Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial
Abstract Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Čuklina, Jelena [verfasserIn] Lee, Chloe H [verfasserIn] Williams, Evan G [verfasserIn] Sajic, Tatjana [verfasserIn] Collins, Ben C [verfasserIn] Rodríguez Martínez, María [verfasserIn] Sharma, Varun S [verfasserIn] Wendt, Fabian [verfasserIn] Goetze, Sandra [verfasserIn] Keele, Gregory R [verfasserIn] Wollscheid, Bernd [verfasserIn] Aebersold, Ruedi [verfasserIn] Pedrioli, Patrick G A [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Molecular Systems Biology - Nature Publishing Group UK, 2023, 17(2021), 8 vom: 25. Aug. |
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| Übergeordnetes Werk: |
volume:17 ; year:2021 ; number:8 ; day:25 ; month:08 |
| Links: |
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| DOI / URN: |
10.15252/msb.202110240 |
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| Katalog-ID: |
SPR058028994 |
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| 245 | 1 | 0 | |a Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial |
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| 520 | |a Abstract Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. | ||
| 520 | |a Graphical Abstract In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data. | ||
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| 650 | 4 | |a normalization |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Lee, Chloe H |e verfasserin |0 (orcid)0000-0002-6232-7119 |4 aut | |
| 700 | 1 | |a Williams, Evan G |e verfasserin |0 (orcid)0000-0002-9746-376X |4 aut | |
| 700 | 1 | |a Sajic, Tatjana |e verfasserin |0 (orcid)0000-0003-4282-1336 |4 aut | |
| 700 | 1 | |a Collins, Ben C |e verfasserin |0 (orcid)0000-0003-0827-3495 |4 aut | |
| 700 | 1 | |a Rodríguez Martínez, María |e verfasserin |0 (orcid)0000-0003-3766-4233 |4 aut | |
| 700 | 1 | |a Sharma, Varun S |e verfasserin |0 (orcid)0000-0002-4531-640X |4 aut | |
| 700 | 1 | |a Wendt, Fabian |e verfasserin |0 (orcid)0000-0002-2501-536X |4 aut | |
| 700 | 1 | |a Goetze, Sandra |e verfasserin |0 (orcid)0000-0001-6880-8020 |4 aut | |
| 700 | 1 | |a Keele, Gregory R |e verfasserin |0 (orcid)0000-0002-1843-7900 |4 aut | |
| 700 | 1 | |a Wollscheid, Bernd |e verfasserin |0 (orcid)0000-0002-3923-1610 |4 aut | |
| 700 | 1 | |a Aebersold, Ruedi |e verfasserin |0 (orcid)0000-0002-9576-3267 |4 aut | |
| 700 | 1 | |a Pedrioli, Patrick G A |e verfasserin |0 (orcid)0000-0001-6719-9139 |4 aut | |
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10.15252/msb.202110240 doi (DE-627)SPR058028994 (SPR)msb.202110240-e DE-627 ger DE-627 rakwb eng Čuklina, Jelena verfasserin (orcid)0000-0002-5220-8642 aut Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. Graphical Abstract In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data. batch effects (dpeaa)DE-He213 data analysis (dpeaa)DE-He213 large‐scale proteomics (dpeaa)DE-He213 normalization (dpeaa)DE-He213 quantitative proteomics (dpeaa)DE-He213 Lee, Chloe H verfasserin (orcid)0000-0002-6232-7119 aut Williams, Evan G verfasserin (orcid)0000-0002-9746-376X aut Sajic, Tatjana verfasserin (orcid)0000-0003-4282-1336 aut Collins, Ben C verfasserin (orcid)0000-0003-0827-3495 aut Rodríguez Martínez, María verfasserin (orcid)0000-0003-3766-4233 aut Sharma, Varun S verfasserin (orcid)0000-0002-4531-640X aut Wendt, Fabian verfasserin (orcid)0000-0002-2501-536X aut Goetze, Sandra verfasserin (orcid)0000-0001-6880-8020 aut Keele, Gregory R verfasserin (orcid)0000-0002-1843-7900 aut Wollscheid, Bernd verfasserin (orcid)0000-0002-3923-1610 aut Aebersold, Ruedi verfasserin (orcid)0000-0002-9576-3267 aut Pedrioli, Patrick G A verfasserin (orcid)0000-0001-6719-9139 aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 17(2021), 8 vom: 25. Aug. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:17 year:2021 number:8 day:25 month:08 https://dx.doi.org/10.15252/msb.202110240 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER 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_72 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 17 2021 8 25 08 |
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10.15252/msb.202110240 doi (DE-627)SPR058028994 (SPR)msb.202110240-e DE-627 ger DE-627 rakwb eng Čuklina, Jelena verfasserin (orcid)0000-0002-5220-8642 aut Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. Graphical Abstract In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data. batch effects (dpeaa)DE-He213 data analysis (dpeaa)DE-He213 large‐scale proteomics (dpeaa)DE-He213 normalization (dpeaa)DE-He213 quantitative proteomics (dpeaa)DE-He213 Lee, Chloe H verfasserin (orcid)0000-0002-6232-7119 aut Williams, Evan G verfasserin (orcid)0000-0002-9746-376X aut Sajic, Tatjana verfasserin (orcid)0000-0003-4282-1336 aut Collins, Ben C verfasserin (orcid)0000-0003-0827-3495 aut Rodríguez Martínez, María verfasserin (orcid)0000-0003-3766-4233 aut Sharma, Varun S verfasserin (orcid)0000-0002-4531-640X aut Wendt, Fabian verfasserin (orcid)0000-0002-2501-536X aut Goetze, Sandra verfasserin (orcid)0000-0001-6880-8020 aut Keele, Gregory R verfasserin (orcid)0000-0002-1843-7900 aut Wollscheid, Bernd verfasserin (orcid)0000-0002-3923-1610 aut Aebersold, Ruedi verfasserin (orcid)0000-0002-9576-3267 aut Pedrioli, Patrick G A verfasserin (orcid)0000-0001-6719-9139 aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 17(2021), 8 vom: 25. Aug. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:17 year:2021 number:8 day:25 month:08 https://dx.doi.org/10.15252/msb.202110240 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER 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_72 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 17 2021 8 25 08 |
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10.15252/msb.202110240 doi (DE-627)SPR058028994 (SPR)msb.202110240-e DE-627 ger DE-627 rakwb eng Čuklina, Jelena verfasserin (orcid)0000-0002-5220-8642 aut Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. Graphical Abstract In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data. batch effects (dpeaa)DE-He213 data analysis (dpeaa)DE-He213 large‐scale proteomics (dpeaa)DE-He213 normalization (dpeaa)DE-He213 quantitative proteomics (dpeaa)DE-He213 Lee, Chloe H verfasserin (orcid)0000-0002-6232-7119 aut Williams, Evan G verfasserin (orcid)0000-0002-9746-376X aut Sajic, Tatjana verfasserin (orcid)0000-0003-4282-1336 aut Collins, Ben C verfasserin (orcid)0000-0003-0827-3495 aut Rodríguez Martínez, María verfasserin (orcid)0000-0003-3766-4233 aut Sharma, Varun S verfasserin (orcid)0000-0002-4531-640X aut Wendt, Fabian verfasserin (orcid)0000-0002-2501-536X aut Goetze, Sandra verfasserin (orcid)0000-0001-6880-8020 aut Keele, Gregory R verfasserin (orcid)0000-0002-1843-7900 aut Wollscheid, Bernd verfasserin (orcid)0000-0002-3923-1610 aut Aebersold, Ruedi verfasserin (orcid)0000-0002-9576-3267 aut Pedrioli, Patrick G A verfasserin (orcid)0000-0001-6719-9139 aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 17(2021), 8 vom: 25. Aug. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:17 year:2021 number:8 day:25 month:08 https://dx.doi.org/10.15252/msb.202110240 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER 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_72 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 17 2021 8 25 08 |
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10.15252/msb.202110240 doi (DE-627)SPR058028994 (SPR)msb.202110240-e DE-627 ger DE-627 rakwb eng Čuklina, Jelena verfasserin (orcid)0000-0002-5220-8642 aut Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. Graphical Abstract In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data. batch effects (dpeaa)DE-He213 data analysis (dpeaa)DE-He213 large‐scale proteomics (dpeaa)DE-He213 normalization (dpeaa)DE-He213 quantitative proteomics (dpeaa)DE-He213 Lee, Chloe H verfasserin (orcid)0000-0002-6232-7119 aut Williams, Evan G verfasserin (orcid)0000-0002-9746-376X aut Sajic, Tatjana verfasserin (orcid)0000-0003-4282-1336 aut Collins, Ben C verfasserin (orcid)0000-0003-0827-3495 aut Rodríguez Martínez, María verfasserin (orcid)0000-0003-3766-4233 aut Sharma, Varun S verfasserin (orcid)0000-0002-4531-640X aut Wendt, Fabian verfasserin (orcid)0000-0002-2501-536X aut Goetze, Sandra verfasserin (orcid)0000-0001-6880-8020 aut Keele, Gregory R verfasserin (orcid)0000-0002-1843-7900 aut Wollscheid, Bernd verfasserin (orcid)0000-0002-3923-1610 aut Aebersold, Ruedi verfasserin (orcid)0000-0002-9576-3267 aut Pedrioli, Patrick G A verfasserin (orcid)0000-0001-6719-9139 aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 17(2021), 8 vom: 25. Aug. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:17 year:2021 number:8 day:25 month:08 https://dx.doi.org/10.15252/msb.202110240 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER 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_72 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 17 2021 8 25 08 |
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10.15252/msb.202110240 doi (DE-627)SPR058028994 (SPR)msb.202110240-e DE-627 ger DE-627 rakwb eng Čuklina, Jelena verfasserin (orcid)0000-0002-5220-8642 aut Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. Graphical Abstract In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data. batch effects (dpeaa)DE-He213 data analysis (dpeaa)DE-He213 large‐scale proteomics (dpeaa)DE-He213 normalization (dpeaa)DE-He213 quantitative proteomics (dpeaa)DE-He213 Lee, Chloe H verfasserin (orcid)0000-0002-6232-7119 aut Williams, Evan G verfasserin (orcid)0000-0002-9746-376X aut Sajic, Tatjana verfasserin (orcid)0000-0003-4282-1336 aut Collins, Ben C verfasserin (orcid)0000-0003-0827-3495 aut Rodríguez Martínez, María verfasserin (orcid)0000-0003-3766-4233 aut Sharma, Varun S verfasserin (orcid)0000-0002-4531-640X aut Wendt, Fabian verfasserin (orcid)0000-0002-2501-536X aut Goetze, Sandra verfasserin (orcid)0000-0001-6880-8020 aut Keele, Gregory R verfasserin (orcid)0000-0002-1843-7900 aut Wollscheid, Bernd verfasserin (orcid)0000-0002-3923-1610 aut Aebersold, Ruedi verfasserin (orcid)0000-0002-9576-3267 aut Pedrioli, Patrick G A verfasserin (orcid)0000-0001-6719-9139 aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 17(2021), 8 vom: 25. Aug. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:17 year:2021 number:8 day:25 month:08 https://dx.doi.org/10.15252/msb.202110240 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER 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_72 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 17 2021 8 25 08 |
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Enthalten in Molecular Systems Biology 17(2021), 8 vom: 25. Aug. volume:17 year:2021 number:8 day:25 month:08 |
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Enthalten in Molecular Systems Biology 17(2021), 8 vom: 25. Aug. volume:17 year:2021 number:8 day:25 month:08 |
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Čuklina, Jelena @@aut@@ Lee, Chloe H @@aut@@ Williams, Evan G @@aut@@ Sajic, Tatjana @@aut@@ Collins, Ben C @@aut@@ Rodríguez Martínez, María @@aut@@ Sharma, Varun S @@aut@@ Wendt, Fabian @@aut@@ Goetze, Sandra @@aut@@ Keele, Gregory R @@aut@@ Wollscheid, Bernd @@aut@@ Aebersold, Ruedi @@aut@@ Pedrioli, Patrick G A @@aut@@ |
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2021-08-25T00:00:00Z |
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While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. 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Čuklina, Jelena Lee, Chloe H Williams, Evan G Sajic, Tatjana Collins, Ben C Rodríguez Martínez, María Sharma, Varun S Wendt, Fabian Goetze, Sandra Keele, Gregory R Wollscheid, Bernd Aebersold, Ruedi Pedrioli, Patrick G A |
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diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial |
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Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial |
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Abstract Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. Graphical Abstract In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data. © The Author(s) 2021 |
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Abstract Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. Graphical Abstract In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data. © The Author(s) 2021 |
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Abstract Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. Graphical Abstract In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data. © The Author(s) 2021 |
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| score |
7.4005365 |