Profiler supports proteomics, metabolomics, lipidomics, genomics, transcriptomics and other omics data types in a unified workflow.
Convert, normalize, clean, correct batches, handle missing-values/zero-inflated and explore raw omics datasets using automated and guided preprocessing pipelines with smart suggestions adapted to each dataset.
Profiler integrates classical statistics, machine learning and deep learning for classification, regression, clustering and feature selection.
Identify discriminant molecular features using volcano plots, feature importance, SHAP and LIME explanations.
Perform biological pathway and enrichment analysis directly from selected features, with more than 100 databases in one frame such as GO, Reactome, drug...
Kaplan–Meier curves and Cox proportional hazards models are available for omics-driven survival studies.
An additional desktop tool for preprocessing Mass Spectrometry Imaging (MSI) .imzML files for direct integration into Profiler.