v3.0.1 - Enable module usage using `github()` directive

• to enable module usage using github() directive
  • source utils.R via paramsinstead ofsnakemake@source`
  • comment global.yaml (now requires full snakemake installation, not minimal)
• add nodefaults to all env YAML

Full Changelog: v3.0.0...v3.0.1

v3.0.0 - Configurable HVF selection

Breaking change: Highly variable feature (HVF) selection is now customizable

Added two helper scripts to increase compatibility

• Converting Parse Bioscience scRNA-seq data to (expected) 10x Genomics/MTX format
• Converting Seurat object to scanpy compatible anndata object in h5ad format

The documentation was updated accordingly.

Bug fixes and other performance improvements are not mentioned.

Full Changelog: v2.0.0...v3.0.0

v2.0.0 - Snakemake 8 compatible

Breaking change: Requires Snakemake >= v8.20.1

Full Changelog: v1.0.1...v2.0.0

v1.0.1 - stable version with complete docs and DOI

Release Notes v1.0.1

Documentation

• Link to Zenodo and document DOI.

Full Changelog: v1.0.0...v1.0.1

Release Notes v1.0.0

Features

• Preparation
  • Support for loading (multimodal) data from 10X Genomics Kits or MTX file format.
  • Optional addition of provided metadata to the Seurat object.
  • Assignment of Guide RNA and KO target genes for CRISPR Guide Capture data.
• Merge & Split
  • Merging of all samples into one large Seurat object, including metadata.
  • Optional addition of externally provided metadata.
  • Splitting of merged data into subsets using metadata.
• Filtering
  • Filtering of cells by a combination of logical expressions using metadata.
• Pseudobulking
  • Pseudobulking based on user-specified metadata columns with options for aggregation methods.
  • Application of a cell count threshold to remove pseudobulked samples with fewer cells than specified.
• Normalization
  • Normalization of gene expression data using SCTransform v2.
  • Normalization of all multimodal data using Centered Log-Ratio (CLR).
  • Dynamic determination of highly variable genes (HVGs).
• Cell Scoring
  • Cell cycle scoring using Seurat::CellCycleScoring.
  • Gene module scoring using Seurat::AddModuleScore.
• Correction
  • Normalization and correction for provided confounders using SCTransform v2.
• Visualization
  • Support for Ridge-, Violin-, Dot-plots, and Heatmaps for various data modalities.
  • Metadata visualization after each processing step using inspectdf.
• Save Counts
  • Functionality to save all counts as CSV after each processing step for all modalities.
• Results Organization
  • Structured saving of results in the configured result path for easy access and analysis.

Documentation

• Template for the Methods section of a scientific publication.
• Detailed description of all features and tips for usage.
• Configuration specifications are provided in the ./config/README.md and within config/config.yaml.
• Example usage with a public scRNA-seq dataset consisting of 15 CRC samples, including instructions for data download.
• Resources section with links to data resources for scRNA-seq data and recommended MR.PARETO modules for downstream analyses.

Full Changelog: https://github.com/epigen/scrnaseq_processing_seurat/commits/v1.0.0

v1.0.0 - stable version with complete docs

Release Notes

Features

• Preparation
  • Support for loading (multimodal) data from 10X Genomics Kits or MTX file format.
  • Optional addition of provided metadata to the Seurat object.
  • Assignment of Guide RNA and KO target genes for CRISPR Guide Capture data.
• Merge & Split
  • Merging of all samples into one large Seurat object, including metadata.
  • Optional addition of externally provided metadata.
  • Splitting of merged data into subsets using metadata.
• Filtering
  • Filtering of cells by a combination of logical expressions using metadata.
• Pseudobulking
  • Pseudobulking based on user-specified metadata columns with options for aggregation methods.
  • Application of a cell count threshold to remove pseudobulked samples with fewer cells than specified.
• Normalization
  • Normalization of gene expression data using SCTransform v2.
  • Normalization of all multimodal data using Centered Log-Ratio (CLR).
  • Dynamic determination of highly variable genes (HVGs).
• Cell Scoring
  • Cell cycle scoring using Seurat::CellCycleScoring.
  • Gene module scoring using Seurat::AddModuleScore.
• Correction
  • Normalization and correction for provided confounders using SCTransform v2.
• Visualization
  • Support for Ridge-, Violin-, Dot-plots, and Heatmaps for various data modalities.
  • Metadata visualization after each processing step using inspectdf.
• Save Counts
  • Functionality to save all counts as CSV after each processing step for all modalities.
• Results Organization
  • Structured saving of results in the configured result path for easy access and analysis.

Documentation

• Template for the Methods section of a scientific publication.
• Detailed description of all features and tips for usage.
• Configuration specifications are provided in the ./config/README.md and within config/config.yaml.
• Example usage with a public scRNA-seq dataset consisting of 15 CRC samples, including instructions for data download.
• Resources section with links to data resources for scRNA-seq data and recommended MR.PARETO modules for downstream analyses.

Full Changelog: https://github.com/epigen/scrnaseq_processing_seurat/commits/v1.0.0