At AIMS@USC, dissemination of methods is as important as their development. Advances in spatial analysis only achieve impact when they are transparent, reproducible, and accessible to the broader community—especially investigators who are just beginning to work with spatial datasets. We are committed to lowering the barrier to entry by sharing well-documented tutorials, open code, and practical workflows that translate cutting-edge methodology into usable tools. For researchers getting started in spatial analysis, clear guidance can dramatically accelerate learning and prevent common analytical pitfalls. On this page, you will find resources we have developed to streamline adoption of new approaches, promote best practices, and ensure that innovations emerging from AIMS@USC rapidly benefit the wider USC research community.
This guide walks through the process of installing R and RStudio, the core tools used for data analysis in bioinformatics and beyond. It provides step-by-step instructions for setting up R across operating systems and introduces RStudio as a user-friendly interface for writing and running code.
Designed for beginners, this section also orients users to the RStudio workspace and its key components. The goal is to ensure a smooth setup experience and prepare learners to begin working with data in R with confidence.
irisThis tutorial introduces foundational data analysis skills in R using
the classic iris dataset as a practical, hands-on example.
Designed for beginners, it walks through essential workflows including
data inspection, transformation with dplyr, and
visualization with ggplot2.
By building from simple summaries to more advanced plots and statistical tests, this guide emphasizes how different tools in R connect within a complete analytical pipeline. The goal is to provide learners with both the technical skills and conceptual understanding needed to explore, visualize, and interpret data effectively.
Table 1This tutorial walks through the process of transforming raw clinical chart review data into a polished “Table 1” summary using R. Designed for beginners and clinical researchers, it demonstrates how to clean and structure real-world data, derive meaningful variables, and generate publication-ready summary tables.
Using packages such as dplyr, gtsummary,
and gt, the guide covers key steps including data
wrangling, handling missing values, grouping variables, and summarizing
patient characteristics across cohorts. It also introduces best
practices for formatting and presenting results in a clear, reproducible
way.
By progressing from raw data to a finalized Table 1, this tutorial highlights how different tools in R integrate into a cohesive workflow. The goal is to equip users with the practical skills and conceptual framework needed to efficiently summarize clinical data for manuscripts, abstracts, and reports.
Spatial transcriptomics bridges molecular profiling with tissue architecture, enabling researchers to study gene expression within its native biological context. This section provides core resources to understand the principles, platforms, and analytical considerations that underpin spatial technologies. Our goal is to equip investigators with both the theoretical framework and practical tools needed to interpret spatial data meaningfully.
Designed as a practical onboarding guide, this tutorial walks users through the structure, processing, and exploration of Xenium datasets. From data import to visualization and quality control, it provides a step-by-step roadmap to confidently begin working with high-resolution spatial transcriptomic data.
Accurate cell type annotation is central to biological interpretation. This guide outlines strategies for annotating Xenium datasets using marker-based approaches, reference mapping, and integrative methods. It highlights common challenges and emphasizes reproducible workflows to ensure robust and defensible cell type assignments.
Spatial datasets are more than expression matrices—they encode physical relationships, tissue architecture, and cellular neighborhoods. This resource focuses on incorporating spatial coordinates into analysis, including neighborhood modeling, spatial statistics, and interaction inference. The aim is to move beyond clustering and fully leverage the contextual information that defines spatial biology.
Spatial data are often first explored in tools like Xenium Explorer, where cell boundaries, gene expression, and tissue organization can be viewed interactively. This guide focuses on moving those outputs into ggplot, where they can be reshaped, customized, and combined into publication-ready figures. The goal is to preserve the spatial context of the data while gaining much more flexibility in how it is visualized and shared.