🧠 Theta and Gamma Oscillation Analysis

Neural Oscillation Networks in Spatial Context created by Landon Wellendorf

Integrated single-nucleus RNA-seq and spatial transcriptomics to map cellular and molecular organization of theta and gamma oscillation networks in hippocampal and cortical circuits of mice during spatial object recognition. This analysis identifies distinct neuronal subtypes driving oscillatory patterns and their spatial distribution.

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View Complete Analysis Report

View the report summarizing methodology, results, and biological interpretation

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1
Single-Nucleus RNA-seq Analysis
Single-nucleus RNA-seq analysis identifying neuronal subtypes related to theta and gamma oscillations. Includes neuronal subtype clustering, differential expression analysis, and oscillation marker identification.
R/Seurat Wilcoxon Tests UMAP Cell Clustering
2
Spatial Transcriptomics Analysis
Spatial transcriptomics analysis mapping theta and gamma oscillation markers across tissue architecture. Visualizes spatial distribution of neural oscillation patterns in brain tissue sections.
Python/scanpy Visium Spatial Clustering Marker Mapping
3
Data Integration & Deconvolution
Integration of snRNA-seq and Visium data to deconvolve spatial cell types linked to oscillations. Uses RCTD deconvolution to estimate cell type proportions and validate oscillation-cell type relationships.
R/Seurat RCTD Deconvolution Spearman Correlation

📊 Data Source: GSE223066 - Gene Expression Omnibus

💻 Source Code: View on GitHub