Introduction | Batch effects analysis in large-scale data | Analysis of large-scale data: steps before and after batch correction | Preparation for the analysis | Installation | Preparing the data for analysis | Loading the libraries | Input data formats | Example dataset | Preparing sample and peptide annotations | Defining the order of samples from running date and time | Generating peptide annotation from OpenSWATH data | Other utility functions | Transforming the data to long or wide format | Transforming the data to log scale | Defining the color scheme | Step-by-step workflow | Initial assessment of the raw data matrix | Plotting the sample mean | Plotting boxplots | Normalization | Median normalization | Quantile normalization | Diagnostics of batch effects in normalized data | Hierarchical clustering | Principal component analysis (PCA) | t-SNE and UMAP | Principal variance component analysis (PVCA) | Peptide-level diagnostics and spike-ins | Correction of batch effects | Continuous drift correction | Discrete batch correction: centering, ComBat, or limmaRBE | Feature-level median centering | ComBat | limma removeBatchEffect() (limmaRBE) | Correct batch effects: universal function | Quality control on batch-corrected data matrix | Heatmap of selected replicate samples | Correlation distribution of samples | Correlation of peptide distributions within and between proteins | SessionInfo | Citation | References

Overview | Setup | Installation | Loading required packages | Loading and preparing the example dataset | Constructing a ProBatchFeatures object | Processing pipeline with diagnostics | Step 1 - filtering features with too many missing values | Step 2 - log2-transformation | Step 3 - median normalization | Step 4 - batch-effect correction | Step 5 - assess processed assays | Principal component analysis | t-SNE and UMAP | Hierarchical clustering | Principal Variance Component Analysis (PVCA) | Inspecting operation log | Extracting data matrices from pbf object | Session info | Citation | References