📌#RUGivingDay is here! Help #RutgersSPH raise money for its scholarship fund - which provides support for students focused on areas like urban, maternal child, and LGBTQ+ health, and environmental health & justice - by giving, sharing, and participating! https://t.co/oyiUVELxnQ
Qian, @vivianstats & co present scINSIGHT, for modeling and interpreting single-cell RNA-seq data from biologically heterogeneous sources, allowing comparison between samples and conditions, based on joint matrix factorization. https://t.co/jbao86dbX3
I am super excited and honored to receive the R35 MIRA award, which will support our work on multi-scale analysis of single-cell transcriptomes. Thank you @NIGMS for this grant for early-stage investigators! Huge thanks to my mentors and colleagues at and outside of @RutgersSPH!
#RutgersSPH's Wei Vivian Li (@vivianstats) receives five-year $1,953,068 @NIGMS Maximizing Investigators' Research Award to develop novel statistical methods & bioinformatics software to further analyze RNA sequencing data at the single-cell level. https://t.co/lso17jZ89r
Very happy to announce that #tidyseurat our second chapter of #tidyTranscriptomics is now public at Bioinformatics!
https://t.co/81uGjcuNho
We also managed to include a nice cheatsheet in the article ;)
Li, Nan, Yang & co present SCAPTURE, to identify polyadenylation sites in 3' tag based single cell RNA-seq data, based around DeepPASS neural network. When applied to PBMC data, it finds differential polyA use in patients infected with SARS-CoV-2 https://t.co/hSJWhWEcdF
Our collaborative work with @BinTian_Lab has just been published in @GenomeBiology ! MAAPER works with both bulk and single-cell sequencing data, and paired or unpaired experimental designs. The R package is available at https://t.co/oHsEX7CDb4. Any feedback is welcome!
MAAPER: model-based analysis of alternative polyadenylation using 3′ end-linked reads, from @vivianstats, @BinTian_lab &co. It can be applied to reads close to, but not spanning, the polyadenylation site, and works with both bulk and single-cell data https://t.co/wFdBZeWAbp
@ruochenj1, @vivianstats and @jsb_ucla describe mbImpute, an imputation method designed for microbiome data that identifies and corrects the zeros that are unlikely biological in microbiome taxon count data https://t.co/VCWRrsZHby
scDesign2: from @TianyiSun17, @SongDongyuan, @vivianstats, @jsb_ucla, a simulator for single-cell RNA-seq data. It is trained on real data, and captures gene correlations present in the real data. It can vary cell number and sequencing depth. https://t.co/CAMqWCLW9M
🌍International students are integral members of our community, enhancing and enriching our learning environment by infusing global perspectives and knowledge into our classrooms. @RutgersSPH will continue to support, protect, and advocate for international students! #RUGlobal