Data analysis workflow

GWAS-eQTL colocalization analysis workflow by Fang Li

RNA-seq data analysis workflow using salmon by Hanrui

RNA-seq analysis in Galaxy and visualization using UCSC genome browser and IGV by Hanrui

Pathway and network analysis workflow by Hanrui

Functional genomic tools and fundamentals (HTML) by Jianting

Single cell RNA-seq analysis using Seurat 2.3.4 (Rmd)/(HTML), Seurat 3.0 (Rmd), and Seurat 3.1 (Rmd)/(HTML) by Sophie and Hanrui

HPC Instructions by Hanrui

Analysis of Label-free Proteomics Data by Perseus by Hanrui



Human induced pluripotent stem cell to macrophage differentiation

We published the protocol to differentiate human induced pluripotent stem cell (iPSC) to macrophages. iPSC-derived macrophages (IPSDM) obtained using this protocol have demonstrated excellent functional and transcriptomic fidelity relative to human monocyte-derived macrophages (HMDM), with advantages and successful applications in disease modeling using patients-derived and CRISPR-edited iPSC lines (ATVB 2017). Through deep RNA-sequencing, the coding transcriptome (Circ Res 2015), alternative splicing events (ATVB 2016) and long non-coding RNA profiles (JAHA 2017) between isogenic IPSDM and HMDM at baseline and during activation are characterized, providing a comprehensive resource for planning IPSDM studies to model such events. This protocol and the associated resources provide a unique platform to understand human macrophage-specific functional, transcriptomic and genomic features in physiology and a broad spectrum of macrophage-relevant diseases.

The RNA-seq data of isogenic IPSDM and HMDM can be visualized in the UCSC genome browser by adding track hub using URL or through the following link. The original RNA-seq data are available from the NCBI Gene Expression Omnibus (GEO) under the accession number GSE55536.

Notes: Some users may find access to the above URL site is intermittent. Please try at a different time or contact if you continue to have problem.


Other Resources

The PISCES pipeline for protein activity inference in single cells.

Data Science Boot Camps by DSI@Columbia

Applied Machine Learning open lectures by Andreas Mueller at Columbia Data Science Institute

HarvardX Biomedical Data Science Open Online Training

Software Capentry Lessons

Data Carpentry Lessons

R for Data Science by Garrett Grolemund and Hadley Wickham

Python Data Science Handbook by Jake VanderPlas

Teaching materials at the Harvard Chan Bioinformatics Core

How to make a html webpage by Columbia Foundations for Research Computing.