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Introduction to proteomics data analysis - MaxQuant Data Dependent Acquisition spike-in study6 months ago
Introduction | Background | Load packages | Data | Peptide table | Sample annotation | Convert to QFeatures | Data preprocessing | Encoding missing values | $\log_2$ transformation | Peptide-Filtering | Remove failed protein inference | Remove reverse sequences and contaminants | Remove highly missing peptides | Normalisation | Summarization to protein level | Data exploration and QC | Marginal distribution at precursor and protein level | Identifications per sample | Dimensionality reduction plot | Correlation matrix | Data Modeling (Robust Regression) | Sources of variation | Model Estimation | Statistical inference | Results table for significant proteins | Volcanoplots | Heatmaps | Assess performance | Real Fold changes | True and false positives | TPR - FDP curves | References
Differential abundance analysis for Data Independent Acquistion (DIA-NN - starting from Precursor.Quantity)6 months ago
Load packages | Data | Precursor table | Sample annotation table | Convert to QFeatures | Data preprocessing | Encoding missing values | Precursor Filtering | Remove questionable identifications | Assay joining | Filtering: Remove highly missing precursors | Filter one-hit wonders | Log-transformation | Normalisation | Summarisation | Data exploration and QC | Marginal distribution at precursor and protein level | Charge state | Identifications per sample | Dimensionality reduction plot | Correlation matrix | Data Modeling (Robust Regression) | Sources of variation | Model Estimation | Statistical inference | Results tables for significant proteins | Volcanoplots | Heatmaps | Assess performance | Real Fold changes | True and false positives | TPR - FDP curves | Volcano-plots | References
Differential abundance analysis for Data Independent Acquistion (Spectronaut - starting from FG_MS2RawQuantity)6 months ago
Load packages | Data | Precursor table | Sample annotation table | Convert to QFeatures | Data preprocessing | Encoding missing values | Precursor Filtering | Remove questionable identifications | Assay joining | Filtering: Remove highly missing precursors | Filter one-hit wonders | Log-transformation | Normalisation | Summarisation | Data exploration and QC | Marginal distribution at precursor and protein level | Charge state | Identifications per sample | Dimensionality reduction plot | Correlation matrix | Data Modeling (Robust Regression) | Sources of variation | Model Estimation | Statistical inference | Results tables for significant proteins | Volcanoplots | Heatmaps | Assess performance | Real Fold changes | True and false positives | TPR - FDP curves | Volcano-plots | References