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Activity Number: 527 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306408
Title: Establishing Single Cell RNA-Seq Data Analysis Pipeline in the Industry Setting
Author(s): Oleg Mayba* and Milena Duerrbaum and Robert Piskol and Leonard Goldstein and Kevin Huang and Josh Kaminker and Aaron Lun and Kiran Mukhyala and Luz Orozco-Guerra and Thomas Wu and Matthew Chang and Brad Friedman and Jason Hackney
Companies: Genentech, Inc and Genentech, Inc and Genentech, Inc and Genentech, Inc and Genentech, Inc and Genentech, Inc and Genentech, Inc and Genentech, Inc and Genentech, Inc and Genentech, Inc and Genentech, Inc and Genentech, Inc and Genentech, Inc
Keywords: single cell RNA-Seq
Abstract:

Single cell RNA-Seq (scRNA-Seq) is a biological assay that allows scientists to investigate cellular transcriptomics at a single cell resolution. Applications of scRNA-Seq include cataloguing different cell types present in samples of interest, assessing changes in cell composition or gene expression between different biological conditions, and identifying cell differentiation trajectories, among others. For a given scRNA-Seq experiment, data analysis workflow consists of a number of steps (e.g., normalization, dimensionality reduction, clustering, differential gene expression analysis, etc...), each of which can be performed using one of the many proposed competing statistical methods and algorithms. Here we describe our strategy in selecting among the different methods with an aim towards establishing a unified, scalable and flexible scRNA-Seq data analysis pipeline.


Authors who are presenting talks have a * after their name.

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