CNV calling pipeline for low coverage single-cell whole genome sequencing data
By onPipeline for analyzing single-cell WGS data amplified with PicoPLEX, PTA, or droplet MDA includes steps for CNV analysis, filtering, and comparison.
scNAT Data for “scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles”
By onThe publication introduces scNAT, a deep learning method that integrates single-cell RNA and T cell receptor sequencing data for improved analysis of immune cell populations.
Scripts for snRNAseq data analysis
By onScripts for analyzing single nuclei sequencing data from healthy and Parkinson's Disease brains include creating a reference database with transposable element annotations and a file for Cell Ranger to produce snRNA count matrices.
Code for analysis of smell test dataset included in: Development of a Simplified Smell Test to Identify Patients with Typical Parkinson’s as Informed by Multiple Cohorts, Machine Learning and External Validation
By onCode used for the analysis of smell test performance as reported in "Development of a Simplified Smell Test to Identify Patients with Typical Parkinson’s as Informed by Multiple Cohorts, Machine Learning and External Validation", Li et al., 2024
Code for clinical dataset analysis included in: Persistent Hyposmia as Surrogate for α-Synuclein-Linked Brain Pathology
By onCode used for the analysis of clinical data as reported in the study "Persistent Hyposmia as Surrogate for α-Synuclein-Linked Brain Pathology" in Mollenhauer, Li et al., MedRxiv 2023
Nextflow pipeline for Nanopore WGS analysis
By onNextflow pipeline to process whole genome long-read sequencing data generated in the context of ASAP project.
CREsted (Cis-Regulatory Element Sequence Training, Explanation and Design): a deep learning package for training enhancer models on single-cell ATAC sequencing (scATAC-seq) data
By onCREsted offers detailed analyses and tutorials for studying enhancer codes and creating synthetic enhancer sequences at cell type-specific, nucleotide-level resolution.
Directionality_GMX
By onThis script calculates a directionality index to assess the randomness of cell movement
HA_Skeleton_Analysis
By onThis script assesses the complexity of extracellular matrix network through a skeleton analysis
R scripts for the statistical analysis and ploting in the paper GEARBOCS: An Adeno Associated Virus Tool for In Vivo Gene Editing in Astrocytes
By onR scripts for the statistical analysis and plotting in the paper GEARBOCS: An Adeno Associated Virus Tool for In Vivo Gene Editing in Astrocytes
Peripheral Immune Neurodegeneration
By onProject investigates if immune cells impact neurodegenerative diseases differently based on ancestry. It examines disease gene expression and genetic links in African, East Asian, and European populations.
POLCAM-SR: Version 1.4
By onSoftware for processing single-molecule and diffraction limited polarisation camera image data.
RoSE-O_POLCAM
By onRoSE-O adapted for SMOLM using polarization camera. Can also be modified to work with other single-channel imaging systems.
Pipeline and MATLAB scripts for micro-CT-based fiber localization
By onPipeline and MATLAB script for micro-CT-based method for precise fiber localization and atlas alignment after performing micro-fiber photometry in behaving mice.
Preprocessing pipeline to establish and extract data from ROIs from multifiber photometry movies
By onPipeline and code are available for imaging data preprocessing using a micro-fiber array approach to measure and manipulate local dynamics in mice, allowing study of cell-specific signals in 3-D volumes.
R scripts associated with SpatialBrain.org
By onAll original R code written to analyse results shown in SpatialBrain.org and Kilfeather & Koo et al. (2024) "Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease".
Custom G-code used to jet-print microfluid-walled dumbbells
By onAll original G-code used to jet-print the microfluid-walled dumbbells described in Nebuloni, F. et al. (2024) study.
