Shila Ghazanfar

ClustSIGNAL improves spatial transcriptomics clustering by adapting smoothing based on neighbourhood complexity, enabling scalable analysis of large datasets.

This paper introduces wSIR, a scalable supervised dimensionality reduction method for spatial transcriptomics data, which retains predictive power of spatial coordinates and is demonstrated on breast cancer and embryonic development datasets.

The paper introduces CCIDeconv, a method to deconvolve whole-cell cell-cell interactions into subcellular compartments using scRNA-seq data and spatial transcriptomics datasets.

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

This paper benchmarks single-cell methods by analyzing 282 papers, highlighting challenges in combining benchmarking studies and preventing fatigue.

The paper introduces spatialMNN, an algorithm that integrates spatial transcriptomics data to identify spatial domains by incorporating 2D spatial information into mutual nearest neighbors analysis.

HuBMAPR provides an R client for accessing HuBMAP data, enabling efficient programmatic discovery and retrieval of spatial-resolution biological data.

This study creates a spatiotemporal atlas of mouse gastrulation and early organogenesis using spatial transcriptomics and scRNA-seq, revealing gene expression dynamics and mesodermal fate decisions.

This study benchmarks single-cell multimodal omics integration methods across multiple tasks to guide method selection based on data characteristics and study goals.

WebAtlas integrates single cell and spatial transcriptomic data into a cloud-optimised format for interactive exploration.

This study benchmarks six methods for predicting spatial gene expression from histology images using breast and skin cancer datasets and TCGA data, evaluating their performance and translational potential.

BIDCell improves subcellular spatial transcriptomics segmentation by combining self-supervised learning with biologically informed loss functions, outperforming existing methods across multiple tissue types.

The paper introduces MoleculeExperiment, an R/Bioconductor package for handling molecule-resolved spatial transcriptomics data, enabling standardization and integration with existing single-cell tools.

This study benchmarks analytical combinations for predicting COVID-19 outcomes using scRNA-seq data, finding that ensemble learning and multi-view features improve performance.

SpatialExperiment provides a data infrastructure for spatially resolved transcriptomics data in R/Bioconductor, with examples from Visium and seqFISH platforms.

SageNet predicts spatial positions and cell-cell proximities of dissociated single-cell data using a graph neural network trained on spatial references.

StabMap integrates mosaic single cell data by using non-overlapping features to stabilize mapping onto a common embedding.

geneBasis selects an optimal gene panel from scRNA-seq data by iteratively adding genes that maximize manifold distance, outperforming existing methods in resolving cell types and states.

This paper uses spatial transcriptomics to study mouse organogenesis, revealing spatial gene expression patterns and cell fate decisions not captured by single-cell RNA sequencing.

The paper identifies coordinated step-changes in gene expression kinetics during erythropoiesis in both mouse and human, challenging assumptions of RNA velocity analysis.

The paper identifies diet-responsive genes in Drosophila by analyzing genetic variation across diets and confirms their role in nutrient storage and utilization.

The paper analyzes clonal evolution in liver cancer at single-cell and single-variant resolution, revealing genetic and phenotypic heterogeneity and their evolutionary relationships.

The paper introduces scHOT, a method to detect changes in higher order gene interactions in single-cell data, demonstrating its application in liver development and spatial gene correlation patterns.

This study evaluates stably expressed genes (SEGs) in single cells using a computational framework and finds they are more stable than traditional housekeeping genes across species.

scMerge integrates multiple scRNA-Seq datasets by removing unwanted variation through stably expressed genes and pseudo-replicates, enhancing biological discovery.

DCARS identifies gene pairs with significant changes in correlation across survival stages, outperforming existing methods in cancer datasets.