Research group of Naveed Ishaque, Professor of Cancer Bioinformatics at Charité - Universitätsmedizin Berlin and the BIH Center of Digital Health.

An open, community-driven platform defining and benchmarking the hardest open problems in single-cell analysis.

Single-nucleus transcriptomics of large-cell pancreatic neuroendocrine carcinoma uncovers aberrant brain-type neuronal programs and stress-responsive cell states, revealing therapeutic vulnerabilities...

Benchmarking spatially aware clustering is challenging. SACCELERATOR is an open-source, extensible framework that formalizes expert-guided spatial clustering analysis.

On Sunday 8 March, the world celebrates the achievements and well-being of its 4 billion female inhabitants.

Read the latest article version by Clare Garrard, Katharina F Heil, Maria Cristina Aspromonte, Bérénice Batut, Magda Chegkazi, John M Hancock, Elaine Harrison, Naveed Ishaque, Giselle Ke...

Ovrlpy identifies overlapping cell signals in the vertical dimension of spatial transcriptomics data.

Segmentation-Free Analyses Sainsc: a new tool for efficient whole organism spatial transcriptomics data analysis at the nanometre scale. Shown is a blended composite spatial map of total gene express...

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcript...

The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. Current c...

This study presents a comprehensive evaluation of Xenium In Situ datasets and provides recommendations on analysis workflows.

Imaging-based spatially resolved transcriptomics can localise transcripts within cells in 3D. Cell segmentation precedes assignment of transcripts to cells and annotation of cell function. However, ce...

Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Le...

The BIH PhD program aims to promote interdisciplinarity and support young translational talents. The program co-funds PhD positions for excellent translational projects that fit one of the four BIH re...

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Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Le...

Spatially resolved transcriptomics methods can now profile the entire transcriptome, at full organism scale with nanometre resolution. Analysis frameworks that can efficiently and intuitively process....