sc-best-practices-complete-100percent

scRNA-seq preprocessing involves quality control, normalization, and feature selection to prepare raw count matrices. It ensures accurate downstream clustering, differential expression, and compositional analysis by removing technical artifacts.

Differential abundance testing with scCODA uses compositional data analysis to identify significant changes in cell-type proportions. You provide cell counts per sample and a reference cell type to detect statistically meaningful shifts across conditions.

Yes, these best practices cover workflows for spatial transcriptomics and multi-omics contexts. You can apply the provided guidance to preprocess data and perform compositional analysis across these varied single-cell data modalities.

The best way to analyze the Haber dataset is to follow structured pipelines using bundled references. You load the exemplar case studies to reproduce established compositional analysis results and test for differential abundance across conditions.

No, you do not need external tooling to run these workflows. The guidance relies on bundled references and frontmatter metadata loaded directly from the repository to demonstrate single-cell analysis pipelines without extra dependencies.