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DESCRIPTION:Jingyi Jessica Li\, PhD\n\nProfessor of Statistics and Data Sci
 ence\n	University of California\, Los Angeles (UCLA)\n\nWHEN: Wednesday\, J
 anuary 17\, 2024\, from 3:30 to 4:30 p.m.\n\nWHERE: hybrid | 2001 9IÖÆ×÷³§Ãâ·Ñ C
 ollege Avenue\, room 1140\; Zoom\n\nNOTE: Dr. Li will be presenting from U
 CLA\n\nAbstract\n\nIn typical single-cell RNA-seq (scRNA-seq) data analysi
 s\, a clustering algorithm is applied to find discrete cell clusters as pu
 tative cell types\, and then a statistical test is employed to identify th
 e differentially expressed (DE) genes between the cell clusters. However\,
  this common procedure suffers the 'double dipping' issue: the same data a
 re used twice to find discrete cell clusters as putative cell types and DE
  genes as potential cell-type marker genes\, leading to false-positive cel
 l-type marker genes even when the cell clusters are spurious. To overcome 
 this challenge\, we propose ClusterDE\, a post-clustering DE method for co
 ntrolling the false discovery rate (FDR) of identified DE genes regardless
  of clustering quality\, which can work as an add-on to popular pipelines 
 such as Seurat. The core idea of ClusterDE is to generate real-data-based 
 synthetic null data containing only one cell type\, in contrast to the rea
 l data\, for evaluating the whole procedure of clustering followed by a DE
  test. Using comprehensive simulation and real data analysis\, we show tha
 t ClusterDE has solid FDR control and the ability to identify canonical ce
 ll-type marker genes as top DE genes\, distinguishing them from common hou
 sekeeping genes. Notably\, the DE genes identified by ClusterDE are inform
 ative markers for discrete cell types and can guide the merging of spuriou
 s clusters. ClusterDE is fast\, transparent\, and adaptive to a wide range
  of clustering algorithms and DE tests.\n\nSpeaker bio\n\nJingyi Jessica L
 i\, Professor of Statistics and Data Science (also affiliated with Biostat
 istics\, Computational Medicine\, and Human Genetics)\, leads a research g
 roup titled the Junction of Statistics and Biology at UCLA. With Ph.D. fro
 m UC Berkeley and B.S. from Tsinghua University\, Dr. Li focuses on develo
 ping interpretable statistical methods for biomedical data. Her research d
 elves into quantifying the central dogma\, extracting hidden information f
 rom transcriptomics data\, and ensuring statistical rigor in data analysis
  by employing synthetic negative controls. Recipient of multiple awards in
 cluding the NSF CAREER Ward\, Sloan Research Fellowship\, ISCB Overton Pri
 ze\, and COPSS Emerging Leaders Award\, her contributions have gained reco
 gnition in the fields of computational biology and statistics. Website: ht
 tp://jsb.ucla.edu/ \n
DTSTART:20240117T203000Z
DTEND:20240117T213000Z
SUMMARY:ClusterDE: a post-clustering differential expression (DE) method ro
 bust to false-positive inflation caused by double dipping
URL:/epi-biostat-occh/channels/event/clusterde-post-cl
 ustering-differential-expression-de-method-robust-false-positive-inflation
 -caused-353631
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