A Lightweight & Versatile Visualization Tool for Spatial-Omics Data
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Updated
Dec 5, 2023 - TypeScript
A Lightweight & Versatile Visualization Tool for Spatial-Omics Data
R Shiny app for Vitessce, an integrative and interactive data visualization tool for exploration of spatial single-cell experiments.
A R Shiny App for single-cell analysis
Repository for projects in Single Cell
Custom codes for Flysta3D paper.
RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours.
Multiplexed droplet scRNA-seq Kang et al Nat Biotechnol 2018
(NOTE: This is a development fork, please refer to official repo at statgen: https://github.com/statgen/popscle) Freemuxlet methods and auxilary tools
Papers and Tools that would guide your journey in Computational Biochemistry
Computational Biologist and Machine Learning Enthusiast
QuantQC is a package for quality control (QC) of single-cell proteomics data. It is optimized to work with nPOP, a method for massively parallel sample preparation on glass slides.
As part of the IEE Healthcare Summit Data Hackathon(2022) Challenge designed a classification model to predict severity of COVID-19 infection from scRNA-seq data.
Cell cluster Analysis with Variational Autoencoder using Conditional Hierarchy Of latent representioN
Analysis code for the clue framework paper: efficient pooled single-cell sequencing without reference genotypes.
Single-Cell Data Science
Documentation analysis and manuscript figures of QuRIE-seq methods paper.
Code for Jonsson*, Zhang*, et al, TteK (Granzyme K+ CD8 T cells), Science Translational Medicine, 2022: the core population of inflamed human tissue-associated CD8 T cells.
Pytorch implementation of "Multi-domain translation between single-cell imaging and sequencing data using autoencoders" (https://www.nature.com/articles/s41467-020-20249-2) with custom models.
Factorial latent dynamic models trained on Markovian simulations of biological processes using single cell RNA sequencing data.
Jekyll page for Wenzel Lab webpage. View the page at https://wenzel-lab.github.io
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