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Microarray Based Gene Expression Analysis using R Programming

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.

SKU: NSTC0015 Category: Tags: ,

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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What You’ll Gain

  • Full access to e-LMS
  • Publication opportunity
  • Self-assessment & final exam
  • e-Certificate

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