$ show omicverse/single_cell_tutorial
This repository is an educational tutorial for single-cell sequencing analysis best practices using Python. It provides a structured series of Jupyter notebooks and documentation covering preprocessing, quality control, normalization, feature selection, and advanced topics like trajectory inference and multi-modal data analysis. The tutorial uses real-world example datasets (e.g., 10x Multiome) and emphasizes reproducible workflows with AnnData and MuData objects, leveraging libraries such as scanpy, omicverse, and scverse tools. While primarily educational, it demonstrates standard data analysis patterns that can be adapted for similar single-cell projects.
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Demonstrates a reproducible preprocessing workflow for single-cell RNA-seq data, including normalization (shift-log and Pearson residuals) and feature selection using highly variable genes. Shows best practices such as storing transformed data in AnnData layers to preserve original counts and using a wrapper function (omicverse.pp.preprocess) for streamlined preprocessing. The notebook is well-documented and includes visualizations to compare raw and normalized data distributions.