Use Case

Automates key steps of scRNA-seq data processing: quality control, normalization, clustering, marker gene identification, cell type annotation and more with customizable code scripts.

Simply describe what you want to measure and Drylab’s AI will run ImageJ workflows for you, including batch processing, to deliver data that answer your question.

Upload count matrices to perform automated DE testing across experimental conditions, generating gene lists, plotting for easy interpretation.

Input the name or sequence of molecules plus optional details to generate 3D structural models with docking poses and scoring reports

Accepts raw, uncleaned laboratory or clinical data, automatically handles cleaning, suggests appropriate tests and delivers clear results with plots and reports.

Analyzes raw mass spectrometry data or processed abundance matrices to compare protein levels across conditions with comprehensive result visualizations.

Automates processing and analysis of spatial gene expression data, including QC, normalization, spot-level expression quantification, clustering, and visualization.

Automates cell population identification in flow or mass cytometry data to improve reproducibility and speed in immunophenotyping studies.

Easily generate publication-ready figures and charts that stay synchronized with your data, with consistent colors and labels automatically.

Quickly find and adapt methods from peer-reviewed literature or drop in your own protocol file to customize it for your available resources and experimental needs.

Check if ideas and results are supported or challenged against high-quality, peer-reviewed research papers, ensuring well-grounded research direction.

Run different pipelines of analysis on multiple datasets at once, suitable for rigorous analysis such as in research.

Ensure analyses use the same code and settings every time, with options to llet AI make small adaptations, to reduce redudant tasks.

Instantly creates reports or incorporates existing reports, reducing time spent on documentation.