# batchCorr Within and between batch correction of LC-MS metabolomics data ## Description This is package contains functions within three areas of batch correction. These algorithms were originally developed to increase quality and information content in data from LC-MS metabolomics. However, the algorithms should be applicable to other data structures/origins, where within and between batch irregularities occur. The three areas indicated are: - alignBatches(): Align features systematically misaligned between batches - correctDrift(): Perform within-batch intensity drift correction - normalizeBatches(): Perform between-batch normalization ### Batch alignment Batch alignment is achieved based on three concepts: - Aggregation of feature presence/missingness on batch level. - Identifying features with missingness within "the box", i.e. sufficiently similar in retention time and m/z. - Ensuring orthogonal batch presence among feature alignment candidates. ### Drift correction Drift correction is achieved based on: - Clustering is performed on features in observation space (as opposed to the normally used observations in feature space) - Clustering provides a tradeoff between - modelling detail (multiple drift patterns within data set) - power per drift pattern - Unbiased clustering is achieved using the Bayesian `mclust` R package ### Batch normalisation Batch normalisation is achieved based on: - QC/Reference (standard normalisation) or population (median normalisation) - The choice between the two is based on a quality heuristic determining whether the QC/Ref samples are suitable for normalization. Otherwise population normalization is performed instead. ## Reference The development and inner workings of these algorithms are reported in: *Brunius C, Shi L, Landberg R, 2016. Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction. Metabolomics 12:173, doi: 10.1007/s11306-016-1124-4*