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DTSTAMP:20210402T160558Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201118T083000
DTEND;TZID=America/New_York:20201118T170000
UID:submissions.supercomputing.org_SC20_sess340_spostu112@linklings.com
SUMMARY:Investigating Inconsistencies in Single-Cell RNA Sequencing Data
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nInvestigating Inco
 nsistencies in Single-Cell RNA Sequencing Data\n\nSingh\n\nSingle-cell RNA
  sequencing (scRNA-seq) has emerged as a robust method for computational g
 enomics. It combines cell biology with computer science in order to analyz
 e thousands of cells at a single-cell level, allowing us to observe gene e
 xpression levels and how they differ across diverse samples. This translat
 es to a knowledge about the exact functions of these cells which can be us
 ed to better understand disease processes and discover exciting new potent
 ial therapies. However, very few studies analyze the effects of preanalyti
 cal variables such as tissue handling and scRNA-seq technique on sequencin
 g results. In this study, we examined three normal ‘human liver’ scRNA-seq
  datasets from high-quality peer-reviewed publications to see if they yiel
 ded comparable results. We hypothesized the pre-analytical variables in sc
 RNA-seq studies would have an impact on characterization of the human live
 r immune cell transcriptome.\n\nTag: Student Program\n\nRegistration Categ
 ory: Tech Program Reg Pass, Exhibits Reg Pass
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