Dani Livne’s research while affiliated with Bar Ilan University and other places

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Publications (4)


Gaussian scaling activity of two variables
PathSingle pipeline
Clustering UMAP results on PBMC dataset
A UMAP representation of the pathways dimension reduction of single-cell expression data reduced to a map in which the different T cell states are of distinct spatial behavior
Using only 3 features (pathways) to represent the data. The original 357 dimensions of the single-cell data was reduced using PathSingle metrics, to a map in which the different T cell states are of distinct spatial behavior. The image provides a stratified representation of T cells states

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Pathway metrics accurately stratify T cells to their cells states
  • Article
  • Full-text available

December 2024

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5 Reads

BioData Mining

Dani Livne

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Pathway analysis is a powerful approach for elucidating insights from gene expression data and associating such changes with cellular phenotypes. The overarching objective of pathway research is to identify critical molecular drivers within a cellular context and uncover novel signaling networks from groups of relevant biomolecules. In this work, we present PathSingle, a Python-based pathway analysis tool tailored for single-cell data analysis. PathSingle employs a unique graph-based algorithm to enable the classification of diverse cellular states, such as T cell subtypes. Designed to be open-source, extensible, and computationally efficient, PathSingle is available at https://github.com/zurkin1/PathSingle under the MIT license. This tool provides researchers with a versatile framework for uncovering biologically meaningful insights from high-dimensional single-cell transcriptomics data, facilitating a deeper understanding of cellular regulation and function. Supplementary Information The online version contains supplementary material available at 10.1186/s13040-024-00416-7.

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Author Correction: Community assessment of methods to deconvolve cellular composition from bulk gene expression

November 2024

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60 Reads

Brian S. White

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[...]

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Andrew J. Gentles

YADA - Reference Free Deconvolution of RNA Sequencing Data

June 2024

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4 Reads

Current Bioinformatics

Introduction We present YADA, a cellular content deconvolution algorithm for estimating cell type proportions in heterogeneous cell mixtures based on gene expression data. YADA utilizes curated gene signatures of cell type-specific marker genes, either obtained intrinsically from pure cell type expression matrices or provided by the user. Method YADA implements an accessible and extensible deconvolution framework uniquely capable of handling marker genes alone as inputs. Adoption barriers are lowered significantly by relying solely on literature-supported cell type-specific signatures rather than full transcriptomic profiles from purified isolates. However, flexible inputs do not necessitate sacrificing rigor - predictions match metrics of current methodologies through an integrated optimization scheme balancing multiple inference algorithms. Efficiency optimizations via compiled runtimes enable rapid execution. Packaging as an importable Python toolkit promotes community enhancement while retaining codebase extensibility. Result Validation studies demonstrate that YADA matches or exceeds the performance of current deconvolution methods on benchmark datasets. To demonstrate the utility and enable immediate usage, we provide an online Jupyter Notebook implementation coupled with tutorials. Conclusion YADA provides an accurate, efficient, and extensible Python-based toolkit for cellular deconvolution analysis of heterogeneous gene expression data.


PathWeigh – Quantifying the Behavior of Biochemical Pathway Cascades

June 2022

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2 Reads

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1 Citation

Lecture Notes in Computer Science

Biochemical pathways analysis is an effective tool for understanding changes in gene expression data and associating such changes with cellular phenotypes. Pathway research aims to identify associated proteins within a cell using pathways and at building new pathways from a group of molecules of interest. Using pathway-based methods we gain insight into different functions of relevant molecules and find direct and indirect relations between them. We present PathWeigh, a Python-based tool for pathway analysis and graph presentation. The tool is open-sourced, extendable and runtime efficient.PathWeigh is available at https://github.org/zurkin1/Pathweigh and is released under MIT license. A sample Python notebook is provided with examples of running the tool.KeywordsPathway analysisRNA sequencingMachine learning

Citations (1)


... This approach enabled assessment of activation strength for each pathway interaction group and examination of co-behavior among interacting molecules. Building on these foundations, PathWeigh [19] introduced probabilistic activation levels and expanded platform support to include both microarray and RNA-seq data. ...

Reference:

Pathway metrics accurately stratify T cells to their cells states
PathWeigh – Quantifying the Behavior of Biochemical Pathway Cascades
  • Citing Chapter
  • June 2022

Lecture Notes in Computer Science