[2026-05-29] Solving an elusive bug: process and lessons learned

For more information on Nick Eagles, check https://bsky.app/profile/nick-eagles..... For more information about the materials discussed in this video, check https://docs.google.com/presentation/.... For more information about the LIBD rstats club, check https://bsky.app/profile/libdrstats.b.... Summary: 🐛 Debugging a Tricky Data Analysis Bug | Spatial Transcriptomics Case Study Video Summary 🔬 **The Problem**: While analyzing HD spatial transcriptomics data comparing transcription inside vs. outside cells, unexpected results emerged - correlations were surprisingly low and spatial registration statistics seemed off. 🔍 **Investigation Journey**: 📊 Initial plots showed suspicious negative correlations where positive ones were expected 🧪 Pseudo-bulk PCA revealed an extreme batch effect by sample ❓ Confusingly, the clustering results looked visually correct! 💡 **The Culprit**: The `duckplyr` package! Unlike standard `dplyr`, it doesn't preserve row order after joins for performance reasons. When using `pull()` after a join, the cluster assignments got scrambled! ⚠️ **Key Lessons Learned**: 1. 🧠 Don't ignore your intuition - sometimes "unexpected" results are actually bugs 2. 📖 Read the documentation carefully - especially for package compatibility differences 3. 🔎 Question your assumptions when debugging 4. ✅ Always verify data alignment with visualization checks 🛠️ **Tools Discussed**: R, dplyr, duckplyr, spatial transcriptomics analysis, pseudo-bulking, PCA Perfect for data scientists, bioinformaticians, and anyone working with spatial -omics data! #DataScience #Bioinformatics #Debugging #RStats #SpatialTranscriptomics #CodingTips #DataAnalysis #FICTURE #spatialLIBD