Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial
Čuklina J, Lee C, Williams E, Sajic T, Collins B, Rodríguez Martínez M, Sharma V, Wendt F, Goetze S, Keele G, Wollscheid B, Aebersold R, Pedrioli P. Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial. Molecular Systems Biology 2021, 17: msb202110240. PMID: 34432947, PMCID: PMC8447595, DOI: 10.15252/msb.202110240.Peer-Reviewed Original ResearchConceptsBatch effectsProteomic studiesLarge-scale proteomic studiesCorrection of batch effectsMass spectrometry-based proteomicsStep-by-step protocolProteomic datasetsProteomic dataSystems biologyBatch correctionMultiple experimental designsProteomic ChallengeR packageProteomicsClinical proteomicsBiological signalsTechnical variabilityStatistical powerIntensity driftBiologyOn the feasibility of deep learning applications using raw mass spectrometry data
Cadow J, Manica M, Mathis R, Reddel R, Robinson P, Wild P, Hains P, Lucas N, Zhong Q, Guo T, Aebersold R, Martínez M. On the feasibility of deep learning applications using raw mass spectrometry data. Bioinformatics 2021, 37: i245-i253. PMID: 34252933, PMCID: PMC8275322, DOI: 10.1093/bioinformatics/btab311.Peer-Reviewed Original ResearchMeSH KeywordsDeep LearningFeasibility StudiesHumansMaleMass SpectrometryNeural Networks, ComputerProteomicsReproducibility of ResultsConceptsRaw mass spectrometry dataDeep learning modelsRaw MS dataMass spectrometry dataClassification performanceDeep learningMS dataMass spectrometryLearning modelsSpectrometry dataApplication of deep learningMS imagesNatural image classificationDeep learning applicationsPrivacy of individualsTransfer learning techniqueData-independent-acquisitionMS2 spectraClassification taskData processing pipelinesClassification labelsImage classificationFeature vectorTransfer learningSample sparsity