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DTSTART:20241103T020000
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DESCRIPTION:ABSTRACT Traditional meta-analyses pool effect sizes across st
 udies to improve statistical power. Likewise\, there is growing interest 
 in joint clustering across datasets to identify disease subtypes for bulk
  gene expression data and to discover cell types for single-cell RNA-sequ
 encing (scRNA-seq) data. Unfortunately\, due to the prevalence of technic
 al batch effects\, directly clustering of samples from multiple gene expr
 ession datasets can lead to wrong results. Therefore\, in the past severa
 l years\, there has been very active research on the integration of multi
 ple gene expression datasets. However\, the discussion on when multiple g
 ene expression datasets can be integrated for joint clustering is lacking
 . Obviously\, if different subtypes are assayed in distinct batches\, the
 n meta-clustering would be impossible no matter what types of machine lea
 rning or statistical methods are used. In this talk\, I will present our 
 Batch-effects-correction-with-Unknown-Subtypes (BUS) framework. BUS is ca
 pable of adjusting batch effects explicitly\, grouping samples that share
  similar characteristics into subtypes\, identifying genes that distingui
 sh subtypes and enjoying a linear-order computational complexity. The BUS
  framework can be adapted to perform meta-clustering for bulk gene expres
 sion data\, scRNA-seq data collected from a single biological condition\,
  and scRNA-seq data collected from multiple biological conditions\, respe
 ctively. The proofs for model identifiability for the corresponding model
 s provide insights on when multiple gene expression data can be integrate
 d for meta-clustering and guidelines on experimental designs. Simulation 
 studies and real data analyses show the advantages of our proposed models
  over state-of-the-art methods\, especially when performing differential 
 inference for scRNA-seq data collected from multiple conditions.\n\nSpeak
 er:\nYingying Wei\n\nAdmission:\nFree\n\nDetails URL:\nhttps://medicine.y
 ale.edu/event/biostatisticsseminar/\n
DTEND;TZID=America/New_York:20250430T130000
DTSTAMP:20261030T064632Z
DTSTART;TZID=America/New_York:20250430T120000
GEO:41.302961;-72.931638
LOCATION:Rm. 106 B\, 47 College Street\, New Haven\, CT\, United States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:Biostatistics Seminar
UID:312258c3-9d01-4001-89f6-f3f2ae05e7f5
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