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Osmanbeyoglu Lab

Resources

Algorithms

SPaRTAN: Computational framework for linking cell-surface receptors to transcriptional regulators by exploiting CITE-seq datasets with cis-regulatory information

Link

Reference

Tutorial

CITRUS: Interpretable neural network to model the impact of somatic alterations on transcription factors and downstream transcriptional programs

Link

Reference

PSIONIC: A multitask framework that jointly models transcriptional networks for several related cancer types by leveraging chromatin accessibility and gene expression data

Link

Reference

Datasets

Mesothelioma: Human malignant mesothelioma CITE-seq profiling

Raw data

Reference

Web resources

COVID-19db of Immune Cell States: A comprehensive web resource linking surface proteins to transcription factors for host immune cell types to assist in the discovery of COVID-19 potential therapeutic targets and diagnostic biomarkers.

Web link

GitHub link

Reference

Mesothelioma Spatial Atlas: A web resource providing interactive visualizations of malignant mesothelioma multiplex immunofluorescence (mIF) dataset and eosin (H&E) images of tumor samples to facilitate the understanding of pathogenesis and identify novel therapies.

Web link

GitHub link

Reference

Spatial Landscape of Glioblastoma: A web resource providing intuitive access to spatial gene expression, metaprograms, TF activity, pathway activity, drug2cell scores and correlation with ligand–receptor expression for researchers to interactively explore the spatial regulation of GBM and supports hypothesis generation for both basic and translational cancer research.

Web link

GitHub link

Reference

Tutorial

Computational Approaches for Identifying Context-Specific Transcription Factors using Single-Cell Multi-Omics Datasets

ECCB 2024

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