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Activity Number: 318 - Advances on the Analysis of Single-Cell Sequencing Data
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract #326510 Presentation
Title: General and Flexible Methods for Signal Extraction from Single-Cell RNA-Seq Data
Author(s): Davide Risso*
Companies: Weill Cornell Medicine
Keywords: single-cell; RNA-seq; dimensionality reduction; factor analysis; clustering; differential expression

Single-cell RNA sequencing (scRNA-seq) is a powerful technique that enables researchers to measure gene expression at the resolution of single cells. Because of the low amount of RNA present in a single cell, many genes fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulations and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA). Furthermore, the model can be used to compute cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data.

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

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