Nicholas Tatonetti, Columbia University - Friday June 19, 2015
- Friday February 27th at 11 am (Li Ka Shing Center, Room 130)
Olivier Gevaert, Center for Biomedical Informatics Research, Stanford Unviersity
Pancancer Analysis of DNA Methylation-Driven Genes
- Aberrant DNA methylation is an important mechanism that contributes to oncogenesis. Yet, few algorithms exist that exploit this vast dataset to identify hypo- and hyper-methylated genes in cancer. We developed a novel computational algorithm called MethylMix to identify differentially methylated genes that are also predictive of transcription. We apply MethylMix to twelve individual cancer sites, and additionally combine all cancer sites in a pancancer analysis. We discover pancancer hypo- and hyper-methylated genes and identify novel methylation-driven subgroups with clinical implications. MethylMix analysis on combined cancer sites reveals ten pancancer clusters reflecting new similarities across malignantly transformed tissues.
- Friday January 23rd at 11 am (Li Ka Shing Center, Room 130)
Sheng Zhong, Jacobs School of Engineering, UC San Diego
Single Cell Analysis of Cell Fate Decisions
- It remains an open question when and how the first cell fate decision is made in mammals. Using deep single-cell RNA-seq of matched sister blastomeres, we report highly reproducible inter-blastomere differences among 10 2-cell and five 4-cell mouse embryos. Inter-blastomere gene expression differences dominated between-embryo differences and noise, and were sufficient to cluster sister blastomeres into distinct groups. Dozens of protein-coding genes exhibited reproducible bimodal expression in sister blastomeres, which cannot be explained by random fluctuations. The highly correlated gene pairs at the 4-cell stage overlapped with those showing the same directions of differential expression between inner cell mass (ICM) and trophectoderm (TE). These data substantiate the hypothesis of inter-blastomere differences in 2- and 4-cell mouse embryos, and associate these differences with ICM/TE differences. In addition, we initiated a class of statistical methods that simultaneously infer spatial and temporal groupings. Such methods explicitly model the time dependencies of clustering indices over time. Our method inferred three genes to be associated with the earliest cell fate decision, which was corroborated by experimental validations.