Posts & Tutorials

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  Analyzing high-throughput FACS assays with R

  Traditional flow cytometry analysis software performs badly with large datasets. Here’s an example analysis workflow for a simple FACS-based assay run in 96-well format.

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  Plotting 3C data as arcs – a ggplot solution

  3C data measuring the interaction frequency between two genomic coordinates can be represented graphically as arcs between the anchor (aka viewpoint) and the region of interest. Here’s how to do this with ggplot.

Code and resources from published papers

Unique Immune Cell Coactivators Specify Locus Control Region Function and Cell Stage
Chu and Hellmuth et al.
Mol Cell. 2020. PubMed PMID: 33232656

Resources

• A UCSC track hub to fully explore all relevant data at the BCL6 locus.
• A version of this track hub with highlights for each constituent enhancer.

Code

• In silico design of a maximum-density CRISPR screening library – script 1.1
• Analysis of CRISPRi screening results
  • Screening data normalization and QC – script 2.1
  • Identification of essential enhancers – script 2.2
• R-based FACS analysis for drop-out experiments – tutorial
• Transcription factor motif analysis
  • Motif download, processing and matching to a region of interest (BCL6 locus) – script 3.1
  • Motif density comparison in essential vs non-essential enhancers – script 3.2