msmsEDA & msmsTests : Label-Free Differential Expression by Spectral Counts
© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature..
msmsTests is an R/Bioconductor package providing functions for statistical tests in label-free LC-MS/MS data by spectral counts. These functions aim at discovering differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package. The three models admit blocking factors to control for nuisance variables. To assure a good level of reproducibility a post-test filter is available, where (1) a minimum effect size considered biologically relevant, and (2) a minimum expression of the most abundant condition, may be set. A companion package, msmsEDA, proposes functions to explore datasets based on msms spectral counts. The provided graphics help in identifying outliers, the presence of eventual batch factors, and check the effects of different normalizing strategies. This protocol illustrates the use of both packages on two examples: A purely spike-in experiment of 48 human proteins in a standard yeast cell lysate; and a cancer cell-line secretome dataset requiring a biological normalization.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2426 |
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| Enthalten in: |
Methods in molecular biology (Clifton, N.J.) - 2426(2023) vom: 29., Seite 197-242 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gregori, Josep [VerfasserIn] |
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| Links: |
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| Themen: |
Batch effects |
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| Anmerkungen: |
Date Completed 01.11.2022 Date Revised 07.11.2022 published: Print Citation Status MEDLINE |
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| doi: |
10.1007/978-1-0716-1967-4_10 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM348229747 |
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| 520 | |a msmsTests is an R/Bioconductor package providing functions for statistical tests in label-free LC-MS/MS data by spectral counts. These functions aim at discovering differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package. The three models admit blocking factors to control for nuisance variables. To assure a good level of reproducibility a post-test filter is available, where (1) a minimum effect size considered biologically relevant, and (2) a minimum expression of the most abundant condition, may be set. A companion package, msmsEDA, proposes functions to explore datasets based on msms spectral counts. The provided graphics help in identifying outliers, the presence of eventual batch factors, and check the effects of different normalizing strategies. This protocol illustrates the use of both packages on two examples: A purely spike-in experiment of 48 human proteins in a standard yeast cell lysate; and a cancer cell-line secretome dataset requiring a biological normalization | ||
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