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Author

Muhtasim Noor Alif

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Open access Oct 2026

ST-DISTAL: Dual-Branch Graph Convolution with Distributional Alignment for Cell-Type Deconvolution

Spatial transcriptomics enables high-throughput gene expression profiling while preserving spatial information, offering valuable insights into tissue architecture and cellular organization. Methods that achieve single-cell or subcellular spatial resolution typically rely on predefined gene panels, which limits genome-...

A. Abir, Muhtasim Noor Alif, Wei Zhang · 0 citations
Review Sep 2026

Benchmarking reference-based cellular deconvolution algorithms to predict cell proportions.

Computational cellular deconvolution enables researchers to estimate the proportions of distinct cell types in bulk RNA-sequencing (RNA-seq) samples using single-cell RNA-seq (scRNA-seq) as a reference. This offers a scalable and cost-effective alternative to physical cell separation. Despite recent advancements in cel...

Ayesha A. Malik, Muhtasim Noor Alif, Ayla Bratton et al. · 0 citations
Open access Aug 2026

SPIDER: spatially integrated denoising via embedding regularization with single cell supervision

SPIDER is introduced, a semi-supervised framework that leverages independently generated, annotated single-cell RNA-seq (scRNA-seq) references to guide ST denoising, providing a generalizable solution for robust ST data denoising.

Md. Istiaq Ansari, Muhtasim Noor Alif, Wei Zhang · 2 citations

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