Single-Cell Differential Expression Interpretation & Insight

Learn how to analyze & interpret differential expression from published studies, with Seurat workflows in R or no-code

What you’ll learn
Discover cellxgene to find annotated single-cell datasets for analysis.
Run differential expression analyses using R (for coders) or using CytoTalk app (for no-code workflows).
Gain full control over FindMarkers function to compare any cell groups.
Interpret differential expression results with confidence, understanding thresholds and pitfalls.
Learn how published studies present differential expression results and turn them into a scientific story.

Requirements
Basic understanding of single-cell RNA-seq concepts (introductory material not covered).
For non-coders: Installation of Docker software, with step-by-step guidance provided in Lecture 3 and the Additional Readings of Section 1 (preview available).
For R coders: Basic familiarity with R and RStudio and be comfortable installing the required packages listed in the Additional Readings of Section 1 (preview available).

Description
*This course contains the use of artificial intelligence.*While many researchers can run single-cell analyses, few can confidently translate complex data into the clear, compelling insights suitable for publication. Whether you are a coder or a non-coder, this course teaches you not only how to execute differential gene expression analyses, but also to interpret them through guided walkthroughs of real published papers.You will learn how to critically justify and present your differential gene expression results by following how authors visualize findings in figures, justify methods, build biological narratives in the results and discussion sections, and respond to selected reviewer comments.The Practical Workflow:We begin with cell type-annotated datasets obtained from published studies available through CELLxGENE. You will learn how to design meaningful gene expression comparisons and how similar analyses are interpreted in the literature. You will also see how revisiting published single-cell datasets can reveal novel biological insights and inspire new research questions.Two Hands-On Learning Paths:R users: Perform the analysis using Seurat code.Non-coders: Reproduce the same analyses using the CytoTalk Explorer Lite app. Step-by-step guidance is provided for installing Docker Desktop and running the app locally.Expert Instruction:All content and examples were designed by Dr. Ahmed S. Abouhashem, Research Assistant Professor at the University of Pittsburgh and founder of CytoTalk LLC, based on real analyses from published single-cell studies. High-quality AI voice-over narration is used for clarity and an enhanced learning experience.By the end of this course, you won’t just run an analysis. You will learn how published studies communicate their findings, framing results into a coherent story with the clarity and confidence required for high-impact manuscripts and scientific presentations.

Biologists, biomedical researchers, and clinicians who want to analyze and interpret single-cell RNA-seq results without coding.,Graduate students, postdocs and trainees seeking a strong conceptual foundation in differential expression analysis using real annotated datasets.,Researchers reading single-cell studies who want to understand how differential expression methods, figures, and supplementary tables are generated and reported.