DANCE: a deep learning library and benchmark platform for single-cell analysis

ESICCC: A systematic computational framework for evaluation, selection and integration of cell-cell communication inference methods

Uses clustering methods, tree edit distance, maximum likelihood based tree inference techniques to construct trees of tumor progression history from single cell RNA sequencing datasets

Retinal ganglion cells regeneration in 2 days post optic nerve crush retina | Lydia Tai | Dong Feng Chen Lab collaboration | Schepens Eye Research Institute, Mass General Hospital, Harvard Medical School

This toolkit provides Python code for preprocessing, quality control, clustering, and visualization of single-cell RNA sequencing data using Scanpy. Ideal for deep insights into cell populations and gene expression.

Snakemake workflow for a basic single-cell RNA-Seq analysis.

Repository of the master project in Bioinformatics at Lund University

This repository enables the reproducible analysis of an important developmental neuroscience dataset on hypothalamus neuron differentiation. It performs quality control, clustering, trajectory analysis, and identification of marker genes to characterize the differentiation trajectories of glutamatergic and GABAergic Onecut3+ neuronal subtypes.

This project explores single-cell RNA sequencing (scRNA-seq) data using advanced machine learning techniques. By applying dimensionality reduction and graph-based models, we analyze high-dimensional scRNA-seq data to uncover cellular heterogeneity and reveal underlying biological processes at the single-cell level.

Collection of scripts generated in the analysis of sex and gender difference in the human brain transcriptome at single-cell level