Tallulah S. Andrews

This study identifies and mitigates protocol-dependent gene expression bias between 3' and 5' scRNA-seq by focusing on a small set of biased genes rather than applying broad normalization.

The paper integrates multiple datasets to create a comprehensive human liver cell atlas, identifying new cell types and associations with demographic factors.

This study shows that NOD2 activation with MDP during ZIKV infection shifts monocytes to an anti-inflammatory state, preserving synapses and cognitive function in the hippocampus.

The paper identifies a human zona incerta subregion with characteristics of the rodent A13 nucleus using MRI and histology.

The paper presents a protocol for analyzing spatial transcriptomics data in liver tissues to study metabolic reprogramming and cellular heterogeneity in MASLD mouse models.

CycleMix improves cell-cycle state classification by allowing flexible assignment of cells into any number of states and identifying non-cycling cells.

The paper shows that cholinergic synaptic plasticity, specifically VAChT responses to tau, correlates with cognitive resilience in presymptomatic AD.

This study creates a single-nucleus and spatial transcriptomic atlas of the COVID-19 liver, revealing cellular and functional disruptions associated with organ damage.

sciRED improves the interpretation of scRNA-seq factor analysis by removing confounding effects and mapping factors to known covariates, revealing biological signals in multiple datasets.

The paper investigates molecular subtypes in PSP by analyzing tau seeding activity and identifies immune-related pathways associated with disease progression.

This study creates a single-cell RNA sequencing atlas of the early intra-mammalian stage of Schistosoma mansoni, identifying gene expression profiles and validating cell markers.

The paper creates a comprehensive single-cell transcriptome atlas of the Plasmodium berghei life cycle and applies it to define stages in human malaria parasites.

The paper introduces a statistical method to distinguish real cells from empty droplets in single-cell RNA sequencing data, improving detection of low-UMI cell libraries while controlling false discovery rates.

This paper introduces dropout-based feature selection methods for scRNASeq, showing they outperform variance-based methods in multiple applications.