seandavi/awesome-single-cell

This repository is a community-maintained reference list of software tools, data resources, and reading material for single-cell data analysis. Single-cell analysis refers to techniques that measure gene activity or other molecular properties in individual cells rather than averaging across millions of them at once. This gives researchers a much finer-grained view of cell differences within a tissue, tumor, or organism. The list is organized into many categories. On the software side it covers tools for RNA-seq analysis (measuring gene expression per cell), quality control, cell clustering (grouping similar cells together), dimension reduction (shrinking complex data into visualizable form), batch-effect correction (removing technical artifacts when combining data from multiple experiments), trajectory inference (reconstructing how cells change over time or as they mature), cell type identification, epigenomics, spatial transcriptomics (which adds location information to single-cell measurements), and more. Tools for immune receptor profiling, copy number analysis, rare cell detection, and cellular communication are also included. Beyond software, the list links to tutorials and analysis workflows, interactive web portals and databases where single-cell datasets can be browsed, and a curated set of journal articles covering methods comparisons, experimental design guidance, and general introductions to the field. A section listing researchers working in single-cell biology is also included. Most of the listed tools are implemented in R or Python, with some in C++, Rust, and other languages. Each entry links to the tool's source code and a brief description of what it does. The list accepts community contributions and has a formal citation DOI for use in academic references. The full README is longer than what was shown.