Скачать с ютуб Live R Coding Session - normalizing spatial transcriptomics data for clustering vs deconvolution в хорошем качестве

Live R Coding Session - normalizing spatial transcriptomics data for clustering vs deconvolution

We recently developed a computational method for analyzing multi-cellular pixel-resolution spatial transcriptomics (ST) data called `STdeconvolve` that recovers the proportion of cell types comprising each multi-cellular spatially resolved pixel along with each cell types' putative transcriptional profile without reliance on external single-cell transcriptomics references. More details regarding how the method works can be found in the [published paper](https://www.nature.com/articles/s4146...) as well as on [https://jef.works/STdeconvolve/](https://jef.works/STdeconvolve/). Some have noticed that when analyzing ST data with `STdeconvolve`, no normalization is done. Rather, an unnormalized gene expression counts matrix is provided. This is different from dimensionality reduction and clustering analysis, where gene expression per spot must be normalized to control for variation in sequencing depth or other factors that could lead to certain spots having more genes detected than others. So why do we normalize when performing dimensionality reduction and clustering but not when performing deconvolution with `STdeconvolve`? Here, I code in R to take a hands-on, simulation-based approach to prove to myself that normalization impacts the results of dimensionality reduction and clustering but not deconvolution with `STdeconvolve`. Some potentially helpful moments: - Introduction:    • Live R Coding Session - normalizing s...   - Simulating spatial transcriptomics data:    • Live R Coding Session - normalizing s...   - Tweaking simulation to create very obvious variation in terms of total genes detected per spatially resolved spot:    • Live R Coding Session - normalizing s...   - Dimensionality reduction and clustering analysis on unnormalized gene expression counts:    • Live R Coding Session - normalizing s...   - Dimensionality reduction and clustering analysis on library-size normalized gene expression:    • Live R Coding Session - normalizing s...   - Deconvolution analysis with `STdeconvolve` on unnormalized gene expression counts:    • Live R Coding Session - normalizing s...   - Deconvolution analysis with `STdeconvolve` on normalized gene expression:    • Live R Coding Session - normalizing s...   - Conclusion:    • Live R Coding Session - normalizing s...   Follow along in the video or try out the code for yourself: - https://jef.works/blog/2023/05/04/nor... Please take my live commentary with a grain of salt ;) I use the terms "cells" and "spots" rather interchangeably though here each spot corresponds to multiple simulated single cells.