iCellR is an interactive R package designed to facilitate the analysis and visualization of high-throughput single-cell sequencing data. It supports a variety of single-cell technologies, including scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq, and Spatial Transcriptomics (ST).

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

Finding surprising needles (=genes) in haystacks (=single cell transcriptome data).

A deep learning architecture for robust inference and accurate prediction of cellular dynamics

a scalable python suite for tree inference and advanced pseudotime analysis from scRNAseq data.

Implementation of MaSigPro for scRNA-Seq Data

Pseudotime analysis for time-series single-cell sequencing and imaging data

Characterize gene dynamics over trajectories using GLMs, GEEs, & GLMMs.

A composite regression neural network for latent timing prediction of single-cell RNA-seq data

MiCV is a python dash-based web-application that enables researchers to upload raw scRNA-seq data and perform filtering, analysis, and manual annotation.

Scripts for analysis of transcriptomic data of the developing cornea

Repository for benchmarking study of scRNA-Seq datasets for clustering and trajectory inference

my_RNA_seq_pipelines

TimeFlow: a density-driven pseudotime method for flow cytometry data analysis.

Bioinfo scripts for the analyses described in EXPERIMENTAL PROCEDURES section of "Structured wound angiogenesis instructs mesenchymal barrier compartments in the regenerating nerve" manuscript

This script utilizes Monocle3 for inferring pseudotime and employs gradient boosting machine learning (xgboost) to identify genes predictive of pseudotime. Subsequently, it fits a regression model using these newly identified genes.

TENET refined