Crash Course Recording Available All Levels Dr. Omics

scRNA seq Data Analysis

Unlock cellular heterogeneity with advanced single-cell RNA transcriptomics pipelines. Master high-throughput genomic data analysis using machine learning and bioinformatics workflows.

  • 5.0/5
  • English
  • Updated Sep 2026
scRNA seq Data Analysis

About this course

In modern genomic research, understanding tissue complexity at single-cell resolution is pivotal for groundbreaking scientific discoveries. This intensive online crash course, presented by Dr. Omics Edu and highlighted in scRNASeq.jpg, is meticulously designed to master scRNA-seq Data Analysis. Participants will explore the entire lifecycle of transcriptomic data, transitioning from raw sequencing reads to deep biological insights. Throughout this program, you will navigate quality control filtering, normalization, and dimensionality reduction techniques essential for handling high-throughput datasets. The curriculum integrates traditional statistical approaches with cutting-edge machine learning algorithms to accurately cluster distinct cell populations and track cellular trajectories. By working hands-on with realistic bioinformatics pipelines, you will learn to uncover rare cell types and decode complex disease mechanisms. Ultimately, this course bridges the gap between raw data and actionable biological discovery, empowering life science researchers to lead the future of precision medicine.

What you will learn

The fundamental mechanics of single-cell isolation, library preparation, and high-throughput sequencing technologies.
How to construct and implement an automated quality control pipeline for filtering low-quality cell data.
Methods for executing dimensionality reduction techniques, including PCA, t-SNE, and UMAP.
Strategies to apply unsupervised machine learning algorithms for robust cell clustering and cell-type annotation.
Techniques for identifying differentially expressed genes (DEGs) and mapping complex cellular trajectories.

Skills you will gain

Transcriptomics Seurat Clustering Bioinformatics Genomics Preprocessing Programming Visualization Automation
Certification

Available

Issued by Dr. Omics

Course curriculum

1 module

  • 1. Introduction to Single-Cell Omics
  • 2. Package installation
  • 3. Single-Cell RNA-Seq Analysis Workflow
  • 4. Quality Control and Preprocessing of scRNA Data
  • 5. Normalization and Batch Effect Correction
  • 6. Cell Clustering and Type Identification
  • 7. Dimensionality Reduction and Data Visualization
  • 8. Trajectory and Pseudotime Analysis
  • 9. Cell-Cell Communication and Interaction Mapping

What you need to start

  • Foundational knowledge of molecular biology, genetics, or basic transcriptomic concepts.
  • Elementary familiarity with data analysis concepts or basic programming logic (prior experience with R or Python is helpful but not strictly required).

Who this course is for

  • Bioinformaticians and Computational Biologists eager to add advanced single-cell pipelines to their analytical toolkits.
  • Wet-Lab Life Scientists wanting to independently analyze their own high-throughput sequencing datasets without relying on third-party core facilities.
  • Genomics Researchers aiming to utilize artificial intelligence and data science tools to resolve cellular heterogeneity in complex tissues.
  • Postgraduate Students and Postdocs looking to master data-driven transcriptomics to elevate their academic publications and research impact.
INR

₹4999

₹10000 50% off
USD

$70

$120 42% off

Indian learners pay in INR; international learners are billed in USD.

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This course includes
  • Format Recording Available
  • Level All Levels
  • Language English
  • Modules 1
  • Access 3 months
  • Certificate Yes
  • Provider Dr. Omics
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