Monthly Archives: September 2017


Reversed graph embedding resolves complex single-cell trajectories

Our next meeting will be at 11:00 on September 26th, in room 4160 of the Discovery building. Our Selected paper is Reversed graph embedding resolves complex single-cell trajectories.
The abstract is as follows.

Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.


GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs

Our next meeting will be at 11:00 on September 12th, in room 4160 of the Discovery building. Our Selected paper is GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs.
The abstract is as follows.

The three-dimensional organization of chromatin plays a critical role in gene regulation and disease. High-throughput chromosome conformation capture experiments such as Hi-C are used to obtain genome-wide maps of 3D chromatin contacts. However, robust estimation of data quality and systematic comparison of these contact maps is challenging due to the multi-scale, hierarchical structure of the data and the resulting idiosyncratic properties of experimental noise. We introduce a multi-scale concordance measure called GenomeDISCO (DIfferences between Smoothed COntact maps) for assessing the similarity of a pair of contact maps obtained from chromosome capture experiments. We denoise the contact maps using random walks on the contact map graph, and integrate concordance at multiple scales of smoothing. We use simulated datasets to benchmark GenomeDISCO’s sensitivity to different types of noise typically affecting chromatin contact maps. When applied to a large collection of Hi-C datasets, GenomeDISCO accurately distinguishes biological replicates from samples obtained from different cell types. Software implementing GenomeDISCO is available at http://github.com/kundajelab/genomedisco.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.