Rhalena A. Thomas

Single nuclear RNA sequencing shows altered microglial and astrocytic functions in post-mortem Parkinson's disease tissue

This study uses snRNA-seq data from post-mortem Parkinson's disease tissue to identify altered molecular pathways in microglia and astrocytes, showing upregulated protein misfolding responses and downregulated inflammation-related genes.

Machine learning and burden analyses highlight novel candidate genes in Parkinson's disease

The paper integrates machine learning and rare variant burden analysis to identify novel candidate genes for Parkinson's disease.

Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling

Ensemblex improves accuracy in genetic demultiplexing of pooled scRNA-seq samples by combining four algorithms with accuracy weighting.

Midbrain organoids with an SNCA gene triplication display dopamine-dependent alterations in network activity

This study shows that midbrain organoids with SNCA gene triplication exhibit PD-like neural activity changes, which can be reversed by dopamine agonists.

Interpretable machine learning classifiers implicate GPC6 in Parkinson's disease from single-nuclei midbrain transcriptomes

The paper uses machine learning on single-nuclei RNA sequencing data to identify GPC6 as a genetic risk factor for Parkinson's disease.

ScRNAbox: Empowering Single-Cell RNA Sequencing on High Performance Computing Systems

scRNAbox provides an HPC-friendly scRNA-seq analysis pipeline with quality control, integration, clustering, annotation, and differential expression analysis.

A deep learning convolutional neural network distinguishes neuronal models of Parkinson's disease from matched controls

This study uses a convolutional neural network to classify Parkinson's disease models from control cells with high accuracy using microscopy images.

Identification of novel variants, genes and pathways potentially linked to Parkinson's disease using machine learning

The paper uses machine learning to identify candidate genes, variants, and pathways potentially linked to Parkinson's disease from GWAS loci and genomic data.

CelltypeR: A flow cytometry pipeline to annotate, characterize and isolate single cells from brain organoids

The paper develops a flow cytometry pipeline called CelltypeR to annotate and characterize cell types in brain organoids, identifying dopamine neuron subtypes and tracking cell type dynamics over time.

Auto-qPCR: A Python based web app for automated and reproducible analysis of qPCR data

Auto-qPCR automates qPCR data analysis, reducing errors and time by providing a web-based tool for DNA quantification and gene expression analysis.

Midbrain organoids with an SNCA gene triplication model key features of synucleinopathy

This study uses midbrain organoids from SNCA triplication patients to model synucleinopathy features, showing increased alpha-synuclein expression and aggregation linked to dopaminergic neuron loss.

A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems

The paper identifies a molecular gradient along the human hippocampus's longitudinal axis that correlates with distinct brain systems and behavioral functions.