Get up to speed in single cell data analysis with this free, online, beginner-friendly bioinformatics training for single cell data analysis. No bioinformatics background required.
To help single cell sequencing users without a bioinformatics background build their own data analysis capability, Singleron Biotechnologies is offering a free, digital bioinformatics beginner training for single cell data analysis.
You’ll learn essential command-line operations, file system navigation, and scripting techniques that are crucial for handling large biological datasets efficiently.
Additionally, you’ll explore Celescope, a specialized tool designed for single-cell RNA sequencing (scRNA-seq) analysis.
By the end of this step, you’ll be equipped with the skills to manage bioinformatics workflows using Linux and leverage Celescope for advanced data interpretation.
This step introduces you to R, one of the most widely used programming languages in bioinformatics. You’ll start with the fundamentals—data structures, functions, and visualization techniques—laying the groundwork for effective biological data analysis.
Then, you’ll dive into single-cell data exploration, learning how to process, visualize, and interpret single-cell RNA sequencing (scRNA-seq) datasets.
By mastering key packages like Seurat and ggplot2, you’ll be able to uncover valuable insights into cellular heterogeneity, gene expression patterns, and biological processes.
In this step, you’ll learn how to assign biological identities to individual cells within single-cell RNA sequencing (scRNA-seq) datasets. Through marker gene analysis, reference datasets, and computational tools like Seurat and SingleR, you’ll classify cells into distinct types based on their gene expression profiles.
Understanding cell type annotation is crucial for identifying cellular heterogeneity, discovering novel cell populations, and interpreting biological processes within complex tissues.
By the end of this module, you’ll be equipped with the skills to refine and validate cell type assignments with confidence.
In this module, you’ll explore how cells transition from one state to another, mapping their developmental pathways and understanding cellular differentiation. Using computational tools like Monocle and Slingshot, you’ll learn to construct pseudotime trajectories that reveal dynamic changes in gene expression over time.
By analyzing cellular progression within single-cell RNA sequencing (scRNA-seq) data, you’ll gain insights into biological processes such as stem cell development, disease progression, and immune responses.
By the end of this step, you’ll be equipped with techniques to model and visualize cell fate decisions.
This step dives into the intricate signaling networks that cells use to interact with one another. You’ll explore methods for analyzing cell-cell communication in single-cell RNA sequencing (scRNA-seq) data, using computational tools like CellChat and NicheNet to infer ligand-receptor interactions and predict downstream signaling effects.
Understanding cell-cell communication is essential for uncovering how cells coordinate responses in development, immune functions, and disease mechanisms.
By the end of this module, you’ll be equipped with strategies to map signaling pathways and interpret their biological significance.
This module focuses on identifying biological themes and pathways enriched within gene expression datasets. You’ll learn how to use computational tools like GOseq, GSEA (Gene Set Enrichment Analysis), and clusterProfiler to determine which functional categories are overrepresented in differentially expressed genes.
By analyzing gene ontology (GO) terms, pathway databases, and curated gene sets, you’ll gain insights into the biological significance of your data—whether it’s revealing disease mechanisms, cellular responses, or functional networks.
By the end of this step, you’ll be able to perform enrichment analysis with confidence and interpret meaningful results.
"*" indicates required fields