init-analysis

Automate scRNA-seq data analysis by providing your dataset path to trigger a 7-step pipeline that handles data loading, quality control, normalization, dimensionality reduction, clustering, and marker analysis automatically.

Yes, the single-cell pipeline supports flow cytometry and CyTOF data. It features auto-detection for high-dimensional single-cell biology data types, including scRNA-seq, CyTOF, flow cytometry, and proteomics.

An automated single-cell analysis pipeline includes seven modular steps: data loading, quality control, normalization, dimensionality reduction, clustering, marker analysis, and final report generation.

Generate interpretability-friendly reports for non-bioinformaticians by running the automated pipeline, which outputs plain-language summaries, processed data, figures, and a machine-readable summary of the results.

Yes, you can customize the single-cell workflow because the pipeline operates with modular steps, allowing you to adapt specific stages like quality control and normalization for custom data analysis strategies.

You need a Python-based pipeline environment and your dataset path to run the automated initial data analysis, which then executes the modular steps and outputs figures with processed data.