v0.1.2-feature-branch

Release Log: UNRAVEL v0.1.2-feature-branch (Feature Branch Update)

Release Date: August 2024

Overview:

This release is a major update to the UNRAVEL v0.1.0 codebase, incorporating both significant improvements to the original version (v0.1.0) and early drafts of code that were foundational for the v1.0.0-beta release on the main branch. The code in this branch represents a bridge between the initial version described in Rijsketic et al., 2023 and the ongoing development efforts aimed at achieving the full functionality envisioned for v1.0.0-beta. This update focuses on enhancing the speed and accuracy of the registration pipeline, expanding the functionality of statistical analysis, and adding new tools for image processing, cluster validation, and data visualization. Additionally, this release includes numerous bug fixes and optimizations designed to streamline the workflow for voxel-wise analysis of c-Fos immunofluorescence and related tasks.

Major Updates:

• Enhanced Registration Pipeline:

  • Streamlined registration scripts (czi_to_tif3.sh, prep_reg.sh, brain_mask.sh) for faster processing and improved accuracy.
  • Added support for parallel processing and optimized memory usage in image loading and saving functions.
  • Integration of ANTsPy tools for affine initialization and non-linear transformations, enabling more robust alignment to the LSFM atlas.

• Statistical Analysis Enhancements:

  • Introduction of prep_vxw_stats.py for voxel-wise statistical analysis with improved resampling and reorientation functions.
  • New scripts for performing 2×2 ANOVA and other statistical tests with enhanced false discovery rate (FDR) correction methods.
  • Added functionality for calculating effect sizes, performing Tukey's post hoc tests, and generating summary statistics across multiple q-value thresholds.

• Cluster Validation Tools:

  • A comprehensive suite of scripts (validate_clusters.py, validate_clusters_summary_pooled_ttest.py, valid_clusters_2x2_ANOVA_draft.py) for validating voxel-wise clusters using post hoc comparisons, cell density quantification, and statistical summaries.
  • Tools for handling bilateral data, aggregating cluster validation results, and generating publication-ready tables and visualizations.

• Image Processing and Data Handling:

  • New utilities for converting image formats (czi_to_nii.py, h5_to_tifs.py, tif_to_tifs.py) and handling large datasets more efficiently.
  • Updated unravel_utils.py with functions for handling metadata, managing output directories, and processing batch data.
  • Advanced tools for manipulating image volumes, including z-scoring, background subtraction, masking, and warping to atlas space.

• Improved User Experience:

  • Refined help guides and improved command-line interface (CLI) with custom argparse formatting for better readability.
  • Enhanced progress tracking with rich library integration and more detailed error handling.

Bug Fixes:

• Resolved issues related to image orientation, file path handling, and parallel processing in various scripts.
• Fixed inconsistencies in metadata extraction and file naming conventions to ensure compatibility with downstream analysis.
• Addressed bugs in statistical scripts related to argument parsing, file aggregation, and result formatting.

Known Issues:

• Ongoing testing is required for certain new features, particularly in the native_clusters2.py and related validation scripts.
• Some scripts remain in draft form and may require further refinement before use in production environments (please see v1.0.0-beta releases on the main branch for our latest code).

Other changes

• Added initial installation guide (#1)
• Bug fixes, added ABAseg_3dc.sh for single rater regional counts (#2)
• Upgraded miracl_conv_convertTIFFtoNII.py to python3 (#3)
• Updated README.md

This release marks a significant milestone in the development of UNRAVEL, incorporating numerous enhancements that improve both the functionality and user experience. The code in this branch has been utilized in the preparation of several scientific manuscripts, underscoring its robustness and applicability to real-world neuroimaging analysis.

Full Changelog: v0.1.1...v0.1.2-feature