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I record myself coding in the R programming language to perform an exploratory bioinformatics analysis of multi-cellular pixel resolution spatial transcriptomics Visium dataset from 10X Genomics using STdeconvolve with SpotClean preprocessing. I hope this will help give students a sense for how I would go about doing an exploratory bioinformatics data analysis, the kinds of challenges I often face and how I try to address them, and the types of 'sanity checks' I like to perform along the way. Some potentially helpful moments: - bypassing R version requirements when installing R packages: • Live R Coding Session - Spatial trans... - running spot decontamination preprocessing with SpotClean: • Live R Coding Session - Spatial trans... - running cell-type deconvolution with STdeconvolve: • Live R Coding Session - Spatial trans... - debugging when the data does not look how you expect: • Live R Coding Session - Spatial trans... - tuning STdeconvolve cleanCounts() parameters: • Live R Coding Session - Spatial trans... - evaluating SpotClean results: • Live R Coding Session - Spatial trans... - running STdeconvolve with output from SpotClean: • Live R Coding Session - Spatial trans... - tuning STdeconvolve visualization parameters: • Live R Coding Session - Spatial trans... - comparing STdeconvolve results with and without SpotClean preprocessing: • Live R Coding Session - Spatial trans... - committing to Github: • Live R Coding Session - Spatial trans... To try it out for yourself, check out: - Code from this coding session: https://github.com/JEFworks/coding_vi... - STdeconvolve: https://jef.works/STdeconvolve/ - SpotClean: https://github.com/zijianni/SpotClean Please take my live commentary with a grain of salt ;) Here are some minor mistakes I've caught in the video: - the scaling factor converts into units of pixels, not microns