init-analysis

You can automate scRNA-seq analysis by running a modular 7-step pipeline that handles data loading, QC, normalization, dimensionality reduction, clustering, and marker analysis automatically. The pipeline detects your data type and outputs an interpretable HTML report with processed data and figures.

A standard single-cell QC and normalization workflow includes data loading, quality control filtering, normalization, dimensionality reduction, clustering, marker analysis, and report generation. This pipeline supports scRNA-seq, CyTOF, and flow cytometry data, producing plain-language reports for downstream interpretation.

Yes, the pipeline auto-detects whether your input is scRNA-seq, CyTOF, or flow cytometry data and adapts its processing accordingly. You provide raw datasets or folders, and the workflow automatically configures the appropriate QC, normalization, and analysis steps for each data type.

The best way to run marker analysis and dimensionality reduction on flow cytometry data is using an automated pipeline that detects the data type and applies a modular 7-step workflow. This produces an HTML report with clustering results and marker findings suitable for exploratory analysis.

No, you do not need to manually separate scRNA-seq and flow cytometry files before analysis. The pipeline automatically detects the data type from your raw datasets or folders and adapts the processing steps accordingly, handling scRNA-seq, CyTOF, and flow cytometry without manual intervention.