Statistical and computational methods for single-cell transcriptome sequencing and metagenomics

منو

" Statistical and computational methods for single-cell transcriptome sequencing and metagenomics "
Perraudeau, Fanny Dudoit, Sandrine

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	896978
Doc. No	:	TL9qb8c96h
Main Entry	:	Perraudeau, Fanny
Title & Author	:	Statistical and computational methods for single-cell transcriptome sequencing and metagenomics\ Perraudeau, FannyDudoit, Sandrine
Date	:	2018
student score	:	2018
Abstract	:	I propose statistical methods and software for the analysis of single-cell transcriptome sequencing (scRNA-seq) and metagenomics data. Specifically, I present a general and flexible zero-inflated negative binomial-based wanted variation extraction (ZINB-WaVE) method, which extracts low-dimensional signal from scRNA-seq read counts, accounting for zero inflation (dropouts), over-dispersion, and the discrete nature of the data. Additionally, I introduce an application of the ZINB-WaVE method that identifies excess zero counts and generates gene and cell-specific weights to unlock bulk RNA-seq differential expression pipelines for zero-inflated data, boosting performance for scRNA-seq analysis. Finally, I present a method to estimate bacterial abundances in human metagenomes using full-length 16S sequencing reads.
Added Entry	:	Perraudeau, Fanny
Added Entry	:	UC Berkeley