Monthly Archives: July 2016


CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations

Our next meeting will be at 12:30 on August 1st, in room 3160 of the Discovery building. Our Selected paper is CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations.
The abstract is as follows.

Motivation: Identifying alterations in gene expression associated with different clinical states is important for the study of human biology. However, clinical samples used in gene expression studies are often derived from heterogeneous mixtures with variable cell-type composition, complicating statistical analysis. Considerable effort has been devoted to modeling sample heterogeneity, and presently, there are many methods that can estimate cell proportions or pure cell-type expression from mixture data. However, there is no method that comprehensively addresses mixture analysis in the context of differential expression without relying on additional proportion information, which can be inaccurate and is frequently unavailable.

Results: In this study, we consider a clinically relevant situation where neither accurate proportion estimates nor pure cell expression is of direct interest, but where we are rather interested in detecting and interpreting relevant differential expression in mixture samples. We develop a method, Cell-type COmputational Differential Estimation (CellCODE), that addresses the specific statistical question directly, without requiring a physical model for mixture components. Our approach is based on latent variable analysis and is computationally transparent; it requires no additional experimental data, yet outperforms existing methods that use independent proportion measurements. CellCODE has few parameters that are robust and easy to interpret. The method can be used to track changes in proportion, improve power to detect differential expression and assign the differentially expressed genes to the correct cell type. .

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


Epigenomic Co-localization and Co-evolution Reveal a Key Role for 5hmC as a Communication Hub in the Chromatin Network of ESCs

Our selected paper for this week is titled Epigenomic Co-localization and Co-evolution Reveal a Key Role for 5hmC as a Communication Hub in the Chromatin Network of ESCs, from Cell.The abstract is as follows:

Epigenetic communication through histone and cytosine modifications is essential for gene regula- tion and cell identity. Here, we propose a framework that is based on a chromatin communication model to get insight on the function of epigenetic modifica- tions in ESCs. The epigenetic communication network was inferred from genome-wide location data plus extensive manual annotation. Notably, we found that 5-hydroxymethylcytosine (5hmC) is the most-influential hub of this network, connecting DNA demethylation to nucleosome remodeling complexes and to key transcription factors of plurip- otency. Moreover, an evolutionary analysis revealed a central role of 5hmC in the co-evolution of chro- matin-related proteins. Further analysis of regions where 5hmC co-localizes with specific interactors shows that each interaction points to chromatin remodeling, stemness, differentiation, or meta- bolism. Our results highlight the importance of cyto- sine modifications in the epigenetic communication of ESCs.

Feel free to begin our discussion in the comments section below. Our meeting will be at 12:30 PM in room 3160 of the Discovery building on July 18th.