Journal Club Papers
Spatial Transcriptomics Journal Club
The UFHCC BCB-SR hosts a monthly spatial transcriptomics journal club.
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