--Perform_perturbation needs guide information, but uns['"
+                        f"{args.guide_targets_key}'] is missing from '{args.prog_key}' "
+                        f"in {h5mu_path}. For non-perturbed data, drop "
+                        "--Perform_perturbation."
+                    )
+
     for sel_thresh in args.sel_threshs:
         for k in args.K:
             output_folder = f"{args.out_dir}/{args.run_name}/Evaluation/{k}_{str(sel_thresh).replace('.', '_')}"
@@ -441,6 +496,7 @@ def main():
                         data_key=args.data_key,
                         prog_name=None,
                         organism=args.organism,
+                        ortholog_table=args.ortholog_table,
                         library="Reactome_2022",
                         method="fisher",
                         database="enrichr",
@@ -461,6 +517,7 @@ def main():
                         data_key=args.data_key,
                         prog_name=None,
                         organism=args.organism,
+                        ortholog_table=args.ortholog_table,
                         library="GO_Biological_Process_2023",
                         method="fisher",
                         database="enrichr",
diff --git a/src/Stage2_Evaluation/A_Metrics/src/enrichment_geneset.py b/src/Stage2_Evaluation/A_Metrics/src/enrichment_geneset.py
index 209dc5c..e63a66a 100644
--- a/src/Stage2_Evaluation/A_Metrics/src/enrichment_geneset.py
+++ b/src/Stage2_Evaluation/A_Metrics/src/enrichment_geneset.py
@@ -12,6 +12,8 @@
 from gseapy import Biomart, Msigdb
 from tqdm.auto import tqdm
 
+from .orthologs import collapse_to_human, load_ortholog_table
+
 logging.basicConfig(level=logging.INFO)
 
 _RESOURCES_DIR = os.path.join(
@@ -78,6 +80,18 @@ def get_idconversion(var_names, organism="human", chunk_size=100):
     return gene_names
 
 
+def _mouse_gene_ids(mdata, prog_key, data_key, gene_names_key):
+    """Mouse symbols or Ensembl IDs for the loadings columns, unchanged in case."""
+    if gene_names_key is not None and gene_names_key in mdata[data_key].var.columns:
+        genes = mdata[data_key].var[gene_names_key].astype(str).tolist()
+    elif "var_names" in mdata[prog_key].uns.keys():
+        genes = [str(v) for v in mdata[prog_key].uns["var_names"]]
+    else:
+        genes = [str(v) for v in mdata[data_key].var_names]
+    # "Symbol:ENSMUSG..." style names: keep the symbol
+    return [g.split(":", 1)[0] if ":ens" in g.lower() else g for g in genes]
+
+
 def get_program_gene_loadings(
     mdata,
     prog_key="prog",
@@ -85,10 +99,22 @@ def get_program_gene_loadings(
     data_key="rna",
     organism="human",
     gene_names_key=None,
+    ortholog_table=None,
+    map_to_human=True,
 ):
-    """Get gene loadings for each program in the mudata object."""
+    """Get gene loadings for each program in the mudata object.
 
-    if gene_names_key is not None and gene_names_key in mdata[data_key].var.columns:
+    For mouse data with map_to_human=True (gene sets in human symbols), genes are
+    mapped to human orthologs (see orthologs.py); ortholog_table is an optional
+    path to a pre-built ortholog table. With map_to_human=False, mouse symbols are
+    kept unchanged (gene sets in mouse symbols, e.g. MSigDB mouse).
+    """
+    if organism not in ("human", "mouse"):
+        raise ValueError(f"Unsupported organism '{organism}'; use 'human' or 'mouse'.")
+
+    if organism == "mouse":
+        gene_names = _mouse_gene_ids(mdata, prog_key, data_key, gene_names_key)
+    elif gene_names_key is not None and gene_names_key in mdata[data_key].var.columns:
         gene_names = (
             mdata[data_key].var[gene_names_key].astype(str).str.upper().tolist()
         )
@@ -113,6 +139,9 @@ def get_program_gene_loadings(
         loadings["gene_names"] = gene_names
         loadings.set_index("gene_names", inplace=True)
 
+    if organism == "mouse" and map_to_human:
+        loadings = collapse_to_human(loadings, load_ortholog_table(ortholog_table))
+
     # with open('var_names.txt', 'w') as fil:
     #     for nam in mdata[data_key].var_names:
     #         fil.write(nam+'\n')
@@ -403,6 +432,7 @@ def compute_geneset_enrichment(
     use_loadings_gene=False,  # False = use all oncology gene, True = use interact expresseed gene
     gene_names_key: Optional[str] = None,
     use_cache: bool = False,
+    ortholog_table: Optional[str] = None,
     **kwargs,
 ) -> Optional[pd.DataFrame]:
     """
@@ -442,6 +472,9 @@ def compute_geneset_enrichment(
         number of threads to run processes on.
     inplace : bool (default: True)
         whether to insert the results back into the mudata object.
+    ortholog_table : str (default: None)
+        For organism='mouse': path to a mouse-human ortholog table. By default the
+        MGI + Ensembl table is downloaded once and cached in Resources/.
 
     Returns
     -------
@@ -484,6 +517,9 @@ def compute_geneset_enrichment(
         data_key=data_key,
         organism=organism,
         gene_names_key=gene_names_key,
+        ortholog_table=ortholog_table,
+        # Enrichr and user gene sets use human symbols; MSigDB mouse sets do not.
+        map_to_human=not (database == "msigdb" and user_geneset is None),
     )
 
     # run enrichment
diff --git a/src/Stage2_Evaluation/A_Metrics/src/orthologs.py b/src/Stage2_Evaluation/A_Metrics/src/orthologs.py
new file mode 100644
index 0000000..bfedda7
--- /dev/null
+++ b/src/Stage2_Evaluation/A_Metrics/src/orthologs.py
@@ -0,0 +1,209 @@
+"""Mouse to human ortholog mapping.
+
+Human gene-set libraries (Enrichr, MSigDB human) and human gene summaries are matched
+to mouse genes through curated orthologs rather than by upper-casing mouse symbols.
+Upper-casing only works when the two species share a symbol, misses orthologs with
+different names (e.g. Zfp451 -> ZNF451) and can pick an unrelated human gene.
+
+The ortholog table is the union of two sources, since each has gaps the other fills:
+- MGI HOM_MouseHumanSequence.rpt (mouse-human homology classes)
+- Ensembl BioMart mouse -> human homologs
+
+The combined table is downloaded on first use and cached as a TSV in
+src/Stage2_Evaluation/Resources/, so later runs are offline and reproducible. A
+pre-built table can also be passed explicitly (columns: mouse_symbol, mouse_ensembl,
+human_symbol, source).
+"""
+
+from __future__ import annotations
+
+import io
+import logging
+import os
+import urllib.parse
+import urllib.request
+from collections.abc import Iterable
+
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+RESOURCES_DIR = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))),
+    "Resources",
+)
+DEFAULT_TABLE_PATH = os.path.join(RESOURCES_DIR, "orthologs_mouse_human.tsv")
+TABLE_COLUMNS = ["mouse_symbol", "mouse_ensembl", "human_symbol", "source"]
+
+MGI_URL = "https://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt"
+# www.ensembl.org redirects BioMart requests; the mirrors answer directly.
+ENSEMBL_MARTS = [
+    "https://useast.ensembl.org/biomart/martservice",
+    "https://asia.ensembl.org/biomart/martservice",
+    "https://www.ensembl.org/biomart/martservice",
+]
+ENSEMBL_QUERY = (
+    '<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE Query>'
+    '<Query virtualSchemaName="default" formatter="TSV" header="1" uniqueRows="1"'
+    ' completionStamp="1">'
+    '<Dataset name="mmusculus_gene_ensembl" interface="default">'
+    '<Attribute name="ensembl_gene_id"/>'
+    '<Attribute name="external_gene_name"/>'
+    '<Attribute name="hsapiens_homolog_associated_gene_name"/>'
+    "</Dataset></Query>"
+)
+# BioMart appends this line only when the full result was sent.
+ENSEMBL_COMPLETION_STAMP = "[success]"
+DOWNLOAD_TIMEOUT_S = 600
+MOUSE_TAXON = "10090"
+HUMAN_TAXON = "9606"
+
+
+def _download(url: str) -> str:
+    with urllib.request.urlopen(url, timeout=DOWNLOAD_TIMEOUT_S) as response:
+        return response.read().decode("utf-8")
+
+
+def _fetch_mgi() -> pd.DataFrame:
+    hom = pd.read_csv(io.StringIO(_download(MGI_URL)), sep="\t", dtype=str)
+    key, taxon = "DB Class Key", "NCBI Taxon ID"
+    mouse = hom.loc[hom[taxon] == MOUSE_TAXON, [key, "Symbol"]]
+    human = hom.loc[hom[taxon] == HUMAN_TAXON, [key, "Symbol"]]
+    pairs = mouse.merge(human, on=key, suffixes=("_mouse", "_human"))
+    if pairs.empty:
+        raise RuntimeError(f"No mouse-human pairs parsed from {MGI_URL}")
+    return pd.DataFrame(
+        {
+            "mouse_symbol": pairs["Symbol_mouse"],
+            "mouse_ensembl": pd.NA,
+            "human_symbol": pairs["Symbol_human"],
+            "source": "MGI",
+        }
+    )
+
+
+def _fetch_ensembl() -> pd.DataFrame:
+    query = urllib.parse.urlencode({"query": ENSEMBL_QUERY})
+    errors = []
+    for mart in ENSEMBL_MARTS:
+        try:
+            text = _download(f"{mart}?{query}").rstrip("\n")
+            body, _, last_line = text.rpartition("\n")
+            if last_line.strip() != ENSEMBL_COMPLETION_STAMP:
+                raise RuntimeError("incomplete response (no completion stamp)")
+            df = pd.read_csv(io.StringIO(body), sep="\t", dtype=str)
+            if df.shape[1] != 3 or df.empty:
+                raise RuntimeError(f"unexpected response: {text[:200]!r}")
+            break
+        except (OSError, ValueError, RuntimeError) as err:  # try the next mirror
+            errors.append(f"{mart}: {err}")
+    else:
+        raise RuntimeError("Ensembl BioMart query failed:\n" + "\n".join(errors))
+    df.columns = ["mouse_ensembl", "mouse_symbol", "human_symbol"]
+    # Rows without a human ortholog are kept: they map Ensembl IDs to mouse symbols.
+    df["source"] = "Ensembl"
+    return df[TABLE_COLUMNS]
+
+
+def build_ortholog_table() -> pd.DataFrame:
+    """Download MGI and Ensembl orthologs and return their union."""
+    table = pd.concat([_fetch_mgi(), _fetch_ensembl()], ignore_index=True)
+    table = table.dropna(subset=["mouse_symbol"]).drop_duplicates()
+    return table[TABLE_COLUMNS].reset_index(drop=True)
+
+
+def load_ortholog_table(path: str | None = None) -> pd.DataFrame:
+    """Load the ortholog table from `path`, the cache, or download and cache it."""
+    if path is not None:
+        if not os.path.isfile(path):
+            raise FileNotFoundError(f"Ortholog table not found: {path}")
+        table = pd.read_csv(path, sep="\t", dtype=str)
+    elif os.path.isfile(DEFAULT_TABLE_PATH):
+        logger.info(f"Loading cached ortholog table from {DEFAULT_TABLE_PATH}")
+        table = pd.read_csv(DEFAULT_TABLE_PATH, sep="\t", dtype=str)
+    else:
+        logger.info("Downloading mouse-human ortholog table (MGI + Ensembl)")
+        try:
+            table = build_ortholog_table()
+        except (OSError, RuntimeError) as err:
+            raise RuntimeError(
+                f"Could not download the mouse-human ortholog table ({err}). On a node "
+                "without internet access, build it once elsewhere (it is cached at "
+                f"{DEFAULT_TABLE_PATH}) or pass a pre-built table with --ortholog_table."
+            ) from err
+        os.makedirs(RESOURCES_DIR, exist_ok=True)
+        # Write to a temporary file first so concurrent jobs never read a partial table.
+        tmp_path = f"{DEFAULT_TABLE_PATH}.{os.getpid()}.tmp"
+        try:
+            table.to_csv(tmp_path, sep="\t", index=False)
+            os.replace(tmp_path, DEFAULT_TABLE_PATH)
+        finally:
+            if os.path.exists(tmp_path):  # only left over if the write failed
+                os.remove(tmp_path)
+        logger.info(f"Saved ortholog table to {DEFAULT_TABLE_PATH}")
+
+    missing = set(TABLE_COLUMNS) - set(table.columns)
+    if missing:
+        raise ValueError(f"Ortholog table is missing columns: {sorted(missing)}")
+    return table
+
+
+def map_mouse_to_human(
+    genes: Iterable[str], table: pd.DataFrame | None = None
+) -> dict[str, list[str]]:
+    """Map mouse gene symbols or Ensembl IDs (ENSMUSG...) to human ortholog symbols.
+
+    Returns a dict from each input gene to a sorted list of human symbols; genes with
+    no human ortholog map to an empty list. A gene with several human orthologs keeps
+    all of them.
+    """
+    if table is None:
+        table = load_ortholog_table()
+    pairs = table.dropna(subset=["human_symbol"])
+    symbol_to_human = pairs.groupby("mouse_symbol")["human_symbol"].apply(set)
+    id_to_symbol = (
+        table.dropna(subset=["mouse_ensembl"])
+        .groupby("mouse_ensembl")["mouse_symbol"]
+        .apply(set)
+    )
+
+    mapping = {}
+    for gene in genes:
+        gene = str(gene)
+        if gene.upper().startswith("ENSMUSG"):
+            symbols = id_to_symbol.get(gene.split(".", 1)[0], set())  # drop version
+        else:
+            symbols = {gene}
+        human = set()
+        for symbol in symbols:
+            human |= symbol_to_human.get(symbol, set())
+        mapping[gene] = sorted(human)
+    return mapping
+
+
+def collapse_to_human(
+    df: pd.DataFrame, table: pd.DataFrame | None = None
+) -> pd.DataFrame:
+    """Re-index a (mouse genes x columns) DataFrame by human ortholog symbols.
+
+    Each human ortholog takes the values of its mouse gene; genes without a human
+    ortholog are dropped, and a human gene with several mouse orthologs keeps the
+    per-column maximum.
+    """
+    df = df.groupby(level=0).max()  # one row per mouse gene
+    mapping = map_mouse_to_human(df.index, table)
+    rows = [(mouse, human) for mouse, hs in mapping.items() for human in hs]
+    n_unmapped = sum(1 for hs in mapping.values() if not hs)
+    logger.info(
+        f"Ortholog mapping: {len(mapping) - n_unmapped}/{len(mapping)} mouse genes "
+        f"have a human ortholog; the rest are excluded."
+    )
+    if not rows:
+        raise ValueError(
+            "No mouse genes could be mapped to human orthologs. Check that gene "
+            "names are mouse symbols or Ensembl IDs (e.g. set gene_names_key)."
+        )
+    mouse_idx, human_idx = zip(*rows)
+    expanded = df.loc[list(mouse_idx)].copy()
+    expanded.index = pd.Index(human_idx, name=df.index.name)
+    return expanded.groupby(level=0).max()
diff --git a/src/Stage3_Interpretation/A_Plotting/README.md b/src/Stage3_Interpretation/A_Plotting/README.md
index 7ce17ce..4c60668 100644
--- a/src/Stage3_Interpretation/A_Plotting/README.md
+++ b/src/Stage3_Interpretation/A_Plotting/README.md
@@ -64,6 +64,8 @@ Generates a multi-panel figure to guide K selection. Reads pre-computed Stage 2
 | `--variance_col` | str | `Total` | Column name for variance values. Use `Total` for summary files, or a per-program column name (will be summed) |
 | `--stability_file` | str | `None` | Path to a pre-computed stability/error file (TSV or NPZ). Bypasses `cnmf.consensus()` — useful for torch-cNMF runs where the cnmf package isn't installed |
 | `--run_program_dotplot` | flag | off | If set, also generate per-(K, sel_thresh) program dotplots. Requires the inference `cNMF_{K}_{thresh}.h5mu` to exist for each pair; off by default because the loop is slow |
+| `--no_trait` | flag | off | Do not require or read trait (GWAS) enrichment files and drop the trait plots. Use when evaluation ran without trait enrichment (e.g. mouse data) |
+| `--no_perturbation` | flag | off | Do not require or read perturbation association files and drop the regulator plots. Use for non-perturbed data (no guides); `--Conditions` is then not needed |
 
 ### Outputs
 
diff --git a/src/Stage3_Interpretation/A_Plotting/Slurm_Version/cNMF_k_selection.py b/src/Stage3_Interpretation/A_Plotting/Slurm_Version/cNMF_k_selection.py
index dc2392a..00b1cf1 100644
--- a/src/Stage3_Interpretation/A_Plotting/Slurm_Version/cNMF_k_selection.py
+++ b/src/Stage3_Interpretation/A_Plotting/Slurm_Version/cNMF_k_selection.py
@@ -20,6 +20,7 @@
     plot_explained_variance,
     programs_dotplots,
     plot_k_selection_panel,
+    plot_k_selection_panel_no_traits,
     read_condition_labels_from_h5mu,
 )
 
@@ -142,6 +143,20 @@ def main():
         help="If set, also generate per-(K, sel_thresh) program dotplots. Requires the inference "
         "h5mu (cNMF_{K}_{thresh}.h5mu) to exist for each (K, sel_thresh). Default off.",
     )
+    parser.add_argument(
+        "--no_trait",
+        action="store_true",
+        help="Skip trait (GWAS) enrichment: do not require or read the trait files and "
+        "drop the trait plots. Use when evaluation ran without --Perform_trait "
+        "(e.g. mouse data). Default off.",
+    )
+    parser.add_argument(
+        "--no_perturbation",
+        action="store_true",
+        help="Skip perturbation results: do not require or read the perturbation "
+        "association files and drop the regulator plots. Use for datasets without "
+        "guides (non-perturbed scRNA-seq). Default off.",
+    )
 
     args = parser.parse_args()
 
@@ -154,7 +169,7 @@ def main():
         yaml.dump(args_dict, f, default_flow_style=False, width=1000)
 
     # ---- Condition labels: default to the values of --groupby in the inference h5mu ----
-    if args.Conditions is None:
+    if args.Conditions is None and not args.no_perturbation:
         first_h5mu = (
             f"{args.output_directory}/{args.run_name}/Inference/adata/"
             f"cNMF_{args.K[0]}_{str(args.sel_threshs[0]).replace('.', '_')}.h5mu"
@@ -175,15 +190,21 @@ def main():
     )
     _ev_pat = args.variance_file or "{k}_Explained_Variance_Summary.txt"
 
+    required_pats = [_go_pat, _gs_pat, _ev_pat]
+    if not args.no_trait:
+        required_pats.append(_tr_pat)
+
     missing = []
     for sel_thresh in args.sel_threshs:
         sel_str = str(sel_thresh).replace(".", "_")
         for k in args.K:
             k_folder = os.path.join(args.eval_folder_name, f"{k}_{sel_str}")
-            for pat in [_go_pat, _gs_pat, _tr_pat, _ev_pat]:
+            for pat in required_pats:
                 fpath = os.path.join(k_folder, pat.format(k=k))
                 if not os.path.isfile(fpath):
                     missing.append(fpath)
+            if args.no_perturbation:
+                continue
             for cond in args.Conditions:
                 fpath = os.path.join(k_folder, _pt_pat.format(k=k, sample=cond))
                 if not os.path.isfile(fpath):
@@ -218,31 +239,40 @@ def main():
             trait_file=args.trait_file,
             term_col=args.term_col,
             adjpval_col=args.adjpval_col,
+            include_traits=not args.no_trait,
         )
         plot_enrichment(
             count_df,
             folder_name=args.save_folder_name,
             file_name=f"Enrichment_{sel_thresh}",
+            include_traits=not args.no_trait,
         )
 
         # Perturbation
-        test_stats_df = load_perturbation_data(
-            folder=args.eval_folder_name,
-            components=args.K,
-            sel_thresh=sel_thresh,
-            conditions=args.Conditions,
-            pval=args.pval,
-            perturbation_file=args.perturbation_file,
-            perturb_adjpval_col=args.perturb_adjpval_col,
-            perturb_target_col=args.perturb_target_col,
-            perturb_log2fc_col=args.perturb_log2fc_col,
-        )
-        plot_perturbation(
-            test_stats_df,
-            folder_name=args.save_folder_name,
-            pval=args.pval,
-            file_name=f"Perturbation_{sel_thresh}",
-        )
+        if args.no_perturbation:
+            print(
+                "--no_perturbation set: skipping perturbation results and regulator plots.",
+                flush=True,
+            )
+            test_stats_df = None
+        else:
+            test_stats_df = load_perturbation_data(
+                folder=args.eval_folder_name,
+                components=args.K,
+                sel_thresh=sel_thresh,
+                conditions=args.Conditions,
+                pval=args.pval,
+                perturbation_file=args.perturbation_file,
+                perturb_adjpval_col=args.perturb_adjpval_col,
+                perturb_target_col=args.perturb_target_col,
+                perturb_log2fc_col=args.perturb_log2fc_col,
+            )
+            plot_perturbation(
+                test_stats_df,
+                folder_name=args.save_folder_name,
+                pval=args.pval,
+                file_name=f"Perturbation_{sel_thresh}",
+            )
 
         # Explained Variance
         stats_EV = load_explained_variance_data(
@@ -260,8 +290,13 @@ def main():
 
         # Motif (working in progress)
 
-        # K-selection panel
-        plot_k_selection_panel(
+        # K-selection panel (perturbation row is hidden when test_stats_df is None)
+        panel_fn = (
+            plot_k_selection_panel_no_traits
+            if args.no_trait
+            else plot_k_selection_panel
+        )
+        panel_fn(
             stats_SE,
             count_df,
             test_stats_df,
diff --git a/src/Stage3_Interpretation/A_Plotting/Slurm_Version/cNMF_program_analysis.py b/src/Stage3_Interpretation/A_Plotting/Slurm_Version/cNMF_program_analysis.py
index 5324231..4866e16 100644
--- a/src/Stage3_Interpretation/A_Plotting/Slurm_Version/cNMF_program_analysis.py
+++ b/src/Stage3_Interpretation/A_Plotting/Slurm_Version/cNMF_program_analysis.py
@@ -235,12 +235,24 @@ def main():
     print(f"there are {program_len} Program found")
 
     # found detected perturbed gene (use gene symbols from var column when var_names are Ensembl IDs)
-    perturbed_gene = np.unique(mdata[args.prog_key].uns["guide_targets"])
-    if args.gene_name_key in mdata[args.data_key].var.columns:
-        gene_symbols = mdata[args.data_key].var[args.gene_name_key].astype(str).tolist()
+    # uns['guide_targets'] only exists for perturbation data (written by inference
+    # when guides are present); without it there are no perturbed genes to mark.
+    if "guide_targets" in mdata[args.prog_key].uns:
+        perturbed_gene = np.unique(mdata[args.prog_key].uns["guide_targets"])
+        if args.gene_name_key in mdata[args.data_key].var.columns:
+            gene_symbols = (
+                mdata[args.data_key].var[args.gene_name_key].astype(str).tolist()
+            )
+        else:
+            gene_symbols = mdata[args.data_key].var_names.tolist()
+        perturbed_gene_found = sorted(set(gene_symbols) & set(perturbed_gene.tolist()))
     else:
-        gene_symbols = mdata[args.data_key].var_names.tolist()
-    perturbed_gene_found = sorted(set(gene_symbols) & set(perturbed_gene.tolist()))
+        perturbed_gene_found = []
+        print(
+            f"No uns['guide_targets'] in '{args.prog_key}': treating the data as "
+            "non-perturbed; perturbed-gene annotations will be empty.",
+            flush=True,
+        )
 
     # compute correlations (both K x K; full matrices written to corr_dir every run)
     corr_dir = args.corr_matrix_path or args.save_path
diff --git a/src/Stage3_Interpretation/A_Plotting/src/k_selection_plots.py b/src/Stage3_Interpretation/A_Plotting/src/k_selection_plots.py
index 1b31c61..a235d22 100644
--- a/src/Stage3_Interpretation/A_Plotting/src/k_selection_plots.py
+++ b/src/Stage3_Interpretation/A_Plotting/src/k_selection_plots.py
@@ -413,7 +413,13 @@ def load_enrichment_data(
     trait_file=None,
     term_col="Term",
     adjpval_col="Adjusted P-value",
+    include_traits=True,
 ):
+    """Count unique significant terms per K for GO, gene set and trait enrichment.
+
+    Set include_traits=False when trait enrichment was not run (non-human data);
+    the trait files are then not read and the 'traits' column is filled with 0.
+    """
 
     # loading function
     def load(k, term, folder, term_col, adjpval_col):
@@ -465,15 +471,16 @@ def load(k, term, folder, term_col, adjpval_col):
                 adjpval_col,
             )
         )
-        term_df.append(
-            load(
-                k,
-                "traits",
-                "{}/{}".format(k_folder, trait_file.format(k=k)),
-                term_col,
-                adjpval_col,
+        if include_traits:
+            term_df.append(
+                load(
+                    k,
+                    "traits",
+                    "{}/{}".format(k_folder, trait_file.format(k=k)),
+                    term_col,
+                    adjpval_col,
+                )
             )
-        )
 
     term_df = pd.concat(term_df, ignore_index=True)
 
@@ -513,7 +520,9 @@ def load(k, term, folder, term_col, adjpval_col):
 
 
 # plot loaded df — 3 independent square figures
-def plot_enrichment(count_df, folder_name=None, file_name=None, selected_k=None):
+def plot_enrichment(
+    count_df, folder_name=None, file_name=None, selected_k=None, include_traits=True
+):
     enrichment_config = [
         ("go_terms", "Unique GO terms", "GO term enrichment", _COLORS["go_terms"], "o"),
         (
@@ -526,6 +535,9 @@ def plot_enrichment(count_df, folder_name=None, file_name=None, selected_k=None)
         ("traits", "Unique traits", "Trait enrichment", _COLORS["traits"], "D"),
     ]
 
+    if not include_traits:
+        enrichment_config = [c for c in enrichment_config if c[0] != "traits"]
+
     for col, ylabel, title, color, marker in enrichment_config:
         fig, ax = plt.subplots(figsize=(4, 3.5))
 
@@ -800,6 +812,88 @@ def plot_explained_variance(stats, folder_name=None, file_name=None, selected_k=
     plt.show()
 
 
+def _plot_regulator_row(ax_all, ax_per_condition, test_stats_df, pval, _add_vline):
+    """Plot unique significant regulators per K, pooled and per condition."""
+    # --- Row 3, Col 0: Regulators (all conditions) ---
+    ax = ax_all
+    plotting_df = (
+        test_stats_df.loc[test_stats_df.adj_pval <= pval, ["K", "target_name"]]
+        .drop_duplicates()
+        .groupby(["K"])
+        .count()
+        .reset_index()
+    )
+    ax.plot(
+        plotting_df["K"],
+        plotting_df["target_name"],
+        color=_COLORS["primary"],
+        linewidth=1.5,
+        marker="o",
+        markersize=4,
+        markerfacecolor=_COLORS["perturbation"],
+        markeredgecolor=_COLORS["primary"],
+        markeredgewidth=0.8,
+        zorder=3,
+    )
+    ax.fill_between(
+        plotting_df["K"],
+        plotting_df["target_name"],
+        alpha=0.08,
+        color=_COLORS["perturbation"],
+    )
+    _add_vline(ax)
+    _style_ax(
+        ax,
+        xlabel="Number of components (k)",
+        ylabel="No. unique regulators",
+        title="Unique regulators for all conditions",
+    )
+    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True, nbins=5))
+
+    # --- Row 3, Col 1: Regulators (per sample) ---
+    ax = ax_per_condition
+    plotting_df_sample = (
+        test_stats_df.loc[
+            test_stats_df.adj_pval <= pval, ["K", "condition", "target_name"]
+        ]
+        .drop_duplicates()
+        .groupby(["K", "condition"])
+        .count()
+        .reset_index()
+    )
+    sns.lineplot(
+        x="K",
+        y="target_name",
+        hue="condition",
+        data=plotting_df_sample,
+        palette=_PALETTE[: plotting_df_sample["condition"].nunique()],
+        linewidth=1.5,
+        marker="o",
+        markersize=4,
+        ax=ax,
+        legend="brief",
+    )
+    _add_vline(ax)
+    _style_ax(
+        ax,
+        xlabel="Number of components (k)",
+        ylabel="No. unique regulators",
+        title="Unique regulators per condition",
+    )
+    ax.legend(
+        title="Condition",
+        fontsize=7,
+        title_fontsize=8,
+        frameon=True,
+        fancybox=False,
+        edgecolor="#cccccc",
+        bbox_to_anchor=(1.02, 1),
+        loc="upper left",
+        borderaxespad=0,
+    )
+    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True, nbins=5))
+
+
 # Combined panel figure: 3 rows x 3 columns
 # Row 1: Stability, Error, Explained variance
 # Row 2: GO terms, Gene sets, Traits
@@ -945,84 +1039,12 @@ def _add_vline(ax):
         _style_ax(ax, xlabel="Number of components (k)", ylabel=ylabel, title=title)
         ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True, nbins=5))
 
-    # --- Row 3, Col 0: Regulators (all conditions) ---
-    ax = axes[2, 0]
-    plotting_df = (
-        test_stats_df.loc[test_stats_df.adj_pval <= pval, ["K", "target_name"]]
-        .drop_duplicates()
-        .groupby(["K"])
-        .count()
-        .reset_index()
-    )
-    ax.plot(
-        plotting_df["K"],
-        plotting_df["target_name"],
-        color=_COLORS["primary"],
-        linewidth=1.5,
-        marker="o",
-        markersize=4,
-        markerfacecolor=_COLORS["perturbation"],
-        markeredgecolor=_COLORS["primary"],
-        markeredgewidth=0.8,
-        zorder=3,
-    )
-    ax.fill_between(
-        plotting_df["K"],
-        plotting_df["target_name"],
-        alpha=0.08,
-        color=_COLORS["perturbation"],
-    )
-    _add_vline(ax)
-    _style_ax(
-        ax,
-        xlabel="Number of components (k)",
-        ylabel="No. unique regulators",
-        title="Unique regulators for all conditions",
-    )
-    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True, nbins=5))
-
-    # --- Row 3, Col 1: Regulators (per sample) ---
-    ax = axes[2, 1]
-    plotting_df_sample = (
-        test_stats_df.loc[
-            test_stats_df.adj_pval <= pval, ["K", "condition", "target_name"]
-        ]
-        .drop_duplicates()
-        .groupby(["K", "condition"])
-        .count()
-        .reset_index()
-    )
-    sns.lineplot(
-        x="K",
-        y="target_name",
-        hue="condition",
-        data=plotting_df_sample,
-        palette=_PALETTE[: plotting_df_sample["condition"].nunique()],
-        linewidth=1.5,
-        marker="o",
-        markersize=4,
-        ax=ax,
-        legend="brief",
-    )
-    _add_vline(ax)
-    _style_ax(
-        ax,
-        xlabel="Number of components (k)",
-        ylabel="No. unique regulators",
-        title="Unique regulators per condition",
-    )
-    ax.legend(
-        title="Condition",
-        fontsize=7,
-        title_fontsize=8,
-        frameon=True,
-        fancybox=False,
-        edgecolor="#cccccc",
-        bbox_to_anchor=(1.02, 1),
-        loc="upper left",
-        borderaxespad=0,
-    )
-    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True, nbins=5))
+    # --- Row 3: Regulators (hidden when there is no perturbation data) ---
+    if test_stats_df is None:
+        axes[2, 0].set_visible(False)
+        axes[2, 1].set_visible(False)
+    else:
+        _plot_regulator_row(axes[2, 0], axes[2, 1], test_stats_df, pval, _add_vline)
 
     # --- Row 3, Col 2: hide empty panel ---
     axes[2, 2].set_visible(False)
@@ -1198,84 +1220,12 @@ def _add_vline(ax):
     # --- Row 2, Col 2: hide empty panel ---
     axes[1, 2].set_visible(False)
 
-    # --- Row 3, Col 0: Regulators (all conditions) ---
-    ax = axes[2, 0]
-    plotting_df = (
-        test_stats_df.loc[test_stats_df.adj_pval <= pval, ["K", "target_name"]]
-        .drop_duplicates()
-        .groupby(["K"])
-        .count()
-        .reset_index()
-    )
-    ax.plot(
-        plotting_df["K"],
-        plotting_df["target_name"],
-        color=_COLORS["primary"],
-        linewidth=1.5,
-        marker="o",
-        markersize=4,
-        markerfacecolor=_COLORS["perturbation"],
-        markeredgecolor=_COLORS["primary"],
-        markeredgewidth=0.8,
-        zorder=3,
-    )
-    ax.fill_between(
-        plotting_df["K"],
-        plotting_df["target_name"],
-        alpha=0.08,
-        color=_COLORS["perturbation"],
-    )
-    _add_vline(ax)
-    _style_ax(
-        ax,
-        xlabel="Number of components (k)",
-        ylabel="No. unique regulators",
-        title="Unique regulators for all conditions",
-    )
-    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True, nbins=5))
-
-    # --- Row 3, Col 1: Regulators (per sample) ---
-    ax = axes[2, 1]
-    plotting_df_sample = (
-        test_stats_df.loc[
-            test_stats_df.adj_pval <= pval, ["K", "condition", "target_name"]
-        ]
-        .drop_duplicates()
-        .groupby(["K", "condition"])
-        .count()
-        .reset_index()
-    )
-    sns.lineplot(
-        x="K",
-        y="target_name",
-        hue="condition",
-        data=plotting_df_sample,
-        palette=_PALETTE[: plotting_df_sample["condition"].nunique()],
-        linewidth=1.5,
-        marker="o",
-        markersize=4,
-        ax=ax,
-        legend="brief",
-    )
-    _add_vline(ax)
-    _style_ax(
-        ax,
-        xlabel="Number of components (k)",
-        ylabel="No. unique regulators",
-        title="Unique regulators per condition",
-    )
-    ax.legend(
-        title="Condition",
-        fontsize=7,
-        title_fontsize=8,
-        frameon=True,
-        fancybox=False,
-        edgecolor="#cccccc",
-        bbox_to_anchor=(1.02, 1),
-        loc="upper left",
-        borderaxespad=0,
-    )
-    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True, nbins=5))
+    # --- Row 3: Regulators (hidden when there is no perturbation data) ---
+    if test_stats_df is None:
+        axes[2, 0].set_visible(False)
+        axes[2, 1].set_visible(False)
+    else:
+        _plot_regulator_row(axes[2, 0], axes[2, 1], test_stats_df, pval, _add_vline)
 
     # --- Row 3, Col 2: hide empty panel ---
     axes[2, 2].set_visible(False)
diff --git a/src/Stage3_Interpretation/A_Plotting/src/utilities.py b/src/Stage3_Interpretation/A_Plotting/src/utilities.py
index 9fa8bf3..4258e14 100644
--- a/src/Stage3_Interpretation/A_Plotting/src/utilities.py
+++ b/src/Stage3_Interpretation/A_Plotting/src/utilities.py
@@ -30,7 +30,7 @@ def convert_with_mygene(dataframe, species="human", index=True):
             dataframe.index,
             scopes="ensembl.gene",
             fields="symbol,name",
-            species="human",
+            species=species,
         )
     else:
         # Query multiple genes at once
@@ -38,7 +38,7 @@ def convert_with_mygene(dataframe, species="human", index=True):
             dataframe.columns,
             scopes="ensembl.gene",
             fields="symbol,name",
-            species="human",
+            species=species,
         )
 
     # Create mapping dictionary
@@ -64,7 +64,7 @@ def convert_adata_with_mygene(adata, species="human"):
     mg = mygene.MyGeneInfo()
     gene_list = adata.var_names.tolist()
     annotations = mg.querymany(
-        gene_list, scopes="ensembl.gene", fields="symbol", species="human"
+        gene_list, scopes="ensembl.gene", fields="symbol", species=species
     )
 
     # Process the results to create mapping
@@ -225,8 +225,38 @@ def _natural_sort_key(filepath):
     ]
 
 
+# obsm keys used by other tools (e.g. Seurat conversions) for the scanpy embeddings
+ALT_EMBEDDING_KEYS = {"X_umap": ("UMAP", "umap"), "X_pca": ("PCA", "pca")}
+
+
+def _adopt_alt_embedding_keys(adata):
+    """Copy an embedding stored under a non-scanpy obsm key to 'X_umap'/'X_pca'.
+
+    Reuses the embedding the data came with instead of recomputing a different
+    one. Existing 'X_umap'/'X_pca' entries and the original keys are left as is.
+    """
+    for std_key, alt_keys in ALT_EMBEDDING_KEYS.items():
+        if std_key in adata.obsm:
+            continue
+        for alt_key in alt_keys:
+            if alt_key not in adata.obsm:
+                continue
+            emb = adata.obsm[alt_key]
+            # scanpy plotting needs an array; some converters store a DataFrame
+            emb = emb.to_numpy() if hasattr(emb, "to_numpy") else np.asarray(emb)
+            adata.obsm[std_key] = emb[:, :2] if std_key == "X_umap" else emb
+            print(f"ensure_umap: using obsm['{alt_key}'] as '{std_key}'.", flush=True)
+            break
+
+
 def ensure_umap(mdata, data_key, prog_key, n_top_genes=2000, n_comps=50):
-    """Compute PCA/UMAP from top-variance genes and write to both modalities; no-op if X_umap already exists in both."""
+    """Compute PCA/UMAP from top-variance genes and write to both modalities; no-op if X_umap already exists in both.
+
+    Embeddings stored under 'UMAP'/'PCA' (e.g. Seurat conversions) are reused first.
+    """
+    _adopt_alt_embedding_keys(mdata[data_key])
+    _adopt_alt_embedding_keys(mdata[prog_key])
+
     has_prog = "X_umap" in mdata[prog_key].obsm
     has_data = "X_umap" in mdata[data_key].obsm
     if has_prog and has_data:
diff --git a/src/Stage3_Interpretation/B_Summarization/README.md b/src/Stage3_Interpretation/B_Summarization/README.md
index 4eaf792..c707f89 100644
--- a/src/Stage3_Interpretation/B_Summarization/README.md
+++ b/src/Stage3_Interpretation/B_Summarization/README.md
@@ -127,7 +127,7 @@ Sidecar files (`specificity_score_*`, `corr_gene_matrix_*`, `kd_efficiency.txt`)
 | **Program_summary** | cNMF program | `Compile_Summary_sheet` | `manual_annotation_label`, `manual_timepoint`, `Notes`, `Automatic Timepoint`, `Total Enriched GO Terms`, `Significant regulators with positive/negative effect {Condition}`, `Top 5 specific regulators (FDR<0.1) {Condition}`, `sigfdr0.05_targets_sorted_abslog2fcd_{Condition}`, `top10_loaded_genes`, `top30_loaded_genes`, `variance_explained`, `Mean program score {Condition}`, `Fra cells above mean program score {Condition}`, `top10_enriched_genesets`, `top10_enriched_go_terms` |
 | **Target_summary** | perturbation target | `Compile_Target_Summary_sheet` | `mean_expression_{Condition}`, `# Cells {Condition}`, `significant programs {Condition}`, `# programs {Condition}`, `top 5 specific programs (FDR < 0.1) {Condition}`, `top 5 specificity scores (FDR < 0.1) {Condition}`, `top 5 pos/neg correls targets (program log2fc) {Condition}`, `top 5 pos/neg correls (program log2fc) {Condition}`, `kd_efficiency_{Condition}`, `Mean KD efficiency (%) across conditions` |
 | **Program_loadings_flat** | cNMF program | `compile_Program_loading_score_sheet_flat` | Rank `1`..`num_gene` → gene name (top genes per program, descending) |
-| **Program_loadings_long** | (program, rank) | `compile_Program_loading_score_sheet_long` | `Program`, `Rank`, `Gene`, `Annotation` (mygene summary) |
+| **Program_loadings_long** | (program, rank) | `compile_Program_loading_score_sheet_long` | `Program`, `Rank`, `Gene`, `Annotation` (mygene summary); with `--organism mouse` also `Human ortholog` (the human genes whose summaries are shown) |
 | **GO** | enriched GO term × program | `Compile_GO_sheet` | indexed by `Term`; passes through columns from `{K}_GO_term_enrichment.txt`; `Genes` truncated to top `num_gene` |
 | **Geneset** | enriched geneset × program | `Compile_Geneset_sheet` | same shape, from `{K}_geneset_enrichment.txt` |
 | **Trait** | enriched trait × program | `Compile_Trait_sheet` | same shape, from `{K}_trait_enrichment.txt` |
diff --git a/src/Stage3_Interpretation/B_Summarization/Slurm_Version/cNMF_excel_summary.py b/src/Stage3_Interpretation/B_Summarization/Slurm_Version/cNMF_excel_summary.py
index ef48ea4..c3c09a7 100644
--- a/src/Stage3_Interpretation/B_Summarization/Slurm_Version/cNMF_excel_summary.py
+++ b/src/Stage3_Interpretation/B_Summarization/Slurm_Version/cNMF_excel_summary.py
@@ -155,6 +155,23 @@ def main():
         default="guide_targets",
         help="uns key for guide target names. Default: guide_targets",
     )
+    parser.add_argument(
+        "--organism",
+        type=str.lower,
+        choices=["human", "mouse"],
+        default="human",
+        help="Species of the data, for MyGene gene summaries in the Program Loadings "
+        "sheet. With mouse, each gene gets the summaries of its human orthologs "
+        "(MGI + Ensembl), listed in a 'Human ortholog' column. Default: human",
+    )
+    parser.add_argument(
+        "--ortholog_table",
+        type=str,
+        default=None,
+        help="For --organism mouse: TSV of mouse-human orthologs (columns: "
+        "mouse_symbol, mouse_ensembl, human_symbol, source). Default: the table "
+        "cached in src/Stage2_Evaluation/Resources/, downloaded if missing",
+    )
     parser.add_argument(
         "--gene_names_key",
         type=str,
@@ -289,6 +306,8 @@ def main():
         num_gene=args.num_gene,
         data_key=args.data_key,
         gene_names_key=args.gene_names_key,
+        species=args.organism,
+        ortholog_table=args.ortholog_table,
     )
     df_Program_loading_flat = compile_Program_loading_score_sheet_flat(
         mdata,
@@ -370,19 +389,29 @@ def main():
     )
 
     # ── Target Summary (writes specificity / correlation / KD-efficiency sidecars to save_path) ──
-    df_Target_Summary = Compile_Target_Summary_sheet(
-        mdata,
-        Perturbation_path_base,
-        Sample=args.Conditions,
-        categorical_key=args.categorical_key,
-        prog_key=args.prog_key,
-        data_key=args.data_key,
-        guide_targets_key=args.guide_targets_key,
-        save_path=args.save_path,
-        effect_size=args.effect_size,
-        control_target_name=args.control_target_name,
-        gene_names_key=args.gene_names_key,
-    )
+    # Needs guide information; skipped for non-perturbed data.
+    has_guides = args.guide_targets_key in mdata[args.prog_key].uns
+    if has_guides:
+        df_Target_Summary = Compile_Target_Summary_sheet(
+            mdata,
+            Perturbation_path_base,
+            Sample=args.Conditions,
+            categorical_key=args.categorical_key,
+            prog_key=args.prog_key,
+            data_key=args.data_key,
+            guide_targets_key=args.guide_targets_key,
+            save_path=args.save_path,
+            effect_size=args.effect_size,
+            control_target_name=args.control_target_name,
+            gene_names_key=args.gene_names_key,
+        )
+    else:
+        print(
+            f"No uns['{args.guide_targets_key}'] in '{args.prog_key}': "
+            "skipping the Targets Summary sheet (non-perturbed data).",
+            flush=True,
+        )
+        df_Target_Summary = None
 
     # ── Program Summary ──
     df_Summary = Compile_Summary_sheet(
@@ -393,7 +422,8 @@ def main():
         df_Program_loading_flat,
         df_Explained_Variance,
         Sample=args.Conditions,
-        specicicity_path=args.save_path,
+        # specificity scores are written by the Targets Summary step
+        specicicity_path=args.save_path if has_guides else None,
         categorical_key=args.categorical_key,
         non_tagerting_key=args.non_targeting_key,
         effect_size=args.effect_size,
@@ -405,9 +435,10 @@ def main():
     df_Program_loading_long.to_csv(
         f"{args.save_path}/Program_Loadings_{args.K}_{thresh_str}.tsv", sep="\t"
     )
-    df_Target_Summary.to_csv(
-        f"{args.save_path}/Targets_Summary_{args.K}_{thresh_str}.tsv", sep="\t"
-    )
+    if df_Target_Summary is not None:
+        df_Target_Summary.to_csv(
+            f"{args.save_path}/Targets_Summary_{args.K}_{thresh_str}.tsv", sep="\t"
+        )
     print(f"Saved separate TSV files to {args.save_path}")
 
     # ── Write Excel ──
@@ -418,7 +449,8 @@ def main():
         df_Program_loading_long.to_excel(
             writer, sheet_name="Program Loadings", index=True
         )
-        df_Target_Summary.to_excel(writer, sheet_name="Targets Summary", index=True)
+        if df_Target_Summary is not None:
+            df_Target_Summary.to_excel(writer, sheet_name="Targets Summary", index=True)
 
         if df_Association is not None:
             df_Association.to_excel(writer, sheet_name="Sample Association", index=True)
diff --git a/src/Stage3_Interpretation/B_Summarization/src/Compile_excel_sheet.py b/src/Stage3_Interpretation/B_Summarization/src/Compile_excel_sheet.py
index 924b338..bb5fe63 100644
--- a/src/Stage3_Interpretation/B_Summarization/src/Compile_excel_sheet.py
+++ b/src/Stage3_Interpretation/B_Summarization/src/Compile_excel_sheet.py
@@ -112,8 +112,19 @@ def add_specificity_scores_file(
 
 # -------------- helper methods for loading sheets--------------
 def compile_Program_loading_score_sheet_long(
-    mdata, num_gene=300, data_key="rna", gene_names_key=None
+    mdata,
+    num_gene=300,
+    data_key="rna",
+    gene_names_key=None,
+    species="human",
+    ortholog_table=None,
 ):
+    """Top genes per program with MyGene summaries (long form).
+
+    For species='mouse', each gene is annotated with the summaries of its human
+    orthologs (MGI + Ensembl table, see Stage2 orthologs.py), listed in a 'Human
+    ortholog' column; ortholog_table is an optional path to a pre-built table.
+    """
 
     print("Load program loadings data in long form")
 
@@ -124,7 +135,9 @@ def compile_Program_loading_score_sheet_long(
     )
 
     # Rename columns to curated gene names
-    if gene_names_key is not None:
+    # Fall back to var_names when the symbol column is absent (e.g. var_names are
+    # already symbols), as compute_kd_efficiency does.
+    if gene_names_key is not None and gene_names_key in mdata[data_key].var.columns:
         rename_map = dict(
             zip(mdata[data_key].var_names, mdata[data_key].var[gene_names_key])
         )
@@ -146,6 +159,17 @@ def compile_Program_loading_score_sheet_long(
     mg = MyGeneInfo()
     long_data = []
 
+    use_orthologs = species == "mouse"
+    if use_orthologs:
+        from Stage2_Evaluation.A_Metrics.src.orthologs import (
+            load_ortholog_table,
+            map_mouse_to_human,
+        )
+
+        # Map all top genes at once (building the lookup is the slow part).
+        all_genes = pd.unique(result_df.stack().dropna().astype(str).str.strip())
+        orthologs = map_mouse_to_human(all_genes, load_ortholog_table(ortholog_table))
+
     for program_idx, row in result_df.iterrows():
         # Query all genes for this program at once
         genes_list = row.dropna().astype(str).str.strip().tolist()
@@ -154,13 +178,23 @@ def compile_Program_loading_score_sheet_long(
         if not genes_list:
             continue
 
+        if use_orthologs:
+            query_genes = sorted({h for g in genes_list for h in orthologs.get(g, [])})
+            query_species = "human"
+        else:
+            query_genes, query_species = genes_list, species
+
         # Get results for all genes in this program
-        results = mg.querymany(
-            genes_list,
-            scopes="symbol",
-            fields="summary",
-            species="human",
-            verbose=False,
+        results = (
+            mg.querymany(
+                query_genes,
+                scopes="symbol",
+                fields="summary",
+                species=query_species,
+                verbose=False,
+            )
+            if query_genes
+            else []
         )
 
         # Create a lookup dictionary for faster access
@@ -175,16 +209,23 @@ def compile_Program_loading_score_sheet_long(
             # Skip NaN values
             if pd.notna(gene) and gene != "":
                 gene_clean = str(gene).strip()
-                annotation = summary_dict.get(gene_clean, "N/A")
-
-                long_data.append(
-                    {
-                        "Program": program_idx,
-                        "Rank": rank,
-                        "Gene": gene_clean,
-                        "Annotation": annotation,
-                    }
-                )
+                record = {
+                    "Program": program_idx,
+                    "Rank": rank,
+                    "Gene": gene_clean,
+                }
+                if use_orthologs:
+                    human = orthologs.get(gene_clean, [])
+                    summaries = [
+                        f"{h}: {summary_dict[h]}"
+                        for h in human
+                        if summary_dict.get(h, "N/A") != "N/A"
+                    ]
+                    record["Human ortholog"] = ";".join(human)
+                    record["Annotation"] = " | ".join(summaries) or "N/A"
+                else:
+                    record["Annotation"] = summary_dict.get(gene_clean, "N/A")
+                long_data.append(record)
 
     # Convert to DataFrame
     annotation_df = pd.DataFrame(long_data)
@@ -205,7 +246,9 @@ def compile_Program_loading_score_sheet_flat(
     )
 
     # Rename columns to curated gene names
-    if gene_names_key is not None:
+    # Fall back to var_names when the symbol column is absent (e.g. var_names are
+    # already symbols), as compute_kd_efficiency does.
+    if gene_names_key is not None and gene_names_key in mdata[data_key].var.columns:
         rename_map = dict(
             zip(mdata[data_key].var_names, mdata[data_key].var[gene_names_key])
         )
diff --git a/tests/Script/Stage2_Evaluation/test_orthologs.py b/tests/Script/Stage2_Evaluation/test_orthologs.py
new file mode 100644
index 0000000..92a73a6
--- /dev/null
+++ b/tests/Script/Stage2_Evaluation/test_orthologs.py
@@ -0,0 +1,227 @@
+"""Unit tests for mouse to human ortholog mapping (Stage2 orthologs.py).
+
+Uses a small synthetic ortholog table, so no network access is needed.
+
+Usage (from the repo root):
+    python -m pytest tests/Script/Stage2_Evaluation/test_orthologs.py -v
+"""
+
+import anndata as ad
+import mudata as md
+import numpy as np
+import pandas as pd
+import pytest
+
+from Stage2_Evaluation.A_Metrics.src.enrichment_geneset import (
+    get_program_gene_loadings,
+)
+from Stage2_Evaluation.A_Metrics.src.orthologs import (
+    TABLE_COLUMNS,
+    collapse_to_human,
+    load_ortholog_table,
+    map_mouse_to_human,
+)
+
+# mouse_symbol, mouse_ensembl, human_symbol, source
+ORTHOLOGS = [
+    ("Cdh5", "ENSMUSG00000031871", "CDH5", "Ensembl"),  # same name, one-to-one
+    ("Cdh5", np.nan, "CDH5", "MGI"),  # duplicate from the second source
+    ("Zfp451", np.nan, "ZNF451", "MGI"),  # different name
+    ("H2-Ab1", np.nan, "HLA-DQB1", "Ensembl"),  # one-to-many
+    ("H2-Ab1", np.nan, "HLA-DQB2", "Ensembl"),
+    ("Cd59a", np.nan, "CD59", "Ensembl"),  # many-to-one
+    ("Cd59b", np.nan, "CD59", "Ensembl"),
+    ("Gm12345", "ENSMUSG00000099999", np.nan, "Ensembl"),  # no human ortholog
+]
+# Upper-cases to an existing human symbol but is not its ortholog in the table.
+CASE_DECOY = "Ccdc115"
+
+
+@pytest.fixture
+def table():
+    return pd.DataFrame(ORTHOLOGS, columns=TABLE_COLUMNS)
+
+
+@pytest.fixture
+def table_path(table, tmp_path):
+    path = tmp_path / "orthologs.tsv"
+    table.to_csv(path, sep="\t", index=False)
+    return str(path)
+
+
+def test_map_mouse_to_human(table):
+    mapping = map_mouse_to_human(
+        ["Cdh5", "Zfp451", "H2-Ab1", "Cd59a", "Gm12345", CASE_DECOY], table
+    )
+    assert mapping == {
+        "Cdh5": ["CDH5"],
+        "Zfp451": ["ZNF451"],
+        "H2-Ab1": ["HLA-DQB1", "HLA-DQB2"],
+        "Cd59a": ["CD59"],
+        "Gm12345": [],
+        CASE_DECOY: [],  # no fallback to upper-casing
+    }
+
+
+def test_map_ensembl_ids(table):
+    mapping = map_mouse_to_human(
+        ["ENSMUSG00000031871", "ENSMUSG00000031871.7", "ENSMUSG00000099999"], table
+    )
+    assert mapping["ENSMUSG00000031871"] == ["CDH5"]
+    assert mapping["ENSMUSG00000031871.7"] == ["CDH5"]  # version suffix ignored
+    assert mapping["ENSMUSG00000099999"] == []
+
+
+def test_collapse_to_human(table):
+    loadings = pd.DataFrame(
+        {"p0": [5.0, 1.0, 2.0, 3.0, 4.0, 9.0], "p1": [0.5, 0.1, 0.2, 0.3, 0.6, 0.9]},
+        index=["Cdh5", "Zfp451", "H2-Ab1", "Cd59a", "Cd59b", "Gm12345"],
+    )
+    human = collapse_to_human(loadings, table)
+
+    assert set(human.index) == {"CDH5", "ZNF451", "HLA-DQB1", "HLA-DQB2", "CD59"}
+    # one-to-many: both human orthologs take the mouse loading
+    assert human.loc["HLA-DQB1", "p0"] == human.loc["HLA-DQB2", "p0"] == 2.0
+    # many-to-one: per-program maximum over the mouse orthologs
+    assert human.loc["CD59", "p0"] == 4.0 and human.loc["CD59", "p1"] == 0.6
+    assert human.loc["ZNF451", "p1"] == 0.1
+
+
+def test_collapse_without_any_ortholog_fails(table):
+    with pytest.raises(ValueError, match="No mouse genes"):
+        collapse_to_human(pd.DataFrame({"p0": [1.0]}, index=["Gm12345"]), table)
+
+
+def test_load_ortholog_table_validates_columns(tmp_path):
+    bad = tmp_path / "bad.tsv"
+    pd.DataFrame({"mouse": ["Cdh5"], "human": ["CDH5"]}).to_csv(
+        bad, sep="\t", index=False
+    )
+    with pytest.raises(ValueError, match="missing columns"):
+        load_ortholog_table(str(bad))
+
+
+def _mdata(genes):
+    rng = np.random.default_rng(0)
+    rna = ad.AnnData(
+        X=rng.random((6, len(genes))).astype(np.float32),
+        var=pd.DataFrame(index=genes),
+    )
+    prog = ad.AnnData(
+        X=rng.random((6, 2)).astype(np.float32), var=pd.DataFrame(index=["0", "1"])
+    )
+    prog.varm["loadings"] = rng.random((2, len(genes))).astype(np.float32)
+    prog.uns["var_names"] = np.array(genes)
+    return md.MuData({"rna": rna, "cNMF": prog})
+
+
+def test_program_loadings_mouse_uses_orthologs(table_path):
+    genes = ["Cdh5", "Zfp451", "H2-Ab1", "Gm12345", CASE_DECOY]
+    loadings = get_program_gene_loadings(
+        _mdata(genes),
+        prog_key="cNMF",
+        data_key="rna",
+        organism="mouse",
+        ortholog_table=table_path,
+    )
+    assert set(loadings.index) == {"CDH5", "ZNF451", "HLA-DQB1", "HLA-DQB2"}
+    assert list(loadings.columns) == ["0", "1"]
+
+
+def test_program_loadings_mouse_native_symbols(table_path):
+    """map_to_human=False (MSigDB mouse gene sets) keeps mouse symbols unchanged."""
+    genes = ["Cdh5", "Zfp451"]
+    loadings = get_program_gene_loadings(
+        _mdata(genes),
+        prog_key="cNMF",
+        data_key="rna",
+        organism="mouse",
+        ortholog_table=table_path,
+        map_to_human=False,
+    )
+    assert list(loadings.index) == genes
+
+
+def test_program_loadings_human_unchanged():
+    genes = ["CDH5", "znf451"]
+    loadings = get_program_gene_loadings(
+        _mdata(genes), prog_key="cNMF", data_key="rna", organism="human"
+    )
+    assert list(loadings.index) == ["CDH5", "ZNF451"]
+
+
+def test_unsupported_organism():
+    with pytest.raises(ValueError, match="Unsupported organism"):
+        get_program_gene_loadings(
+            _mdata(["Cdh5"]), prog_key="cNMF", data_key="rna", organism="rat"
+        )
+
+
+# Download handling (network calls are replaced by stubs)
+
+ENSEMBL_HEADER = "Gene stable ID\tGene name\tHuman gene name\n"
+ENSEMBL_ROW = "ENSMUSG00000031871\tCdh5\tCDH5\n"
+
+
+def test_ensembl_requires_completion_stamp(monkeypatch):
+    """A response cut short by BioMart (no '[success]' line) is rejected."""
+    from Stage2_Evaluation.A_Metrics.src import orthologs
+
+    monkeypatch.setattr(
+        orthologs, "_download", lambda url: ENSEMBL_HEADER + ENSEMBL_ROW
+    )
+    with pytest.raises(RuntimeError, match="completion stamp"):
+        orthologs._fetch_ensembl()
+
+    monkeypatch.setattr(
+        orthologs, "_download", lambda url: ENSEMBL_HEADER + ENSEMBL_ROW + "[success]\n"
+    )
+    df = orthologs._fetch_ensembl()
+    assert df.to_dict("records") == [
+        {
+            "mouse_symbol": "Cdh5",
+            "mouse_ensembl": "ENSMUSG00000031871",
+            "human_symbol": "CDH5",
+            "source": "Ensembl",
+        }
+    ]
+
+
+def test_download_failure_suggests_ortholog_table(monkeypatch, tmp_path):
+    from Stage2_Evaluation.A_Metrics.src import orthologs
+
+    def offline():
+        raise OSError("network unreachable")
+
+    monkeypatch.setattr(orthologs, "DEFAULT_TABLE_PATH", str(tmp_path / "cache.tsv"))
+    monkeypatch.setattr(orthologs, "build_ortholog_table", offline)
+    with pytest.raises(RuntimeError, match="--ortholog_table"):
+        orthologs.load_ortholog_table()
+
+
+def test_cache_is_written_atomically(monkeypatch, tmp_path, table):
+    """The cache appears in one step (no partial file) and is reused afterwards."""
+    from Stage2_Evaluation.A_Metrics.src import orthologs
+
+    cache = tmp_path / "cache.tsv"
+    monkeypatch.setattr(orthologs, "RESOURCES_DIR", str(tmp_path))
+    monkeypatch.setattr(orthologs, "DEFAULT_TABLE_PATH", str(cache))
+    monkeypatch.setattr(orthologs, "build_ortholog_table", lambda: table)
+    replaced = []
+    real_replace = orthologs.os.replace
+
+    def record_replace(src, dst):
+        assert not cache.exists()  # nothing written to the final path before the move
+        replaced.append((src, dst))
+        real_replace(src, dst)
+
+    monkeypatch.setattr(orthologs.os, "replace", record_replace)
+    first = orthologs.load_ortholog_table()
+    assert replaced and replaced[0][1] == str(cache)
+    assert list(tmp_path.iterdir()) == [cache]  # temporary file is gone
+
+    monkeypatch.setattr(
+        orthologs, "build_ortholog_table", lambda: pytest.fail("rebuilt")
+    )
+    second = orthologs.load_ortholog_table()
+    assert len(second) == len(first)
diff --git a/tests/Script/Stage3_Interpretation/A_Plotting/Program/test_ensure_umap.py b/tests/Script/Stage3_Interpretation/A_Plotting/Program/test_ensure_umap.py
new file mode 100644
index 0000000..a740d29
--- /dev/null
+++ b/tests/Script/Stage3_Interpretation/A_Plotting/Program/test_ensure_umap.py
@@ -0,0 +1,87 @@
+"""Tests for ensure_umap reusing embeddings stored under non-scanpy obsm keys.
+
+Seurat conversions store embeddings as obsm['UMAP'] / obsm['PCA'] (often as
+DataFrames); ensure_umap should reuse them instead of computing a new UMAP.
+
+Usage (from the repo root):
+    python -m pytest tests/Script/Stage3_Interpretation/A_Plotting/Program/test_ensure_umap.py -v
+"""
+
+import anndata as ad
+import mudata as md
+import numpy as np
+import pandas as pd
+
+from Stage3_Interpretation.A_Plotting.src.utilities import ensure_umap
+
+N_CELLS = 60
+N_GENES = 40
+N_PROGRAMS = 3
+SEED = 0
+
+
+def _mdata():
+    """MuData with 'rna' and 'cNMF' modalities and no embeddings."""
+    rng = np.random.default_rng(SEED)
+    cells = [f"cell{i}" for i in range(N_CELLS)]
+    rna = ad.AnnData(
+        X=rng.poisson(3.0, size=(N_CELLS, N_GENES)).astype(np.float32),
+        obs=pd.DataFrame(index=cells),
+        var=pd.DataFrame(index=[f"Gene{i}" for i in range(N_GENES)]),
+    )
+    prog = ad.AnnData(
+        X=rng.random((N_CELLS, N_PROGRAMS)).astype(np.float32),
+        obs=pd.DataFrame(index=cells),
+        var=pd.DataFrame(index=[str(p) for p in range(N_PROGRAMS)]),
+    )
+    return md.MuData({"rna": rna, "cNMF": prog})
+
+
+def _seurat_embedding(mdata, n_cols, prefix):
+    rng = np.random.default_rng(SEED + n_cols)
+    return pd.DataFrame(
+        rng.normal(size=(N_CELLS, n_cols)),
+        index=mdata["rna"].obs_names,
+        columns=[f"{prefix}_{i + 1}" for i in range(n_cols)],
+    )
+
+
+def test_adopts_seurat_keys_and_mirrors_to_programs(capsys):
+    mdata = _mdata()
+    umap = _seurat_embedding(mdata, 3, "UMAP")  # some converters keep 3 dims
+    pca = _seurat_embedding(mdata, 10, "PC")
+    mdata["rna"].obsm["UMAP"] = umap
+    mdata["rna"].obsm["PCA"] = pca
+
+    ensure_umap(mdata, "rna", "cNMF")
+
+    for key in ["rna", "cNMF"]:
+        x_umap = mdata[key].obsm["X_umap"]
+        assert isinstance(x_umap, np.ndarray)
+        np.testing.assert_array_equal(x_umap, umap.to_numpy()[:, :2])
+        np.testing.assert_array_equal(mdata[key].obsm["X_pca"], pca.to_numpy())
+    assert "UMAP" in mdata["rna"].obsm and "PCA" in mdata["rna"].obsm
+    assert "using obsm['UMAP'] as 'X_umap'" in capsys.readouterr().out
+
+
+def test_existing_x_umap_is_not_overwritten():
+    mdata = _mdata()
+    existing = np.zeros((N_CELLS, 2))
+    for key in ["rna", "cNMF"]:
+        mdata[key].obsm["X_umap"] = existing
+    mdata["rna"].obsm["UMAP"] = _seurat_embedding(mdata, 2, "UMAP")
+
+    ensure_umap(mdata, "rna", "cNMF")
+
+    for key in ["rna", "cNMF"]:
+        np.testing.assert_array_equal(mdata[key].obsm["X_umap"], existing)
+
+
+def test_computes_umap_when_no_embedding_exists():
+    mdata = _mdata()
+
+    ensure_umap(mdata, "rna", "cNMF", n_top_genes=N_GENES, n_comps=5)
+
+    for key in ["rna", "cNMF"]:
+        assert mdata[key].obsm["X_umap"].shape == (N_CELLS, 2)
+        assert mdata[key].obsm["X_pca"].shape == (N_CELLS, 5)
diff --git a/tests/Script/test_non_perturbed.py b/tests/Script/test_non_perturbed.py
new file mode 100644
index 0000000..e6280b3
--- /dev/null
+++ b/tests/Script/test_non_perturbed.py
@@ -0,0 +1,407 @@
+"""Tests for running the pipeline on non-perturbed (no guide) and non-human data.
+
+Covers the code paths that previously assumed CRISPR guide information or human data:
+
+- Stage 2 evaluation skips trait (GWAS) enrichment for non-human organisms and fails
+  early with a clear message when perturbation association is requested without guides.
+- Stage 3 K-selection runs without trait or perturbation files (--no_trait,
+  --no_perturbation).
+- Stage 3 Excel summary runs on an h5mu without uns['guide_targets'] and, for
+  mouse data, annotates genes with their human orthologs' summaries.
+
+All inputs are small synthetic files; no network access or inference output is needed.
+
+Usage (from the repo root, in the pipeline environment):
+    python -m pytest tests/Script/test_non_perturbed.py -v
+"""
+
+import importlib.util
+import os
+import subprocess
+import sys
+from pathlib import Path
+from typing import ClassVar
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import anndata as ad
+import mudata as md
+import numpy as np
+import pandas as pd
+import pytest
+
+PIPELINE_ROOT = Path(__file__).resolve().parents[2]
+SRC = PIPELINE_ROOT / "src"
+sys.path.insert(0, str(SRC))
+
+EVAL_SCRIPT = (
+    SRC / "Stage2_Evaluation/A_Metrics/Slurm_Version/cNMF_evaluation_pipeline.py"
+)
+K_SELECTION_SCRIPT = (
+    SRC / "Stage3_Interpretation/A_Plotting/Slurm_Version/cNMF_k_selection.py"
+)
+EXCEL_SCRIPT = (
+    SRC / "Stage3_Interpretation/B_Summarization/Slurm_Version/cNMF_excel_summary.py"
+)
+
+K_VALUES = [3, 5]
+SEL_THRESH = 2.0
+SEL_STR = "2_0"
+RUN_NAME = "non_perturbed_run"
+N_CELLS = 40
+N_GENES = 25
+SEED = 0
+
+
+def _load_module(path, name):
+    spec = importlib.util.spec_from_file_location(name, path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def _make_mdata(k, rng):
+    """MuData with 'rna' and 'cNMF' modalities and no guide information."""
+    cells = [f"cell{i}" for i in range(N_CELLS)]
+    genes = [f"Gene{i}" for i in range(N_GENES)]  # mouse-style capitalization
+    rna = ad.AnnData(
+        X=rng.poisson(2.0, size=(N_CELLS, N_GENES)).astype(np.float32),
+        obs=pd.DataFrame(
+            {"sample": pd.Categorical(rng.choice(["ctrl", "treated"], N_CELLS))},
+            index=cells,
+        ),
+        # Symbols as var_names and no 'symbol' column, as in Seurat-converted data
+        var=pd.DataFrame(index=genes),
+    )
+    prog = ad.AnnData(
+        X=rng.random((N_CELLS, k)).astype(np.float32),
+        obs=pd.DataFrame(index=cells),
+        var=pd.DataFrame(index=[str(p) for p in range(k)]),
+    )
+    prog.varm["loadings"] = rng.random((k, N_GENES)).astype(np.float32)
+    prog.uns["var_names"] = np.array(genes)
+    return md.MuData({"rna": rna, "cNMF": prog})
+
+
+@pytest.fixture(scope="module")
+def run_dir(tmp_path_factory):
+    """Synthetic run directory: inference h5mu files plus Stage 2 outputs without
+    trait or perturbation results, as produced for non-perturbed mouse data."""
+    rng = np.random.default_rng(SEED)
+    out_dir = tmp_path_factory.mktemp("out")
+    run = out_dir / RUN_NAME
+    adata_dir = run / "Inference" / "adata"
+    adata_dir.mkdir(parents=True)
+
+    stability = []
+    for k in K_VALUES:
+        _make_mdata(k, rng).write(adata_dir / f"cNMF_{k}_{SEL_STR}.h5mu")
+
+        eval_dir = run / "Evaluation" / f"{k}_{SEL_STR}"
+        eval_dir.mkdir(parents=True)
+        programs = [str(p) for p in range(k)]
+        for fname in [f"{k}_GO_term_enrichment.txt", f"{k}_geneset_enrichment.txt"]:
+            pd.DataFrame(
+                {
+                    "Term": [f"TERM_{p}" for p in programs],
+                    "Adjusted P-value": rng.uniform(0, 0.1, k),
+                    "Genes": ["Gene0;Gene1"] * k,
+                    "program_name": programs,
+                }
+            ).to_csv(eval_dir / fname, sep="\t", index=False)
+        pd.DataFrame({"Total": [rng.uniform(0.2, 0.6)]}, index=["all"]).to_csv(
+            eval_dir / f"{k}_Explained_Variance_Summary.txt", sep="\t"
+        )
+        pd.DataFrame(
+            {
+                "VarianceExplained": rng.uniform(0, 0.1, k),
+                "ProgramID": programs,
+                "program_name": programs,
+            }
+        ).to_csv(eval_dir / f"{k}_Explained_Variance.txt", sep="\t", index=False)
+        stability.append(
+            {
+                "k": k,
+                "silhouette": rng.uniform(0.5, 1.0),
+                "prediction_error": rng.uniform(1, 2),
+            }
+        )
+
+    stability_file = run / "Inference" / "k_selection_stats.tsv"
+    pd.DataFrame(stability).to_csv(stability_file, sep="\t", index=False)
+    return {"out_dir": out_dir, "run": run, "stability_file": stability_file}
+
+
+# Stage 2: evaluation
+
+
+@pytest.fixture(scope="module")
+def evaluation_main():
+    return _load_module(EVAL_SCRIPT, "cNMF_evaluation_pipeline").main
+
+
+def _eval_argv(run_dir, *extra):
+    return [
+        str(EVAL_SCRIPT),
+        "--out_dir",
+        str(run_dir["out_dir"]),
+        "--run_name",
+        RUN_NAME,
+        "--K",
+        str(K_VALUES[0]),
+        "--sel_threshs",
+        str(SEL_THRESH),
+        *extra,
+    ]
+
+
+def test_trait_enrichment_skipped_for_mouse(
+    run_dir, evaluation_main, monkeypatch, capsys
+):
+    """--Perform_trait with a non-human organism is skipped with a message,
+    without requiring --gwas_data_path."""
+    monkeypatch.setattr(
+        sys, "argv", _eval_argv(run_dir, "--Perform_trait", "--organism", "mouse")
+    )
+    assert evaluation_main() == 0
+
+    assert "skipping trait (GWAS) enrichment" in capsys.readouterr().out
+    eval_dir = run_dir["run"] / "Evaluation" / f"{K_VALUES[0]}_{SEL_STR}"
+    assert not (eval_dir / f"{K_VALUES[0]}_trait_enrichment.txt").exists()
+
+
+def test_trait_enrichment_still_requires_gwas_for_human(
+    run_dir, evaluation_main, monkeypatch
+):
+    """Human behavior is unchanged: --Perform_trait needs --gwas_data_path."""
+    monkeypatch.setattr(
+        sys, "argv", _eval_argv(run_dir, "--Perform_trait", "--organism", "human")
+    )
+    with pytest.raises(SystemExit):
+        evaluation_main()
+
+
+def test_perturbation_without_guides_fails_clearly(
+    run_dir, evaluation_main, monkeypatch
+):
+    """Fails before any metric is computed (no categorical results written)."""
+    monkeypatch.setattr(
+        sys,
+        "argv",
+        _eval_argv(
+            run_dir,
+            "--Perform_perturbation",
+            "--Perform_categorical",
+            "--categorical_key",
+            "sample",
+        ),
+    )
+    with pytest.raises(ValueError, match="drop --Perform_perturbation"):
+        evaluation_main()
+    eval_dir = run_dir["run"] / "Evaluation" / f"{K_VALUES[0]}_{SEL_STR}"
+    assert not (
+        eval_dir / f"{K_VALUES[0]}_categorical_association_results.txt"
+    ).exists()
+
+
+def test_perturbation_with_wrong_prog_key_names_the_modality(
+    run_dir, evaluation_main, monkeypatch
+):
+    monkeypatch.setattr(
+        sys, "argv", _eval_argv(run_dir, "--Perform_perturbation", "--prog_key", "prog")
+    )
+    with pytest.raises(ValueError, match="check --prog_key"):
+        evaluation_main()
+
+
+def test_organism_is_case_insensitive(run_dir, evaluation_main, monkeypatch, capsys):
+    monkeypatch.setattr(
+        sys, "argv", _eval_argv(run_dir, "--Perform_trait", "--organism", "Mouse")
+    )
+    assert evaluation_main() == 0
+    assert "skipping trait (GWAS) enrichment" in capsys.readouterr().out
+
+
+def test_unsupported_organism_is_rejected(run_dir, evaluation_main, monkeypatch):
+    monkeypatch.setattr(sys, "argv", _eval_argv(run_dir, "--organism", "rat"))
+    with pytest.raises(SystemExit):
+        evaluation_main()
+
+
+# Stage 3: K-selection plotting
+
+
+def test_k_selection_without_trait_and_perturbation(run_dir, tmp_path):
+    save_dir = tmp_path / "K_selection"
+    cmd = [
+        sys.executable,
+        str(K_SELECTION_SCRIPT),
+        "--output_directory",
+        str(run_dir["out_dir"]),
+        "--run_name",
+        RUN_NAME,
+        "--save_folder_name",
+        str(save_dir),
+        "--eval_folder_name",
+        str(run_dir["run"] / "Evaluation"),
+        "--K",
+        *[str(k) for k in K_VALUES],
+        "--sel_threshs",
+        str(SEL_THRESH),
+        "--stability_file",
+        str(run_dir["stability_file"]),
+        "--no_trait",
+        "--no_perturbation",
+    ]
+    env = {**os.environ, "MPLBACKEND": "Agg"}
+    result = subprocess.run(cmd, capture_output=True, text=True, env=env, check=False)
+    assert result.returncode == 0, result.stdout[-3000:] + result.stderr[-3000:]
+
+    produced = {p.name for p in save_dir.iterdir()}
+    assert f"K-selection_panel_{SEL_THRESH}.png" in produced
+    assert f"Enrichment_{SEL_THRESH}_go_terms.png" in produced
+    assert f"Enrichment_{SEL_THRESH}_traits.png" not in produced
+    assert not any(name.startswith("Perturbation_") for name in produced)
+
+
+def test_k_selection_requires_files_by_default(run_dir, tmp_path):
+    """Without the new flags, missing trait/perturbation files are still an error."""
+    cmd = [
+        sys.executable,
+        str(K_SELECTION_SCRIPT),
+        "--output_directory",
+        str(run_dir["out_dir"]),
+        "--run_name",
+        RUN_NAME,
+        "--save_folder_name",
+        str(tmp_path / "K_selection"),
+        "--eval_folder_name",
+        str(run_dir["run"] / "Evaluation"),
+        "--K",
+        *[str(k) for k in K_VALUES],
+        "--sel_threshs",
+        str(SEL_THRESH),
+        "--Conditions",
+        "ctrl",
+        "--stability_file",
+        str(run_dir["stability_file"]),
+    ]
+    result = subprocess.run(cmd, capture_output=True, text=True, check=False)
+    assert result.returncode != 0
+    assert "required evaluation file(s) not found" in result.stdout
+
+
+def test_k_selection_panel_hides_regulator_row(tmp_path):
+    from Stage3_Interpretation.A_Plotting.src import k_selection_plots as ksp
+
+    stats = pd.DataFrame(
+        {"k": K_VALUES, "silhouette": [0.8, 0.7], "prediction_error": [1.5, 1.2]}
+    )
+    counts = pd.DataFrame(
+        {"go_terms": [3, 4], "genesets": [2, 3], "traits": [0, 0]}, index=K_VALUES
+    )
+    variance = {3: 0.3, 5: 0.4}
+    figures = []
+    original_subplots = ksp.plt.subplots
+
+    def capture_subplots(*args, **kwargs):
+        fig, axes = original_subplots(*args, **kwargs)
+        figures.append(axes)
+        return fig, axes
+
+    ksp.plt.subplots = capture_subplots
+    try:
+        for panel_fn in [
+            ksp.plot_k_selection_panel,
+            ksp.plot_k_selection_panel_no_traits,
+        ]:
+            panel_fn(
+                stats, counts, None, variance, folder_name=str(tmp_path), file_name="p"
+            )
+    finally:
+        ksp.plt.subplots = original_subplots
+
+    for axes in figures:
+        assert not axes[2, 0].get_visible()
+        assert not axes[2, 1].get_visible()
+
+
+# Stage 3: Excel summary
+
+
+class _FakeMyGene:
+    """Offline stand-in for mygene.MyGeneInfo that records the species queried."""
+
+    species_seen: ClassVar[list] = []
+
+    def querymany(self, genes, species=None, **kwargs):
+        _FakeMyGene.species_seen.append(species)
+        return [{"query": g, "summary": f"summary of {g}"} for g in genes]
+
+
+def test_excel_summary_without_guides(run_dir, monkeypatch, capsys, tmp_path):
+    """Non-perturbed mouse data: no Targets Summary sheet, and gene summaries come
+    from human orthologs (synthetic table: even-numbered genes have an ortholog)."""
+    excel = _load_module(EXCEL_SCRIPT, "cNMF_excel_summary")
+    from Stage3_Interpretation.B_Summarization.src import Compile_excel_sheet
+
+    monkeypatch.setattr(Compile_excel_sheet, "MyGeneInfo", _FakeMyGene)
+    _FakeMyGene.species_seen.clear()
+    ortholog_table = tmp_path / "orthologs.tsv"
+    pd.DataFrame(
+        {
+            "mouse_symbol": [f"Gene{i}" for i in range(0, N_GENES, 2)],
+            "mouse_ensembl": pd.NA,
+            "human_symbol": [f"HSGENE{i}" for i in range(0, N_GENES, 2)],
+            "source": "test",
+        }
+    ).to_csv(ortholog_table, sep="\t", index=False)
+
+    k = K_VALUES[1]
+    monkeypatch.setattr(
+        sys,
+        "argv",
+        [
+            str(EXCEL_SCRIPT),
+            "--out_dir",
+            str(run_dir["out_dir"]),
+            "--run_name",
+            RUN_NAME,
+            "--K",
+            str(k),
+            "--sel_thresh",
+            str(SEL_THRESH),
+            "--organism",
+            "mouse",
+            "--categorical_key",
+            "sample",
+            "--num_gene",
+            "5",
+            "--ortholog_table",
+            str(ortholog_table),
+        ],
+    )
+    assert excel.main() == 0
+
+    assert "skipping the Targets Summary sheet" in capsys.readouterr().out
+    assert set(_FakeMyGene.species_seen) == {"human"}
+    save_path = run_dir["run"] / "Interpretation" / "Summary_table" / f"{k}_{SEL_STR}"
+    sheets = pd.ExcelFile(save_path / f"cNMF_{k}_{SEL_STR}.xlsx").sheet_names
+    assert "Summary" in sheets and "Program Loadings" in sheets
+    assert "Targets Summary" not in sheets
+    assert not (save_path / f"Targets_Summary_{k}_{SEL_STR}.tsv").exists()
+
+    loadings = pd.read_csv(
+        save_path / f"Program_Loadings_{k}_{SEL_STR}.tsv",
+        sep="\t",
+        keep_default_na=False,  # keep the literal "N/A"
+    )
+    for _, row in loadings.iterrows():
+        n = int(row["Gene"].removeprefix("Gene"))
+        if n % 2 == 0:
+            assert row["Human ortholog"] == f"HSGENE{n}"
+            assert row["Annotation"] == f"HSGENE{n}: summary of HSGENE{n}"
+        else:
+            assert row["Human ortholog"] == "" and row["Annotation"] == "N/A"