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.
We’ll begin with an introduction to Linux, followed by system configuration and common commands. Then, we’ll dive into Conda, an open-source environment management system primarily used in Python and R programming.
By the end of this step, you’ll have a comprehensive understanding of Linux fundamentals and practical tools for bioinformatics workflows.
In this session, we’ll explore CeleScope, a powerful bioinformatics pipeline designed for single-cell multi-omics analysis.
We’ll start with an introduction to single-cell RNA sequencing and CeleScope. Next, we’ll walk you through the installation process. Then, we’ll dive into the multi_rna analysis module and finally, we’ll explore each step of the CeleScope pipeline and how to interpret the QC report.
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.
By the end of this session, you’ll be equipped to write R code, manipulate data, and create compelling visualizations.
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.
By the end of this module, you’ll be equipped with the skills to refine and validate cell type assignments with confidence.
This session will guide you through the principles, tools, and workflows used to identify and annotate cell types from single-cell RNA sequencing data.
We’ll cover three main topics: an overview of cell types, methods for cell type identification, and practical annotation operations. Each section builds toward a comprehensive understanding of how to accurately label cells in your dataset.
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.
By the end of this step, you’ll be able to perform enrichment analysis with confidence and interpret meaningful results.
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.
By the end of this module, you’ll be equipped with strategies to map signaling pathways and interpret their biological significance.
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 data, you’ll gain insights into biological processes such as stem cell development, disease progression, and immune responses.
"*" indicates required fields