[2026-04-10] TRIPOD journal club

For more information on Manisha Barse, check https://bsky.app/profile/mbarse.bsky..... For more information about the materials discussed in this video, check https://doi.org/10.1016/j.cels.2022.0.... For more information about the LIBD rstats club, check https://bsky.app/profile/libdrstats.b.... Summary: 🧬 TRIPOD: Understanding Gene Regulation with Single-Cell Multi-Omics Data 📊 This video presents TRIPOD, a non-parametric statistical framework for uncovering regulatory relationships between *transcription factors (TFs)**, **genes**, and **cis-regulatory regions (peaks)* using single-cell RNA and ATAC multi-omic data. 🔬 Key Concepts Covered: 🧪 How gene expression, chromatin accessibility, and TF activity work together in cells 📈 Why combining TF + peak information improves gene expression prediction vs. using peaks alone 🔗 The shift from *pairwise correlations* to *trio relationships* (TF-peak-gene) ⚖️ *Conditional testing* vs. marginal correlations — and why standard approaches can miss cell-type-specific regulation 🛠️ TRIPOD Methodology: 🎯 **Level 1**: Tests conditional associations by matching on TF expression OR peak accessibility 🎯 **Level 2**: Examines interaction effects using partial residuals 🔄 Non-parametric matching reduces cell-type confounding without assuming linearity 📌 Real-World Example: 🩸 PBMC dataset analysis showing LEF1 → CCR7 regulation 🔴 Identified B and T cells as key cell types where this regulatory trio is active ✅ Validated with chromVAR motif accessibility scores 🎓 Perfect for researchers working with single-cell multi-omics, gene regulatory networks, and computational biology! #SingleCell #Multiomics #Bioinformatics #GeneRegulation #TranscriptionFactors #ATAC #scRNAseq #ComputationalBiology #Genomics