Section 6 (analysis and visualization of lipidomics data)

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capture_6 LipidSig 2.0 OzFAD LipidOne Skyline Lipid Data Analyzer (LDA) Lipostar 2.0 MS-DIAL MZmine XCMS on-line LIPID MAPS Statistical Analysis Tools MetaboAnalyst 5.0
Data normalization/scaling Https://lipidsig.bioinfomics.org/FAQ/?FAQ13 No Yes Yes Normalization (single/multi standard, normalization factor, samples sum) Filtering, normalization (single/multi standard, normalization factor, samples sum/average, quality control, LOESS), scaling (autoscaling, pareto) Filtering by blank’s peaks, an internal standard based (for relative intensity-based sample comparison), quality control sample’s profiles based (for LOWESS method), total ion count based (for relative intensity-based sample comparison), scaling Various filtering options, normalization (reference feature) Yes Normalization, scaling Filtering, normalization (sample-specific, sum, median, PQN, pooled groups, reference feature), scaling (mean centring, auto, pareto, range)
Missing value estimation Link No Automatic imputation at upload: missing values are filled with a random value between 0.01 and 0.1 of the smallest non-zero value in the row. Yes Yes Min/2, NIPALS No Recursive feature finding (gap filling) Yes No No
Univariate methods Student’s t-test, Wilcoxon test, ANOVA Ttest T.test / ANOVA Yes No ANOVA, fold-change analysis, volcano plot analysis ANOVA, fold-change analysis ANOVA ANOVA, fold-change analysis ANOVA, fold-change analysis T-test, fold-change analysis, ANOVA, correlation heatmaps, pattern search, correlation networks
Multivariate methods PCA, PLS-DA, t-SNE, UMAP, Linear regression, Logistic regression, Random forest, SVM, Lasso, Ridge, ElasticNet, XGBoost No PCA, PLS-DA, sPLS-DA, oPLS Yes Yes PCA, CPCA, LDA, DPCA, PLS, PLS-DA, O-PLS, O-PLS-DA PCA, PLS, PLS-DA, O-PLS, O-PLS-DA PCA PCA PCA, LDA, O-PLS-DA/VIP analysis PCA, PLS-DA, sPLS-DA, orthoPLS-DA
Clustering and correlation Hierarchical clustering, Pearson and Spearman correlation No Hierarchical HeatMap, Dendrogram, K-Means Yes No Heatmap, K-means, bisecting K-means, trend analysis HCA Hierarchical, expectation-maximization, farthest-first, k-means No Cim_network.R, hclust Hierarchical (dendrogram, heatmap), partitional (K-means, SOM) clustering
Classification and feature selection Link No No No No No Yes No No No No