Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

Abstract Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalizatio...
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Gespeichert in:

Autor*in:	Townes, F. William [verfasserIn] Irizarry, Rafael A. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Gene expression Single cell RNA-seq Normalization Quasi-UMI

Übergeordnetes Werk:	Enthalten in: Genome biology - London : BioMed Central, 2000, 21(2020), 1 vom: 03. Juli
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:1 ; day:03 ; month:07

Links:	Volltext

DOI / URN:	10.1186/s13059-020-02078-0

Katalog-ID:	SPR040233197

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spelling	10.1186/s13059-020-02078-0 doi (DE-627)SPR040233197 (SPR)s13059-020-02078-0-e DE-627 ger DE-627 rakwb eng 570 ASE 42.13 bkl 42.20 bkl Townes, F. William verfasserin aut Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets. Gene expression (dpeaa)DE-He213 Single cell (dpeaa)DE-He213 RNA-seq (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Quasi-UMI (dpeaa)DE-He213 Irizarry, Rafael A. verfasserin aut Enthalten in Genome biology London : BioMed Central, 2000 21(2020), 1 vom: 03. Juli (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:21 year:2020 number:1 day:03 month:07 https://dx.doi.org/10.1186/s13059-020-02078-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.13 ASE 42.20 ASE AR 21 2020 1 03 07
allfields_unstemmed	10.1186/s13059-020-02078-0 doi (DE-627)SPR040233197 (SPR)s13059-020-02078-0-e DE-627 ger DE-627 rakwb eng 570 ASE 42.13 bkl 42.20 bkl Townes, F. William verfasserin aut Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets. Gene expression (dpeaa)DE-He213 Single cell (dpeaa)DE-He213 RNA-seq (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Quasi-UMI (dpeaa)DE-He213 Irizarry, Rafael A. verfasserin aut Enthalten in Genome biology London : BioMed Central, 2000 21(2020), 1 vom: 03. Juli (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:21 year:2020 number:1 day:03 month:07 https://dx.doi.org/10.1186/s13059-020-02078-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.13 ASE 42.20 ASE AR 21 2020 1 03 07
allfieldsGer	10.1186/s13059-020-02078-0 doi (DE-627)SPR040233197 (SPR)s13059-020-02078-0-e DE-627 ger DE-627 rakwb eng 570 ASE 42.13 bkl 42.20 bkl Townes, F. William verfasserin aut Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets. Gene expression (dpeaa)DE-He213 Single cell (dpeaa)DE-He213 RNA-seq (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Quasi-UMI (dpeaa)DE-He213 Irizarry, Rafael A. verfasserin aut Enthalten in Genome biology London : BioMed Central, 2000 21(2020), 1 vom: 03. Juli (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:21 year:2020 number:1 day:03 month:07 https://dx.doi.org/10.1186/s13059-020-02078-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.13 ASE 42.20 ASE AR 21 2020 1 03 07
allfieldsSound	10.1186/s13059-020-02078-0 doi (DE-627)SPR040233197 (SPR)s13059-020-02078-0-e DE-627 ger DE-627 rakwb eng 570 ASE 42.13 bkl 42.20 bkl Townes, F. William verfasserin aut Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets. Gene expression (dpeaa)DE-He213 Single cell (dpeaa)DE-He213 RNA-seq (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Quasi-UMI (dpeaa)DE-He213 Irizarry, Rafael A. verfasserin aut Enthalten in Genome biology London : BioMed Central, 2000 21(2020), 1 vom: 03. Juli (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:21 year:2020 number:1 day:03 month:07 https://dx.doi.org/10.1186/s13059-020-02078-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.13 ASE 42.20 ASE AR 21 2020 1 03 07
language	English
source	Enthalten in Genome biology 21(2020), 1 vom: 03. Juli volume:21 year:2020 number:1 day:03 month:07
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topic_facet	Gene expression Single cell RNA-seq Normalization Quasi-UMI
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authorswithroles_txt_mv	Townes, F. William @@aut@@ Irizarry, Rafael A. @@aut@@
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author	Townes, F. William
spellingShingle	Townes, F. William ddc 570 bkl 42.13 bkl 42.20 misc Gene expression misc Single cell misc RNA-seq misc Normalization misc Quasi-UMI Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers
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topic_title	570 ASE 42.13 bkl 42.20 bkl Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers Gene expression (dpeaa)DE-He213 Single cell (dpeaa)DE-He213 RNA-seq (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Quasi-UMI (dpeaa)DE-He213
topic	ddc 570 bkl 42.13 bkl 42.20 misc Gene expression misc Single cell misc RNA-seq misc Normalization misc Quasi-UMI
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title	Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers
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title_full	Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers
author_sort	Townes, F. William
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author_browse	Townes, F. William Irizarry, Rafael A.
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title_sort	quantile normalization of single-cell rna-seq read counts without unique molecular identifiers
title_auth	Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers
abstract	Abstract Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.
abstractGer	Abstract Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.
abstract_unstemmed	Abstract Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.
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title_short	Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers
url	https://dx.doi.org/10.1186/s13059-020-02078-0
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author2	Irizarry, Rafael A.
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doi_str	10.1186/s13059-020-02078-0
up_date	2024-07-03T14:39:23.302Z
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