Depmap

DepMap — Cancer Dependency Map

When to use

Use this skill to rank dependencies in cancer models, compare prespecified biomarker cohorts, inspect co-essentiality, or relate genetic dependencies to PRISM compound response. These analyses generate target and synthetic-lethality hypotheses; they do not establish clinical efficacy, a therapeutic window, or a causal interaction.

DepMap integrates Broad and Sanger CRISPR screens and hosts independent RNAi and drug-screen datasets. Match each question to its assay and pinned release. RNAi DEMETER2 is a different perturbation modality, not an older CRISPR scoring method.

1. Discover a release, then obtain its files

Use DepMap Downloads and the selected release's README and release notes. On review, the live catalogue's latest DepMap Public release was 26Q1, dated 2026-04-01. Rediscover before new work; a release name or expected quarter is not evidence a release is available.

Verified public endpoint

GET https://depmap.org/portal/api/no-captcha/download/files returns a complete CSV catalogue, with no request body, API key, or pagination parameters. Required metadata columns are release, release_date, filename, and md5_hash. The live response also has url: older files can have links, while recent release rows have blank links. All 85 entries for 26Q1 had blank url values at review. Treat availability per row, not per endpoint.

The staff announcement introduced this metadata route in July 2026. The older https://depmap.org/portal/api/download/files catalogue can supply download links but returned an HTML verification page during review, including with HTTP 200. Validate content before parsing. Obtain current files through the portal when verification is required. Refresh expiring signed links immediately before use; do not fabricate storage URLs or assume every new release is on Figshare.

No supported contract was verified for the former /api/gene?gene_id=... or /api/data/gene_dependency examples. Use local release matrices for gene slices. Do not assume a Python package named depmap is an official portal client.

From the skill root, use the bundled helpers:

from scripts.depmap_data import fetch_catalogue, select_file, verify_md5

catalogue = fetch_catalogue()
public = catalogue[catalogue["release"].str.startswith("DepMap Public ")]
print(public[["release", "release_date"]].drop_duplicates()
      .sort_values("release_date", ascending=False).head(12))

# Explicit example pin; inspect discovery output before selecting your release.
release = "DepMap Public 26Q1"
record = select_file(catalogue, release, "CRISPRGeneEffect.csv")
print(record[["release", "filename", "md5_hash"]])
# After obtaining this file from the portal:
# verify_md5("CRISPRGeneEffect.csv", record["md5_hash"])

Save the selected metadata, retrieval date, release citation, local checksum, and README alongside analysis outputs. Record source dataset terms separately from this skill's license; newer release terms differ from older Figshare releases. For a missing published checksum, retain a local SHA-256 and explicitly state that it was not compared with a publisher checksum.

2. Inspect file contracts before joining

The following names were verified in the 26Q1 catalogue. Check that release's README for units and identifier level before treating a file as a numeric matrix.

Current model annotations include CellLineName, OncotreeLineage, OncotreePrimaryDisease, and OncotreeSubtype. Legacy sample_info.csv fields (DepMap_ID, lineage, primary_disease) require an explicit conversion. Model.csv includes models without CRISPR measurements: absence from the effect matrix is not a nondependency score.

Omics may be indexed by SequencingID, ModelConditionID, or ModelID. For a basal model analysis, use the release's IsDefaultEntryForModel == Yes flag, then require one row per ModelID. The condition-level flag IsDefaultEntryForMC answers a different question. Subset the assay/datatype before selecting defaults from a mapping table. Never average repeated conditions or count them as independent models without an explicit scientific design.

See the mapping guide source and metadata definitions.

3. Interpret Chronos and inspect a target

Chronos gene effect is continuous and unbounded. More negative values indicate stronger loss of fitness. In the normalized release matrix, approximately 0 is the nonessential-control anchor and −1 is the common-essential-control anchor; −1 is not a dependency boundary. A filter such as ≤ −0.5 is exploratory, not a p-value, FDR, or probability cutoff. Positive effects may reflect growth advantage or technical noise and need validation.

For binary calls, inspect the selected file's statistic and direction. A high dependency probability and a low FDR are different rules. Do not silently convert between them. Use release-matched positive/negative control lists and QC outputs. See score and QC caveats.

The following local-data examples are illustrative until run against your chosen files. Helper behavior is tested using synthetic fixtures; current bulk matrices were not downloaded during this review.

from scripts.depmap_data import load_gene_effect, load_models, gene_profile

effects = load_gene_effect("CRISPRGeneEffect.csv")
models = load_models("Model.csv")
profile = gene_profile(effects, models, "KRAS (3845)")
print(profile[["CellLineName", "OncotreeLineage", "gene_effect"]].head(20))

# Inspect actual annotation values, then choose an exact cohort.
print(profile["OncotreeLineage"].value_counts())
known = profile.dropna(subset=["OncotreeLineage"])
lung = known.loc[known["OncotreeLineage"].eq("Lung"), "gene_effect"]
other = known.loc[~known["OncotreeLineage"].eq("Lung"), "gene_effect"]
if lung.empty or other.empty:
    raise ValueError("Both cohorts require observed effects and known lineage")
print({"n_lung": len(lung), "n_other": len(other),
       "other_minus_lung_mean": other.mean() - lung.mean(),
       "lung_fraction_below_exploratory_cutoff": (lung <= -0.5).mean()})

This is a descriptive comparison against other cancer models, not against normal tissue. Keep the denominator and missingness counts. Preserve complete gene labels; a symbol can be ambiguous, and the helper refuses ambiguous symbol resolution.