GDIS-Bio v1.1.0 — Expanded Reproducibility and Robustness Release

GDIS-Bio v1.1.0 — Expanded Reproducibility and Robustness Release

This release expands the reproducibility workflow accompanying:

“GDIS-Bio: A Generalized Dynamical Instability Framework for Localizing Transcriptional State Transitions in Single-Cell Trajectories”

Authors: Hamid Ismail, Ahmed Harb, Basem William, and Marwan Bikdash

GDIS-Bio v1.1.0 extends the original reproducibility release with additional reviewer-driven robustness analyses, alternative pseudotime and graph-geodesic ordering assessments, component-ablation analyses, improved pipeline validation, and expanded documentation.

Major additions

• Controlled GDIS component-ablation analysis.
• Pseudotime-order perturbation and robustness analyses.
• Alternative pseudotime reconstruction using diffusion pseudotime.
• Graph-geodesic reconstruction of developmental ordering.
• Cohort-wide geodesic reconstruction qualification.
• Alternative-ordering GDIS comparison and paired statistics.
• Additional supplementary figure and table generation.
• Dependence-aware coordinated-shift sensitivity analysis.
• Non-circular landmark null analysis.
• Pancreatic annotated-state IQR localization analysis.
• Fixed-cell-window pseudotime-span audit.
• Repository-level P25 validation utilities.
• Expanded reproducibility, provenance, reviewer-analysis, and release documentation.
• Improved README with installation, computational requirements, workflow organization, interpretation guidance, and citation information.

Datasets

The workflow analyzes two public single-cell RNA-seq datasets:

• GSE114412 — human pancreatic endocrine differentiation.
• GSE175634 — human iPSC-derived cardiac differentiation.

Reproducibility

The repository includes:

• sequential scientific analysis scripts;
• environment specifications;
• frozen analysis parameters;
• Linux and Windows execution helpers;
• P25 smoke and integrity validation;
• selected manuscript assets;
• documentation linking analyses to figures, tables, and reviewer-response stages.

Large raw and intermediate datasets are not bundled with the source release. They can be downloaded or regenerated using the supplied workflow.

Validation

The updated repository has been checked for:

• presence of all 44 primary analysis-stage scripts;
• Python syntax compilation;
• required P25 dependencies;
• repository cleanliness;
• P25 source-level smoke validation;
• compatibility with source-only GitHub checkout behavior.

Repository

https://github.com/hamiddi/GDIS-Bio

Version: v1.1.0
License: MIT

GDIS-Bio v1.0.0 – Manuscript Reproducibility Release

This is the first reproducibility release of GDIS-Bio, associated with the manuscript:

“GDIS-Bio: A Generalized Dynamical Instability Framework for Localizing Transcriptional State Transitions in Single-Cell Trajectories”

Authors: Hamid Ismail, Ahmed Harb, Basem William, and Marwan Bikdash

This release contains the complete computational workflow used to reproduce the analyses reported in the study, including:

public-data acquisition from NCBI GEO;
dataset qualification and cohort definition;
single-cell preprocessing and transcriptional state-space construction;
pseudotemporal trajectory validation and sliding-window generation;
GDIS calculation using pyGDIS v1.0.0;
state-space dimensionality and window-size sensitivity analyses;
conventional transition-associated metric benchmarking;
structure-preserving circular-shift null analyses;
external validation in an independent cardiac differentiation dataset;
paired statistical comparisons; and
generation of manuscript figures, supplementary figures, and summary tables.

The workflow analyzes two public single-cell RNA-seq datasets:

GSE114412 — human pancreatic endocrine differentiation;
GSE175634 — human iPSC-derived cardiac differentiation.

The repository includes the sequential analysis scripts, environment specifications, frozen analysis parameters, Linux and Windows execution scripts, selected reference outputs, manuscript assets, and documentation linking the reported results to the corresponding computational stages.

Large raw and intermediate data files are not included in the release. They can be downloaded or regenerated using the supplied scripts.

Repository: https://github.com/hamiddi/GDIS-Bio

Version: v1.0.0

License: MIT