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Microarray Based Gene Expression Analysis using R Programming
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00
Aim: The aim of the “Microarray Data Analysis using R” workshop is to equip participants with the practical skills and knowledge required to proficiently analyze microarray data using the R programming language, enabling them to uncover meaningful insights, identify differentially expressed genes, and effectively contribute to genomics research and data-driven decision-making in both academic and professional settings.
Aim
Microarray Based Gene Expression Analysis using R Programming trains participants to analyze and interpret microarray gene expression data using R and Bioconductor. You will learn the end-to-end workflow—data import, quality control, normalization, differential expression, annotation, visualization, and functional interpretation—so you can produce reproducible results and publication-ready figures from microarray studies.
Program Objectives
- Understand Microarray Concepts: Platforms, probes, signal intensities, and experimental design basics.
- Build an R/Bioconductor Workflow: Data structures, packages, and reproducible project setup.
- Perform QC & Normalization: Identify outliers/batch effects and normalize arrays correctly.
- Differential Expression Analysis: Use linear modeling approaches and interpret logFC/FDR responsibly.
- Annotate & Visualize Results: Map probes to genes and generate heatmaps, volcano plots, and PCA.
- Functional Interpretation: Pathway/GO enrichment concepts and careful biological interpretation.
- Communicate Results: Create a reproducible report with methods, plots, and results tables.
- Hands-on Application: Complete a capstone analysis on a real or curated microarray dataset.
Program Structure
Module 1: Microarray Fundamentals & Study Design
- Microarray workflows: probes, hybridization, scanning, intensity data.
- Study design basics: groups, replicates, controls, and confounders.
- Common sources of noise: batch effects, labeling bias, sample quality.
- What microarrays can/can’t tell you compared to RNA-seq (balanced overview).
Module 2: R + Bioconductor Setup for Microarrays
- Project structure: raw data, metadata, scripts, outputs, reports.
- Bioconductor essentials: ExpressionSet and basic data containers.
- Key packages (workflow view): limma, affy/oligo (platform-dependent), annotation packages.
- Reproducibility basics: session info, scripts vs notebooks, clean exports.
Module 3: Data Import & Metadata Management
- Importing common formats: CEL files (Affymetrix) and processed matrices (overview).
- Building sample metadata tables: conditions, batches, covariates.
- Initial summaries: intensity distributions and sample-level diagnostics.
- Data hygiene: consistent sample naming and factor handling.
Module 4: Quality Control (QC) & Preprocessing
- QC plots: boxplots, density plots, MA plots, PCA/MDS concepts.
- Outlier detection and decision rules (documenting why samples are removed).
- Batch effects: recognizing patterns and when correction may be needed (intro).
- Preprocessing choices: background correction and filtering low-signal probes.
Module 5: Normalization Strategies
- Why normalization matters: making arrays comparable.
- Common methods: quantile normalization and platform-specific preprocessing (conceptual).
- Post-normalization checks: “did it work?” validation plots.
- Transformations and summarization: log2 scaling and probe summarization concepts.
Module 6: Differential Expression with limma
- Design matrix and contrasts: translating biology into statistical models.
- Linear modeling workflow: fit → contrasts → empirical Bayes moderation.
- Interpreting results: logFC, p-values, adjusted p-values (FDR).
- Common pitfalls: low replicates, confounding, and over-interpretation.
Module 7: Annotation, Visualization & Results Packaging
- Probe-to-gene mapping: annotation packages and ID types (gene symbol/Entrez/Ensembl).
- Key plots: volcano plot, heatmap, PCA/MDS, top-table summaries.
- Building ranked gene lists and exporting results tables for publications.
- Good figure practice: labeling, thresholds, and transparency.
Module 8: Functional Interpretation (GO/Pathway Concepts)
- From genes to biology: enrichment analysis basics (conceptual workflow).
- Over-representation vs ranked-list methods (high-level differences).
- Interpreting pathway outputs: avoiding “story inflation” and confirming evidence.
- Reporting limitations and validation suggestions (qPCR, external datasets).
Final Project
- Analyze a microarray dataset with provided metadata (case/control or multi-group).
- Perform QC → normalization → differential expression → visualization.
- Generate a final report and results package (tables + figures + methods summary).
- Deliverables: reproducible R script/notebook + report (HTML/PDF) + results folder.
Participant Eligibility
- UG/PG students in Biotechnology, Bioinformatics, Genetics, Life Sciences
- PhD scholars and researchers working with gene expression datasets
- Faculty and lab members aiming to interpret microarray datasets independently
- Professionals transitioning into bioinformatics and omics analytics
- Basic familiarity with biology + willingness to learn R (prior coding helpful but not required)
Program Outcomes
- End-to-End Workflow Skill: Ability to run complete microarray analysis pipelines in R.
- QC & Normalization Confidence: Ability to detect outliers/batch effects and choose preprocessing methods.
- Differential Expression Readiness: Ability to build limma models and interpret DE results responsibly.
- Visualization & Reporting: Ability to create publication-ready plots and a reproducible analysis report.
- Portfolio Deliverable: A completed microarray analysis project you can showcase academically or professionally.
Program Deliverables
- Access to e-LMS: Full access to lessons, datasets, and code templates.
- Microarray Toolkit Pack: Metadata template, QC checklist, limma design/contrast templates.
- Plot Templates: PCA/MDS, volcano, heatmap, and result summary plotting scripts.
- Report Template: RMarkdown/Quarto report skeleton for microarray studies.
- Hands-on Project Support: Guided feedback on capstone analysis and interpretation.
- Final Assessment: Certification after assignments + capstone submission.
- e-Certification and e-Marksheet: Digital credentials provided upon successful completion.
Future Career Prospects
- Bioinformatics Analyst (Gene Expression) – Entry-level
- Omics Data Analyst / Research Data Associate
- Biostatistics + Transcriptomics Research Assistant
- Academic/Industry Research Associate (Expression Analytics)
- Bioconductor/R Analyst for Life Sciences Teams
Job Opportunities
- Universities & Research Institutes: Transcriptomics projects, microarray re-analysis, reproducible reporting.
- Biotech/Pharma R&D: Biomarker discovery support, expression profiling studies, documentation.
- CROs & Bioinformatics Service Providers: Microarray pipeline execution, QC, DE results delivery.
- Hospitals & Clinical Research Units: Translational expression studies and reporting support.
- HealthTech & Analytics Firms: Multi-omics support and evidence-generation teams.
| Category | E-LMS, E-LMS+Video, E-LMS+Video+Live Lectures |
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