Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

Enjoy your transcriptomic data and analysis responsibly - like sipping a cocktail

pcaExplorer - Interactive exploration of Principal Components of Samples and Genes in RNA-seq data

Evolutionary Transcriptomics with R

TreeExp 2.0: Toolbox for analyzing expression evolution based on RNA-seq count data

AnceTran2.0: R package for transcriptome evolution analysis based on RNA-seq expression data or ChIP-seq TF-binding data

TrendCatcher is an open source R-package that allows users to systematically analyze and visualize time course data. Please cite "Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19" by Xinge Wang et al published in JCI Insight (2022) - https://insight.jci.org/articles/view…

RefEx: a reference gene expression dataset of mammalian tissues and cell lines measured by different methods

RepeatMasker Trinity based Parse Script

A collection of custom scripts used in the placenta transcriptome paper, Gong et al. Nat Comm, 2021

De nove assembly and annotation of Fragilaria radians transcriptome

Pipeline for low-level RNA-Seq data processing

Matrix factorization-based biological discovery from large-scale transcriptome data using easyMF

The package RNAseqAnalysis does the complete analysis of RNA seq data starting from raw reads. It provides the user with differnt functions like generation of qc report, filtering, assembly and GO-term annotation, differential expression analysis and heatmap generation, and Alternative splicing-site prediction

This repository houses the pipeline I coded to perform differential analysis of transcriptomes from two oyster species, C. gigas and C. virginica and isolate genes in the apoptosis pathway.

Code and example data for transcriptional-data-guided brain network classification

Back-end R package for running anexvis web application

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

Code to reproduce Adaptive elastic-net sparse PCA for robust cross-species, cross-platform analysis of complex gene expression data in Alzheimer’s disease (Hin et al.)