Naveed Ishaque

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

The paper identifies two neuroendocrine cell states in large-cell pancreatic neuroendocrine carcinoma using single-nucleus transcriptomics, revealing an aberrant brain-type neuronal program and a stress-responsive state.

The paper extends SpatialLeiden to handle atlas-scale, 3D, and multimodal spatial omics data, improving modularity and scalability across multiple samples.

This paper introduces a tool to detect overlapping cells and segmentation errors in 3D spatial transcriptomics data.

NetworkVI integrates multi-omics data using a biologically informed variational autoencoder, enabling interpretable cell phenotyping and discovery of lineage-specific immune states.

This study evaluates the Xenium In Situ spatial transcriptomics platform using mouse brain and human breast cancer datasets, comparing it to other technologies and benchmarking analysis tools.

SpatialLeiden integrates spatial embeddings into Leiden clustering to create a spatially aware method that performs well compared to existing spatial clustering techniques.

Sainsc provides a segmentation-free tool for analyzing high-resolution spatial transcriptomics data, enabling accurate subcellular cell-type mapping with lower computational demands.

The paper identifies a disrupted trophoblast transition in preeclampsia, linking placental pathology to maternal syndrome through multi-omics profiling and in vitro analyses.

SSAM-lite provides a lightweight, web-based tool for rapid, segmentation-free analysis of spatially resolved transcriptomics data, enabling broad accessibility without requiring computational expertise.

This paper introduces SSAM, a cell segmentation-free method for cell-type identification and tissue domain detection in in situ transcriptomics data, showing it detects known and discovers new cell types in mouse brain tissues.

The paper uses single nucleus and in situ RNA sequencing to create a comprehensive atlas of human pancreas cells, revealing cell topographies and differences between adult and neonatal pancreata.

The paper maps DNA methylation changes during differentiation of HL-60/S4 cells, showing enhancer and CTCF site methylation drives transcriptional reprogramming.