Journal Club
Fall 2026 Series
The UFHCC BCB-SR Bioinformatics and Computational Biology Journal Club is running a lightning-style format this fall. Each meeting features short presentations on two papers: a technical paper covering a new method (e.g. benchmarking studies) and an applications paper (e.g. an innovative approach developed and applied to an important cancer research question).
To join our email distribution list for updates, newsletters, and more, email UFHCC-BCB-SR@ad.ufl.edu.
September 11, 2026 — First Meeting
Technical paper:
To Be Announced. Check back soon!
Applications paper:
Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer — Azimzade et al., Nature Communications, May 2026
Applies an explainable machine learning pipeline (SHAP values derived from random survival forests and survival SVMs) to deconvolved cell-type fractions across roughly 5,000 METABRIC and TCGA samples, producing a "Survival Score" for each cell type. The analysis surfaces a dichotomy in which macrophages track positively with chemotherapy response but negatively with relapse-free survival in ER+ and luminal subtypes. The authors then integrate imaging mass cytometry and scRNA-seq data to link a specific HLA-ABC-high macrophage population to immunosuppressive niches and candidate interaction targets.
Archive
Materials from past series and standing topic collections.
Spatial Transcriptomics Journal Club
Previously hosted monthly by the UFHCC BCB-SR.
Power Analysis Topic
General Power Analysis:
Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives
ST Power Analysis:
PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions
scRNA-seq Power Analysis: - Maximizing statistical power to detect differentially abundant cell states with scPOST - scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Bulk RNA-seq Power Analysis:
Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments
Discussion Questions:
Discussion Questions - Structured questions to guide journal club conversations
Slides:
Presentation Slides
Xenium Data Analysis
Presentation by Dr. Jeff Bylund, Senior Science & Technology Advisor, 10X Genomics
Presentation Recording:
Recording - only accessible when logged into Dropbox with UF e-mail
Papers from the Presentation:
Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows
Cell segmentation-free inference of cell types from in situ transcriptomics data
Review Papers
Comprehensive reviews that provide foundational knowledge and current state-of-the-art in key bioinformatics areas
Chromatin accessibility profiling methods
Benchmarking Papers
Benchmarking papers compare methods to determine, based on a set of parameters and often empirical and simulated data, what the "best" tool is for a particular analysis. Tools are evaluated on speed and accuracy, usually. (Keep in mind these papers are often published to introduce a new tool, and therefore may be biased.)
- Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data
- Benchmarking scRNA-seq copy number variation callers
Reproducibility
Understanding the importance of and tools for reproducibility is critical for bioinformatics work.
The five pillars of computational reproducibility: bioinformatics and beyond
Multi-omics
Integrating diverse biological data types (genomics, transcriptomics, proteomics, metabolomics) to gain comprehensive insights into complex biological systems
Multi-Omics Data Integration in Cancer Research
Eye on Relevant New Methods
Recently published methods and tools that represent significant advances or novel approaches in computational biology
- hUSI is a robust transcriptome-based cellular senescence prediction tool
- Novel cancer subtyping method guided by tumor-normal sample in latent space of transcriptomic variational autoencoder