pirlygenes 5.2.5

Curated cancer gene sets and reference expression data. Analysis lives in `trufflepig`.

pirlygenes

Curated cancer gene knowledge + reference expression data.

pirlygenes is the data layer for cancer RNA analysis. It ships:

• Curated gene-set CSVs — therapy targets, cancer-testis antigens, surfaceome, cancer-driver / cancer-key panels, lineage panels, rule tables, the cancer-type registry, narrative gene sets, …
• Curated gene families keyed by Ensembl ID — mtDNA, NUMTs, rRNA
  • pseudogenes, ribosomal proteins, histones, hemoglobins, immune-receptor segments, small ncRNAs, MALAT1/NEAT1.
• Reference expression matrices — pan-cancer TCGA × HPA panel, HPA cell-type expression, ESTIMATE signatures.
• Mechanical transforms on the data — normalize_expression, fpkm_to_tpm, renormalize_to_million, tpm_to_housekeeping_normalized, classify_gene_qc, and aggregate_gene_expression.
• Cohort-baseline constants (TCGA_MEDIAN_PURITY).

Analysis-layer code (CLI, plotting, sample-QC narration, deconvolution, signature scoring) lives in trufflepig.

Install

pip install pirlygenes

Run analyses with trufflepig (distributed on PyPI as pirl-trufflepig — the bare trufflepig name is owned by an unrelated package; the command + Python import are both still trufflepig):

pip install pirl-trufflepig
trufflepig run --sample expr.tsv --workspace out --cancer-type PRAD

Python API

Most accessors are re-exported from the top-level package, so from pirlygenes import pan_cancer_expression works for any of the ~75 names in pirlygenes.__all__. The submodule paths below are the canonical home and stay stable across versions.

Gene-set panels and resolvers

from pirlygenes.gene_sets_cancer import (
    CTA_gene_names,                   # ~258 cancer-testis antigens
    surface_protein_gene_names,       # 2,799 surfaceome genes
    cancer_surfaceome_gene_names,     # 147 tumor-specific surface targets
    therapy_target_gene_names,        # modality: "ADC", "CAR-T", "TCR-T",
                                      #   "bispecific-antibodies", "radioligand",
                                      #   "multispecific-TCE" (plus trial / approved
                                      #   sub-keys, e.g. "ADC-trials")
    cancer_type_registry,             # cancer-type registry DataFrame (125 rows)
    resolve_cancer_type,              # "prostate" / "PRAD" / "SARC_DDLPS" → registry code
    CANCER_TYPE_NAMES,                # registry-backed {code: display_name} view
    lineage_genes_by_cancer_type,     # lineage panels
    cancer_family_panels,             # broad-family aggregate panels (keys: PROSTATE,
                                      #   CRC, GASTRIC, ESCA_SQ, SQUAMOUS, MESENCHYMAL,
                                      #   RENAL, GLIAL, MELANOCYTIC)
    cancer_family_panel,              # e.g. cancer_family_panel("MESENCHYMAL")
    therapy_benefit_toxicity_evidence,# curated clinical benefit/toxicity rows
    housekeeping_gene_ids,
    mitochondrial_gene_ids,
    tme_marker_gene_ids,              # tumor microenvironment markers
    degradation_gene_pairs,           # for RNA degradation index
    TCGA_MEDIAN_PURITY,               # per-cohort median tumor purity (Aran et al., 2015)
)
from pirlygenes.gene_sets_cancer import (
    fusion_expression_effect_rules_df,
    mutation_expression_effect_rules_df,
    rare_cancer_fusion_rules_df,
    rare_cancer_rna_surrogate_rules_df,
    degenerate_subtype_pairs_df,
    fusion_surrogate_expression_df,
    narrative_gene_sets_df,
    narrative_gene_set,
    disease_state_rules_df,
)
from pirlygenes.load_dataset import get_data, get_all_csv_paths
from pirlygenes.gene_ids import (
    find_canonical_gene_ids_and_names,
    get_alias_as_list,                # symbol → list of known synonyms
    get_reverse_alias_as_list,        # canonical → all symbols that map to it
)
from pirlygenes.gene_names import display_name, short_gene_name, aliases
from pirlygenes.gene_families import (
    gene_family_for_ensembl_id,    # ENSG → family name (or None)
    gene_family_for_symbol,        # Symbol → family name (or None)
    gene_family_names,             # list of every shipped family
    gene_family_ids,               # set of ENSGs in one named family
    gene_family_symbols,           # set of Symbols in one named family
    gene_family_table,             # long-form DataFrame across all families
    # Typed per family (ID and symbol variants for each)
    numt_pseudogene_ids,
    numt_pseudogene_symbols,
    nuclear_retained_lncrna_ids,   # MALAT1, NEAT1 (ENE-stabilized)
    nuclear_retained_lncrna_symbols,
    rrna_and_pseudogene_ids,
    rrna_and_pseudogene_symbols,
    ribosomal_protein_ids,
    ribosomal_protein_pseudogene_ids,
    small_noncoding_rna_ids,       # snoRNAs, snRNAs, miRNAs, Y RNAs, ...
    histone_gene_ids,
    hemoglobin_gene_ids,
    immune_receptor_segment_ids,   # IG/TR V/D/J/C segments
)

Expression matrices and transforms

from pirlygenes.expression import (
    # Reference matrices (long- and wide-form)
    available_cancer_expression_references,
    cancer_reference_expression,       # source-agnostic non-TCGA references
    pan_cancer_expression,            # 19,784 genes × expression reference columns
    cancer_expression,                # one cancer type, clean TPM by default
    cancer_enriched_genes,            # genes enriched in one cancer vs the others
    hpa_cell_type_expression,         # HPA single-cell consensus
    estimate_signatures,              # Yoshihara 2013 stromal/immune sigs

    # Rescaling primitives — pure math on expression matrices
    add_tpm_columns_from_fpkm,        # preserve FPKM and append TPM companions
    normalize_expression,             # zero technical-RNA rows + renormalize
    fpkm_to_tpm,                      # rescale each column to sum to 10⁶
    percentile_rank_expression,       # within-column percentile ranks
    renormalize_to_million,           # bare utility for column rescaling
    tpm_to_housekeeping_normalized,   # divide each column by housekeeping geomean
    log1p_transform,                  # natural log1p over selected value columns
    normalize_technical_rna_columns,
    normalize_technical_rna_long_table,

    # Classifier — symbol/ENSG → QC class for tech-RNA flagging
    classify_gene_qc,
    is_rescue_feature,
    GeneQcClass,

    # Transcript → gene rollup
    aggregate_gene_expression,
)

pan_cancer_expression() exposes a single normalize= keyword so callers don't have to chain the primitives by hand. It accepts None, a string, or a list of strings. The default is normalize="tpm_clean" (equivalent to normalize=["tpm_clean"]): TCGA *_FPKM columns are preserved for provenance, deterministic TCGA *_TPM companions are added, HPA *_nTPM columns are preserved, and cleaned TPM-scale analysis columns are added as *_TPM_clean / *_nTPM_clean.

# Zero mtDNA / NUMT / rRNA / MALAT1+NEAT1 rows across TPM-scale analysis
# columns and pin each column sum back at 1e6. Base *_nTPM, *_TPM, and
# *_FPKM columns remain unchanged; clean values are added as *_nTPM_clean
# and *_TPM_clean companion columns.
pan_cancer_expression()                          # normalize="tpm_clean"

# Raw/provenance view: raw <code>_FPKM from TCGA, <tissue>_nTPM from HPA,
# and deterministic <code>_TPM companions for analysis.
pan_cancer_expression(normalize=None)

# Add deterministic <code>_TPM companions derived from the FPKM columns.
pan_cancer_expression(normalize="tpm")

# Add explicit natural-log analysis columns while preserving raw/base values.
pan_cancer_expression(normalize="tpm_log1p")
pan_cancer_expression(normalize="tpm_clean_log1p")

# Add housekeeping-normalized TPM-scale columns. Percentile ranks are also
# available via normalize="percentile" as *_percentile columns.
pan_cancer_expression(normalize="hk")

# Combine modes in one call; tpm_clean, hk, and percentile each imply "tpm".
pan_cancer_expression(normalize=["tpm_clean", "hk", "percentile"])

cancer_expression(cancer_type) uses the same default analysis view across reference sources: clean TPM (normalize="tpm_clean"). For TCGA-backed cancer types, housekeeping-normalized values are available only when explicitly requested with normalize="hk" or normalize="housekeeping". cancer_reference_expression() exposes packaged non-TCGA tumor references through the same raw TPM / clean TPM contract; current bundled sources include CLL-map (CLL), MMRF CoMMpass (MM), and TARGET ALL (B_ALL, T_ALL).

The older pan_cancer_expression() kwargs (technical_rna_normalize, remove_noncoding, and renormalize_to_million) have been removed. Use normalize="tpm_clean" for the TPM-scaled, technical-RNA-cleaned view; compose normalize_expression() / renormalize_to_million() directly when you need lower-level transforms.

The gene-family panels are ENSG-keyed sets derived from every installed Ensembl release (numt-pseudogenes.csv, nuclear-retained-lncrnas.csv, etc.); pirlygenes.expression.qc reads them as the source of truth for classify_gene_qc lookup. Mitochondrial-DNA membership is sourced from the curated mitochondrial-genes.csv (with a semantic Role column). Regenerate the derived CSVs with python scripts/generate_gene_family_sets.py after the upstream regex panel changes.

What's bundled (pirlygenes/data/)

Every CSV ships in the wheel under pirlygenes/data/. The "Primary accessor" column points at the typed Python entry point; any CSV listed as get_data("…") has no named accessor and is meant to be read raw via the generic loader.

File	Primary accessor
ADC-approved.csv, ADC-trials.csv, ADC-withdrawn.csv, CAR-T-approved.csv, TCR-T-trials.csv, TCR-T-approved.csv, bispecific-antibodies-approved.csv, multispecific-tcell-engager-trials.csv, radioligand-targets.csv	therapy_target_gene_names(modality) / therapy_target_gene_ids(modality)
cancer-surfaceome.csv	cancer_surfaceome_gene_names(), cancer_surfaceome_evidence()
surface-proteins.csv	surface_protein_gene_names(), surface_protein_evidence()
cancer-testis-antigens.csv	CTA_gene_names(), CTA_evidence()
cancer-driver-genes.csv	get_data("cancer-driver-genes")
cancer-driver-variants.csv	get_data("cancer-driver-variants")
cancer-key-genes.csv	cancer_key_genes_df()
therapy-benefit-toxicity-evidence.csv	therapy_benefit_toxicity_evidence()
cancer-type-registry.csv	cancer_type_registry(), CANCER_TYPE_NAMES, resolve_cancer_type(), cancer_types_in_family(), cancer_types_by_tissue(), cancer_type_subtypes_of()
cancer-family-panels.csv	cancer_family_panels(), cancer_family_panel(name), cancer_family_panels_df()
cancer-type-genes.csv	cancer_type_gene_sets(cancer_type)
lineage-genes.csv	lineage_genes_df(), lineage_genes_by_cancer_type(), lineage_gene_ids(cancer_type), lineage_gene_symbols(cancer_type)
mutation-expression-effects.csv	mutation_expression_effect_rules_df()
fusion-expression-effects.csv	fusion_expression_effect_rules_df()
rare-cancer-fusion-rules.csv	rare_cancer_fusion_rules_df()
rare-cancer-rna-surrogates.csv	rare_cancer_rna_surrogate_rules_df()
degenerate-subtype-pairs.csv	degenerate_subtype_pairs_df()
fusion-surrogate-expression.csv	fusion_surrogate_expression_df()
disease-state-rules.csv	disease_state_rules_df()
narrative-gene-sets.csv	narrative_gene_sets_df(), narrative_gene_set(name)
housekeeping-genes.csv	housekeeping_gene_names(), housekeeping_gene_ids()
mitochondrial-genes.csv	mitochondrial_genes_df(role=...), mitochondrial_gene_ids(), mitochondrial_gene_names()
culture-stress-genes.csv	culture_stress_genes_df(), culture_stress_gene_ids(), culture_stress_gene_names()
tme-markers.csv	tme_markers_df(), tme_marker_gene_ids(), tme_marker_gene_names()
degradation-gene-pairs.csv	degradation_gene_pairs_df(), degradation_gene_pairs()
ffpe-sensitive-markers.csv	ffpe_sensitive_markers_df(direction=...)
artifact-expectations.csv	get_data("artifact-expectations")
numt-pseudogenes.csv	numt_pseudogene_ids(), numt_pseudogene_symbols()
nuclear-retained-lncrnas.csv	nuclear_retained_lncrna_ids(), nuclear_retained_lncrna_symbols()
rrna-and-pseudogenes.csv	rrna_and_pseudogene_ids(), rrna_and_pseudogene_symbols()
ribosomal-protein-genes.csv	ribosomal_protein_ids(), ribosomal_protein_symbols()
ribosomal-protein-pseudogenes.csv	ribosomal_protein_pseudogene_ids(), ribosomal_protein_pseudogene_symbols()
small-noncoding-rnas.csv	small_noncoding_rna_ids(), small_noncoding_rna_symbols()
histone-genes.csv	histone_gene_ids(), histone_gene_symbols()
hemoglobin-genes.csv	hemoglobin_gene_ids(), hemoglobin_gene_symbols()
immune-receptor-segments.csv	immune_receptor_segment_ids(), immune_receptor_segment_symbols()
pan-cancer-expression.csv	pan_cancer_expression(), cancer_expression(cancer_type), cancer_enriched_genes(cancer_type)
hpa-cell-type-expression.csv	hpa_cell_type_expression()
estimate-signatures.csv	estimate_signatures()
gene-sets.csv	get_data("gene-sets") (catalog of named sets)
therapy-response-signatures.csv	get_data("therapy-response-signatures")
ensembl-id-aliases.csv	get_data("ensembl-id-aliases") (consumed internally by gene_ids)
extra-tx-mappings.csv	get_data("extra-tx-mappings") (consumed internally by gene_ids, aggregate_gene_expression)

Gene-family CSVs (numt-pseudogenes.csv, nuclear-retained-lncrnas.csv, rrna-and-pseudogenes.csv, ribosomal-protein splits, small-noncoding-rnas.csv, histone-genes.csv, hemoglobin-genes.csv, immune-receptor-segments.csv) are derived — generated by scripts/generate_gene_family_sets.py walking every installed Ensembl release. Re-run the script after the upstream regex panel in pirlygenes.expression.qc.classify_gene_qc changes. mitochondrial-genes.csv is curated by hand (the 37-row mtDNA set with a semantic Role column).

Where the boundary is

pirlygenes owns curated reference data and the mechanical operations on it — gene-set lookups, expression-matrix accessors, FPKM↔TPM, housekeeping rescaling, technical-RNA masking, transcript→gene rollup. Anything that requires interpretive judgment (per-sample QC narration, library-prep auto-detection, deconvolution pipelines, signature scoring, rescue heuristics) lives in pirl-trufflepig, which depends on this package.

Downstream consumers pick their level:

• "I just want the data and its obvious transforms" → pirlygenes only.
• "I want to run a deconvolution / signature / report pipeline" → pirl-trufflepig (which pulls in pirlygenes).

Migrating from pirlygenes 4.x or 5.0.x

Only relevant if you're upgrading. Fresh 5.1+ installs can skip this.

The expression matrices and CLI ran a brief migration through 5.0.0–5.0.2 where the data lived in trufflepig. As of 5.1.0 the expression data is back in pirlygenes and the boundary is the one described above.

Was somewhere	Is now
pirlygenes CLI (4.x: analyze, compare-analyze, plot-*, data, cancers)	trufflepig run, trufflepig compare, trufflepig plot-*, trufflepig data, trufflepig cancers
4.x: from pirlygenes import infer_sample_context, SampleContext, plot_*	from trufflepig.sample_context import …, from trufflepig.plot import …
4.x: from pirlygenes.tumor_purity import TCGA_MEDIAN_PURITY	from pirlygenes.gene_sets_cancer import TCGA_MEDIAN_PURITY
4.x or 5.0.x: from pirlygenes.gene_sets_cancer import pan_cancer_expression	from pirlygenes.expression import pan_cancer_expression (or from pirlygenes import pan_cancer_expression)
5.0.x: from trufflepig.reference import pan_cancer_expression	from pirlygenes.expression import pan_cancer_expression
5.0.x: from trufflepig.expression_normalize import normalize_expression, fpkm_to_tpm	from pirlygenes.expression import normalize_expression, fpkm_to_tpm
5.0.x: from trufflepig.expression_qc import classify_gene_qc	from pirlygenes.expression import classify_gene_qc
from pirlygenes.cli import analyze, compare_analyze (Python API)	from trufflepig.main import analyze, compare_analyze

Unchanged across all versions: gene_sets_cancer.* accessors, load_dataset.*, gene_ids.*, gene_names.*.

If the pirlygenes console-script is still on PATH from a prior install, it now prints a one-line "moved to trufflepig" notice and exits 2.

Migration history

• v5.2.0 — add normalize= presets for TPM-scaled and technical-RNA-cleaned expression accessors, derive <TCGA>_TPM columns from the ID-keyed pan-cancer <TCGA>_FPKM columns, and remove deconvolution-derived reference tables from the package.
• v5.1.0 — restore expression matrices to pirlygenes and add pirlygenes.expression with the rescaling primitives, the QC classifier, and the transcript→gene aggregator. Closes pirlygenes#246 and #247.
• v5.0.0 – v5.0.2 — analysis CLI, plotting, and expression matrices briefly moved to trufflepig. 5.0.0's migration message pointed at pip install trufflepig, which on PyPI is an unrelated package; 5.0.1 corrected it to pip install pirl-trufflepig; 5.0.2 added perf / caching polish.
• v4.x — combined data + analysis package.

See pirl-unc/trufflepig#1 for the analysis-migration umbrella and pirl-unc/pirlygenes#119 for the original deprecation thread.

License

Apache 2.0 — see LICENSE.