• Home
  • /
  • Course
  • /
  • Microarray Based Gene Expression Analysis using R Programming
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

Reviews

There are no reviews yet.

Be the first to review “Microarray Based Gene Expression Analysis using R Programming”

Your email address will not be published. Required fields are marked *

Related products

Certificate Image

What You’ll Gain

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

All Live Workshops

AI Workshop

AI for Ecosystem Intelligence, Biodiversity Monitoring & Restoration Planning
AI for Ecosystem Intelligence, Biodiversity Monitoring & Restoration Planning

AI Workshop

Blockchain for Supply Chain: Smart Contract Development & Security Auditing
Blockchain for Supply Chain: Smart Contract Development & Security Auditing
View More

Feedbacks

Scientific Paper Writing: Tools and AI for Efficient and Effective Research Communication

Mam explained very well but since for me its the first time to know about these softwares and More journal papers littile bit difficult I found at first. Then after familiarising with Journal papers and writing it .Mentors guidance found most useful.
DEEPIKA R : 06/10/2024 at 10:48 am

In Silico Molecular Modeling and Docking in Drug Development

Thank you for good lecture


Aleksandra Kuliga : 02/15/2024 at 2:35 pm

The lectures were very insightful and valuable. I think the Mentor has a very good scientific More background to give this workshop. He’s very competent in knowledge.
Gabriel Murillo Morales : 04/10/2025 at 11:52 pm

This was a good workshop some of the recommended apps are not compatible with MAC based computers. More would recommend to update the recommendations.
Shahid Karim : 10/09/2024 at 3:14 pm

Scientific Paper Writing: Tools and AI for Efficient and Effective Research Communication

Excellent delivery of course material. Although, we would have benefited from more time to practice More with the plethora of presented resources.
Kevin Muwonge : 04/02/2024 at 10:08 pm

Good


Sradha A S : 04/14/2025 at 8:04 pm

Predicting 3D Structures of Proteins and Nucleic Acids

I sincerely appreciate the mentor’s clear and engaging way of explaining complex concepts related to More 3D structure prediction. The session was a bit unorganized due to his technical issue of device other than that it was greatly informative
Chanika Mandal : 05/20/2025 at 9:28 pm

In Silico Molecular Modeling and Docking in Drug Development

Very well structured and presented lectures.


Iva Valkova : 04/11/2024 at 12:03 pm