April 06, 2021
We are very excited to announce that Daniel Osorio has joined the lab as a postdoctoral fellow through the Marie Curie Scientia Fellows II program. Daniel is joining us from the University of A&M Texas, where he did his PhD in the group of James Cai. Daniel will be working on regulatory network modeling in single-cell data. Here's a news article to learn more.
March 26, 2021
We have a new BioRχiv pre-print by Ping-Han! Ping-Han found that certain normalization methods, in specific quantile-based methods, can introduce false positive associations in co-expression measurements. However, there methods can be very powerful. Smooth quantile normalization, for example, allows one to normalize heterogeneous data, while keeping global expression differences between different subgroups of samples. Ping-Han therefore developed a new algorithm, called CAIMAN, to correct for these false-positive associations....
March 12, 2021
We recently contributed to a new publication as well as a newly posted BioRxiv pre-print. The new publication in PLoS ONE done in collaboration with Ola Myklebost and Leonardo Meza-Zepeda's groups from the University of Bergen and Oslo, respectively. It involved high-throughput drug screening to discover novel candidates for treatment of liposarcoma. We contributed with bioinformatics analyses to identify potential biomarkers of drug response....
March 10, 2021
We are very excited to announce that Daniel Osorio received a Marie Curie Scientia Fellowship and will join our group this spring as a postdoctoral fellow. Daniel received his PhD from Texas A&M University, where he worked in Dr. James J. Cai's group on method development for single-cell RNA-sequencing data analysis. In his postdoctoral project, Daniel will be developing computational tools to integrate multi-omic and multimodal single-cell data...
February 12, 2021
We're happy to share a new BioRχiv pre-print by the group, which was work done in collaboration the Quackenbush group. We used large-scale genomic network modeling approaches PANDA and LIONESS to model networks for individual glioblastoma patients, and identified regulation of PD1 signaling to be associated with glioblastoma outcome. More information on the pre-print can be found here.