Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-13 18:23 UTC

Teichlab / Genes2Genes

Aligning gene expression trajectories of single-cell reference and query systems

Jupyter NotebookMIT★ 93 stars⑂ 13 forkssince Oct 2022View on GitHub ↗

Teichlab/Genes2Genes holds a health index of 42 out of 100, placing it in the Weak band. It scores highest on Engineering Quality (58/100) and lowest on Vitality (30/100). It was last updated 47 days ago. A single contributor accounts for most of its recent work.

42
overall / 100
Weak

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

42
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 45 is calibrated to 42 on the published index scale (record calibration 2026-08-02).

Ownership

Teichmann GroupOrganization
351 followers87 public repossince Aug 2015

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIgenes2genes0.2.0-1877 days agosingle-celltrajectory-alignmentdynamic-programming

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

30At Risk · 21% of overall
How it's scored
18/36Push recencylast push 47 days ago
0.7/36Commit cadence1/52 weeks with commits
2.7/18Commit volume1 commits in the last year
0/10OpenSSF Scorecard: Maintained1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year1
human_commit_share1
days_since_last_push47
active_weeks_last_year1
How it's scored
27/27Ships releases1 releases published
0/36Release recencylatest release 885 days ago
12.6/27Release cadencecadence unknown (single release)
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count1
latest_release_tagv0.1.0
releases_from_tagsno
days_since_latest_release885
mean_days_between_releases
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

45Weak · 17% of overall
How it's scored
31.9/60Stars93 stars
9/25Forks13 forks
0/15Watchers2 watchers
Inputs used
forks13
stars93
watchers2
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

47Weak · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
1/22.5Commit distributiontop contributor authored 96% of commits
4.1/13.5Contributor breadth3 contributors
3/10OpenSSF Scorecard: Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled3
top_contributor_share0.956
How it's scored
17.3/42Issue resolution41% of issues closed
30/30PR acceptance1/1 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs1
open_issues10
closed_issues7
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.412
closed_unmerged_prs0
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
18.3/25Owner reach351 followers of Teichlab
25/25Track record87 public repos, account ~11 yr old
Inputs used
followers351
owner_typeOrganization
is_verifiedno
owner_loginTeichlab
public_repos87
account_age_days4,058
How it's scored
25/25Published & resolvable1 package(s) on pypi
4/35Publish recencylatest publish 877 days ago
4/20Version history1 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesgenes2genes
ecosystemspypi
any_deprecatedno
min_days_since_publish877

Engineering Quality

Are baseline engineering and documentation practices in place?

58Moderate · 19% of overall
How it's scored
24/24CI workflows1 workflow(s)
0/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsno
has_editorconfigno
has_linter_configno
has_precommit_configno
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://teichlab.github.io/Genes2Genes/
10/10Repository description
10/10Topics9 topics
10/10Wiki
Inputs used
topicsdynamic-programming, minimum-message-length, bayesian-inference, python, scrna-seq, single-cell, human-cell-atlas, pseudotime-trajectory, trajectory-alignment
has_wikiyes
homepagehttps://teichlab.github.io/Genes2Genes/
docs_sitehttps://teichlab.github.io/Genes2Genes/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

47Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
0/2.5CI-Testsno data
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 0/30 approved changesets -- score normalized to 0
0.8/2.5Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0/7.5Maintained1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTno SAST tool detected
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated14
scorecard_versionv5.5.0
checks_inconclusive4
scorecard_aggregate3.4
Excluded from scoring (no data or not applicable): Branch-Protection, CI-Tests, Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages73
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:genes2genes@0.2.0 runtime dependency closure — what installing the published package pulls in — 73 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

31At Risk · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0.5/40Legible commit history1 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.01
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapdoc/Makefile
0/22Automated tests
0/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsno
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesdoc/Makefile
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codeJupyter Notebook without a type-check config
55/55Manageable file sizes0/12 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes38,805
source_files_sampled12
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesnotebooks
Inputs used
example_dirsnotebooks
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

93GitHub stars
3contributors
1commits, last 12 months
47days since last push
1releases
1bus factor
10open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'genes2genes' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

Star and fork history 0 ★ / 13 ⇿
0Stars
13Forks
1Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

03581013151312023-032024-092026-02
Major 0Minor 1Patch 0

Each point covers 3 days.

OpenSSF Scorecard 3.4 / 10
3.4aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-13 18:23 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 14
RegistryPackageVersion constraintManifest
PyPIanndatapyproject.toml
PyPIregexpyproject.toml
PyPItabulatepyproject.toml
PyPIgseapypyproject.toml
PyPIblitzgseapyproject.toml
PyPItorchpyproject.toml
PyPIscipypyproject.toml
PyPIscikit-learnpyproject.toml
PyPItqdmpyproject.toml
PyPImatplotlibpyproject.toml
PyPInumpy<2pyproject.toml
PyPIpandas>=2.0.3pyproject.toml
PyPIseaborn>=0.12.2pyproject.toml
PyPIlevenshteinpyproject.toml
All dependencies 4

Full resolved dependency set from the GitHub dependency graph: 3 direct and 1 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInumpydirect
PyPIpandasdirect
PyPIseaborndirect
PyPIflit-coreindirect
Dependency advisories 0

Installing pypi:genes2genes@0.2.0 pulls in 73 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.