Automation of (Biological) Data Analysis and Report Generation
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The document outlines a framework for automating biological data analysis and report generation by integrating human and bot interactions to facilitate workflows, data parsing, and statistical analysis. It discusses various tasks such as visualization, exploratory analysis, and predictive modeling, while highlighting challenges like data cleaning and quality validation. The aim is to create robust analyses and reports through intelligent automation and workflow integration.
Automation of (Biological) Data Analysis and Report Generation
- 1. Automation of Biological Data Analysis and Report Generation Dmitry Grapov, PhD
- 2. Bots write the darndest things http://www.latimes.com/local/lanow/earthquake-27-quake-strikes-near-westwood- california-rdivor,0,3229825.story#axzz2wQwc82EK •fill in the template (easy) •human-guided automation (e.g. Metaboanalyst, intermediate) •intelligent/reactive writing (e.g. ~AI, advanced) http://narrativescience.com/
- 3. Humans + Bots Interaction: •Bots and humans combine in guided analyses •Humans: make choices (based on bot guides) •Bots: automate! Facilitate: • workflow logging and template creation •reproducible results Bot: Initial data and meta data parsing and quality validation (need: template input) Human: data cleaning and experimental design identification (use: multiple choice, dynamic GUI) Bot: instantiation of complex workflows Human: overview of bot assumptions and results Bot: Numerical and text output generation
- 4. Humans + Bots write darndender things? Choose Your Own Life Adventure! ? https://github.com/ dgrapov/AdventureR
- 5. Data Analysis Tasks Visualization (how does it look?) • histograms, density plots, box plots, line plots, scatter plots, networks, etc. Statistical Analysis (what is statistically significant?) • summary tables, ANOVA, FDR adjustment, power analysis, etc. Exploration (what are the major patterns/trends?) • clustering, PCA, ICA, etc. Predictive Modeling (what explains my hypothesis?) • mixed effects, partial least squares (O-/PLS/-DA), etc. Network Analysis and Mapping (how are things related?) • Functional analysis: pathway enrichment or overrepresentation • Networks: biochemical, structural, mass spectral and empirical networks • Mapping: projection of analysis results onto network
- 6. WCMC Data Analysis Reports ™ Statistical analysis Clustering PCA O-PLS-DA Biochemical enrichment Network mapping Input template: BinBase •inference of experimental goals from sample meta data •mapping variables to external databases Tasks: Report: Tools:
- 7. Automation Challenges Data cleaning and quality validation •use: quality control samples; identify: precision/accuracy, normalization, batch corrections; mitigate: outliers, missing values, batch effects, etc. Identification of experimental goals •use: meta data, identify: main and accessory effects; choose: statistics, multivariate tests and visualizations Integration of multiple tasks to evolve robust analyses •tasks: statistics, multivariate, functional, networks, database mapping, etc Data analysis report generation •use: R, Latex, markdown ?
- 8. Challenges to automated metabolite ID mapping Stereochemistry? Search: catechin Best Match: Catechin Biologically relevant: D-catechin Synonyms? Search: UDP GlcNAc FAIL: UDP GlcNac PASS: UDP-GlcNac
- 9. Strategies for automated metabolite ID mapping (from synonym) #1: CTS+ #2: Web query #3: Curated DB •Use CTS to translate from synonyms to KEGG (KID) and PubChem (CID) •Use KEGGREST and PUG to filter and choose most appropriate IDs •Use fuzzy matching and word similarity metrics (e.g. Damerau– Levenshtein distance) •Use KEGGREST + PubChem PUG to translate synonyms to IDs •For KEGG ID: synonym SID KID •Generate a curated DB for KEGG and CID translations + •Include InChI Keys •Map to other DBs •Allow fuzzy matching on synonyms •e.g. IDEOM http://bioinformatics.oxfordjournals.org/content /early/2012/02/04/bioinformatics.bts069
- 10. Interactive Analysis and Report Generation knitr (http://yihui.name/knitr/) Analysis Report Generation •Analysis on rails or open sandbox •Humans facilitate robust results generation + Bots ensure reproduction •Generation of Methods and Results should be automateable
- 11. Devium 2.0 Human-guided automated data analysis and report generator Human-guided automation could help ensure robust results by making choices which are otherwise difficult to automate. https://github.com/dgrapov/DeviumWeb
- 12. MetaMapR Linking data analysis and biology https://github.com/dgrapov/MetaMapR Integration of complex work flows is key to automation.
- 13. + Workflows for complex experiments (e.g. time-course) + Biochemical functional analysis (pathway enrichment) + GUI for report generation (Devium 2.0) + Integrate multi-’Omic’ data sets (MetaMapR 2.0) + Scientific literature mining (RapportR) + Interactive plots and networks (JavaScript) Future Goals
- 14. dgrapov@ucdavis.edu metabolomics.ucdavis.edu This research was supported in part by NIH 1 U24 DK097154