init-analysis

You can automate initial scRNA-seq data analysis by running a modular pipeline that handles loading, quality control, normalization, dimensionality reduction, clustering, and marker analysis, ending with an HTML report.

Processing high-dimensional flow cytometry data end-to-end is best handled by an automated pipeline that auto-detects your data type and orchestrates quality control, normalization, dimensionality reduction, and clustering.

Yes, the single-cell analysis pipeline supports CyTOF data automatically by detecting the data type from the input path and applying the appropriate quality control and dimensionality reduction steps.

You can customize specific steps in the scRNA-seq pipeline by importing individual step modules, allowing you to extend or modify the quality control and normalization processes as needed.

You need a Python runtime environment to run the bioinformatics analysis pipeline, providing the input dataset path to execute the end-to-end workflow and generate the final report.