Metabolomic Data Analysis Using MetaboAnalyst

This is the fifth module in the 2016 Informatics and Statistics for Metabolomics workshop hosted by the Canadian Bioinformatics Workshops.
This lecture is by David Wishart from the University of Alberta How it Begins by Kevin MacLeod is licensed under a Creative Commons Attribution license Source: Artist: Table of Contents: 00:10 - 00:56 - Learning Objectives 01:15 - A Typical Metabolomics Experiment 03:16 - 2 Routes to Metabolomics 03:31 - Metabolomics Data Workflow 05:11 - Data IntegrityQuality 06:46 - DataSpectral Alignment 07:28 - Binning 3000 pts to 14 bins 07:53 - Data NormalizationScaling 09:40 - Data NormalizationScaling 10:20 - Data QC Outlier Removal Data Reduction 12:08 - MetaboAnalyst 13:25 - MetaboAnalyst History 14:37 - MetaboAnalyst Overview 16:00 - MetaboAnalyst Modules 17:00 - MetaboAnalyst Modules 18:03 - Example Datasets 19:09 - Example Datasets 19:34 - Metabolomic Data Processing 19:44 - Example Datasets 19:45 - Example Datasets 19:45 - MetaboAnalyst Modules 19:48 - Example Datasets 19:48 - Example Datasets 19:49 - Metabolomic Data Processing 19:51 - Common Tasks 20:38 - Select a Module Statistical Analysis 20:48 - Common Tasks 20:49 - Metabolomic Data Processing 20:49 - Example Datasets 20:49 - Example Datasets 20:55 - Example Datasets 20:55 - Metabolomic Data Processing 20:56 - Common Tasks 20:56 - Select a Module Statistical Analysis 21:07 - Data Upload 21:37 - Alternatively 22:18 - Data Set Selected 23:24 - Data Integrity Check 24:11 - Data Normalization 25:51 - Data Normalization 26:28 - Data Normalization 27:40 - Normalization Result 29:49 - Data Normalization 29:59 - Normalization Result 30:03 - Data Normalization 30:14 - Normalization Result 30:15 - Data Normalization 30:15 - Data Normalization 30:16 - Data Normalization 30:21 - Data Normalization 30:21 - Data Normalization 30:36 - Normalization Result 31:24 - Data Normalization 31:26 - Next Steps 31:37 - Quality Control 32:41 - Visual Inspection 33:36 - Outlier Removal Data Editor 34:01 - Noise Reduction Data Filtering 35:09 - Noise Reduction cont. 35:36 - Data Reduction and Statistical Analysis 36:07 - Common Tasks 36:30 - 37:00 - ANOVA 37:31 - ANOVA 38:58 - View Individual Compounds 39:46 - Whats Next? 40:06 - Overall Correlation Pattern 41:20 - High Resolution Image 41:53 - Whats Next? 42:40 - Pattern Matching 43:29 - Pattern Matching cont.
44:57 - 45:16 - Pattern Matching cont.
45:17 - Pattern Matching 45:32 - Pattern Matching cont.
45:34 - 45:55 - Multivariate Analysis 46:58 - PCA Scores Plot 47:40 - PCA Loading Plot 47:58 - PCA Scores Plot 48:15 - PCA Loading Plot 49:14 - 3D Score Plot 50:28 - 51:06 - 3D Score Plot 51:07 - PCA Loading Plot 51:08 - PCA Scores Plot 51:46 - PCA Loading Plot 51:47 - 3D Score Plot 51:49 - 52:30 - Multivariate Analysis 52:54 - PLS-DA Score Plot 53:23 - Evaluation of PLS-DA Model 55:19 - Important Compounds 57:07 - Model Validation 58:07 - 58:16 - Hierarchical Clustering Heat Maps 58:34 - Heatmap Visualization 58:48 - Heatmap Visualization cont. 59:16 - Whats Next? 59:28 - Heatmap Visualization cont. 59:29 - Heatmap Visualization 59:32 - Heatmap Visualization cont. 01:00:18 - Whats Next? 01:02:45 - Download Results 01:02:56 - Analysis Report 01:03:21 - Select a Module Enrichment Analysis 01:03:29 - Metabolite Set Enrichment Analysis MSEA 01:04:18 - Enrichment Analysis 01:04:56 - MSEA 01:05:09 - The MSEA Approach 01:05:18 - Data Set Selected 01:06:02 - Start with a Compound List for ORA 01:06:10 - Upload Compound List 01:06:38 - Perform Compound Name Standardization 01:06:55 - Name Standardization cont. 01:07:11 - Select a Metabolite Set Library 01:07:46 - Result 01:08:39 - Result cont. 01:09:04 - The Matched Metabolite Set 01:09:18 - Phenylalanine and Tyrosine Metabolism in SMPDB 01:09:42 - Single Sample Profiling SSP Basically used by a physician to analyze a patient 01:10:14 - Concentration Comparison 01:11:13 - Quantitative Enrichment Analysis QEA 01:11:30 - Result 01:11:47 - The Matched Metabolite Set 01:12:02 - Select a Module Pathway Analysis 01:12:09 - Pathway Analysis 01:12:57 - Data Set Selected 01:13:03 - Pathway Analysis Module 01:13:09 - Data Upload 01:13:22 - Perform Data Normalization 01:13:38 - Select Pathway Libraries 01:13:54 - Perform Network Topology Analysis 01:14:04 - Pathway Position Matters 01:14:44 - Which Node is More Important? 01:14:54 - Pathway Position Matters 01:14:56 - Which Node is More Important? 01:15:01 - Pathway Visualization 01:17:15 - Pathway Visualization cont. 01:17:28 - Pathway Impact 01:17:43 - Result 01:17:54 - Select a Module Biomarker Analysis 01:18:58 - Biomarker Analysis 01:19:37 - Select Test Data Set 1 01:19:42 - Data Set Selected 01:20:34 - Perform Data Integrity Check