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Non-linear Normalization for Non-UMI Single Cell RNA-Seq

Single cell RNA-seq data, like data from other sequencing technology, contain systematic technical noise. Such noise results from a combined effect of unequal efficiencies in the capturing and counting of mRNA molecules, such as extraction/amplification efficiency and sequencing depth. We show that...

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Detalles Bibliográficos
Autores principales: Wu, Zhijin, Su, Kenong, Wu, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8063035/
https://www.ncbi.nlm.nih.gov/pubmed/33897755
http://dx.doi.org/10.3389/fgene.2021.612670
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author Wu, Zhijin
Su, Kenong
Wu, Hao
author_facet Wu, Zhijin
Su, Kenong
Wu, Hao
author_sort Wu, Zhijin
collection PubMed
description Single cell RNA-seq data, like data from other sequencing technology, contain systematic technical noise. Such noise results from a combined effect of unequal efficiencies in the capturing and counting of mRNA molecules, such as extraction/amplification efficiency and sequencing depth. We show that such technical effects are not only cell-specific, but also affect genes differently, thus a simple cell-wise size factor adjustment may not be sufficient. We present a non-linear normalization approach that provides a cell- and gene-specific normalization factor for each gene in each cell. We show that the proposed normalization method (implemented in “SC2P" package) reduces more technical variation than competing methods, without reducing biological variation. When technical effects such as sequencing depths are not balanced between cell populations, SC2P normalization also removes the bias due to uneven technical noise. This method is applicable to scRNA-seq experiments that do not use unique molecular identifier (UMI) thus retain amplification biases.
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spelling pubmed-80630352021-04-24 Non-linear Normalization for Non-UMI Single Cell RNA-Seq Wu, Zhijin Su, Kenong Wu, Hao Front Genet Genetics Single cell RNA-seq data, like data from other sequencing technology, contain systematic technical noise. Such noise results from a combined effect of unequal efficiencies in the capturing and counting of mRNA molecules, such as extraction/amplification efficiency and sequencing depth. We show that such technical effects are not only cell-specific, but also affect genes differently, thus a simple cell-wise size factor adjustment may not be sufficient. We present a non-linear normalization approach that provides a cell- and gene-specific normalization factor for each gene in each cell. We show that the proposed normalization method (implemented in “SC2P" package) reduces more technical variation than competing methods, without reducing biological variation. When technical effects such as sequencing depths are not balanced between cell populations, SC2P normalization also removes the bias due to uneven technical noise. This method is applicable to scRNA-seq experiments that do not use unique molecular identifier (UMI) thus retain amplification biases. Frontiers Media S.A. 2021-04-09 /pmc/articles/PMC8063035/ /pubmed/33897755 http://dx.doi.org/10.3389/fgene.2021.612670 Text en Copyright © 2021 Wu, Su and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Wu, Zhijin
Su, Kenong
Wu, Hao
Non-linear Normalization for Non-UMI Single Cell RNA-Seq
title Non-linear Normalization for Non-UMI Single Cell RNA-Seq
title_full Non-linear Normalization for Non-UMI Single Cell RNA-Seq
title_fullStr Non-linear Normalization for Non-UMI Single Cell RNA-Seq
title_full_unstemmed Non-linear Normalization for Non-UMI Single Cell RNA-Seq
title_short Non-linear Normalization for Non-UMI Single Cell RNA-Seq
title_sort non-linear normalization for non-umi single cell rna-seq
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8063035/
https://www.ncbi.nlm.nih.gov/pubmed/33897755
http://dx.doi.org/10.3389/fgene.2021.612670
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