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Releases: Svvord/FastCCC

FastCCC v1.0.1

23 May 03:48

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This is the official release of FastCCC with full-featured functionality.

This release fixes several potential bugs, adds coding-agent skills for natural-language FastCCC workflows, and improves automated reports with cell-type-specific evidence explorers, clearer condition-comparison language, figure-generation audit records, and enhanced reference reports.

Reference reports can be generated from a selected healthy tissue panel (reference_tissue) or from a user-built custom control panel (reference_path) with fastccc.report.generate_reference_report.

v0.1.4 — Automated Report Generation

09 May 05:26

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New Features

Automated HTML report (fastccc.report.generate_report)

After running FastCCC, a single function call now produces a fully self-contained
interactive HTML report. No server required — open the output file directly in any
browser.

  • 24 publication-quality figures organized into six thematic sections: Global CCC
    Overview, Ligand–Receptor Pair Analysis, Pathway Enrichment, Cell-Type Profiles,
    Network Analyses, and Advanced L-R Analyses
  • Multi-condition support: provide results for two groups (e.g. treated vs. control)
    to enable per-condition tabs and an automatic differential comparison tab
    (differential heatmap, volcano plot, pathway activity bar chart)
  • Run parameters displayed in the Executive Summary of each tab for full
    reproducibility

Bug Fixes

  • Added fastccc.version attribute
  • Fixed SyntaxWarning for invalid escape sequence in build_reference.py

New Dependencies

matplotlib, seaborn, jinja2, networkx, adjusttext

Documentation

  • New tutorial: Automated Report
  • New examples: examples

v0.1.3

25 Mar 13:19

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add AnnData input support

version 0.1.2

15 Feb 20:54

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This release expands the supported NumPy version range for FastCCC to improve compatibility.

Initial release for publication

11 Oct 18:13

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This release corresponds to the version of the code used in the paper: "FastCCC: A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies"