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Technology-invariant pipeline for spatial omics analysis that scales to millions of cells (Xenium / Visium HD / MERSCOPE / CosMx / PhenoCycler / MACSima / etc)
Integrating GWAS and spatial transcriptomics for spatially resolved mapping of cells associated with human complex traits.
Covarying neighborhood analysis (CNA) is a method for finding structure in- and conducting association analysis with multi-sample single-cell datasets.
R implementation of the Reshef and Rumker CNA method (https://github.com/immunogenomics/cna)
Count ratio uncertainty modeling base linear regression
Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices
fastFMM: Fast Functional Mixed Models using Fast Univariate Inference
Identification of biased features from SVGs in spatial transcriptomics data
Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics (cell2location model)
Preferential Subspace Identification Algorithm
Spatial-eXpression-R: Cell type identification (including cell type mixtures) and cell type-specific differential expression for spatial transcriptomics
Significance analysis for clustering single-cell RNA-sequencing data
Collaboration between Costa, Martinowich, and Hicks labs investigating basolateral amygdala cell-types across huamn and non-human primate species.
Spatially-aware quality control for spatial transcriptomics
A curated list of Quarto talks, tools, examples & articles! Contributions welcome!
NEural MOdelS, a statistical modeling framework for neuroscience.
R Package: Regularized Principal Component Analysis for Spatial Data
Neyman-Scott point process model to identify sequential firing patterns in high-dimensional spike trains
Methods to discover gene programs on single-cell data
Analyze neuroscience data in the cloud