Supported Features

The following is a list of supported features

We iterate fast. If there's a feature your team needs sooner, reach out to your Customer Success Manager or contact us at [e-mail]

Feature Overview

Feature	Status	Data Types	Description
Exploratory Data Analysis (EDA)	✅ Supported	Bulk, scRNA-seq, Proteomics, DRUG-seq, Code-Omics	Supporting QC, normalization, ample filtering, and outlier detection.
Differential Expression Analysis (DEA)	✅ Supported	Bulk, scRNA-seq, Proteomics, DRUG-seq, Code-Omics	Supports Bulk RNA-seq (DESeq2, Limma, pseudobulk counts. scRNA-seq (cell type specific Wilcoxon rank-sum. Proteomics, DrugSeq, CodeOmics
Functional Enrichment Analysis (FEA)	✅ Supported	Bulk, scRNA-seq, Proteomics, DRUG-seq, Code-Omics	Performs Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA) to identify enriched biological processes, molecular functions, and cellular components from your expression or abundance data. Outputs enrichment bar charts, pathway and gene set overlays and gene-set relationship tables. KEGG-based analysis with overlays.
Target Discovery	✅ Supported	Bulk, scRNA-seq, Proteomics, DRUG-seq, Code-Omics	Supporting raw fastq or expression matrix files (h5ad, csv, etc) as input to infer PPI networks and predicted targets. Integrates expression networks and literature.
Protein-Protein Interaction	✅ Supported	Bulk, scRNA-seq, Proteomics, DRUG-seq, Code-Omics	Visualize gene interaction networks and identify hubs or regulators based on expression data.
Clustering Analysis	✅ Supported	Bulk, scRNA-seq, Proteomics, DRUG-seq, Code-Omics	Group similar samples or cells by expression profile. Leiden/Louvain supported
Dimensionality Reduction	✅ Supported	Bulk, scRNA-seq, Proteomics, DRUG-seq, Code-Omics	Visualize structure using PCA, UMAP, t-SNE, or PACMAP.
Cell Type Identification	✅ Supported	scRNA-seq	Match cell clusters to reference annotations from public atlases. uses marker gene scoring and optional references.
Discovery Engine	✅ Supported	All	Leveraging the power of Mithrl's Lattice Knowledge Graph to auto-surface known & predicted associations, drug links, and co-expression via the knowledge inference engine.
WGS/WES Analysis	🔜 Coming Soon	Genomic	Detect mutations and structural variants; link to transcriptomic impact
DNA Methylation (Bulk + sc)	🔜 Coming Soon	Epigenomic	Identify differentially methylated regions and correlate with gene activity
Proteomics Integration	🔜 Coming Soon	Proteomic	Connect transcript expression to protein-level data and changes
Metabolomics Integration	🔜 Coming Soon	Metabolomic	Map transcriptomic shifts to metabolic pathways and readouts
Multi-Omics Cohort Analysis	🔜 Coming Soon	Multi-omics	Analyze across multiple omics (e.g., RNA, DNA, protein) into unified analysis
Lead Optimization AI	🔜 Coming Soon	Downstream	Suggest lead molecules based on gene signature, pathway response, and SAR overlays
High-Throughput Screening AI	🔜 Coming Soon	Downstream	Interpret large HTS datasets using AI-guided dimensionality reduction, target filtering, and phenotypic enrichment
IND Support AI	🔜 Coming Soon	Preclinical	Identify toxicogenomic signatures, validate biomarkers across species, and generate IND-ready transcriptomic justifications
Clinical Trial Analysis AI	🔜 Coming Soon	Clinical	Stratify trial arms, match molecular subtypes to response groups, identify responder biomarkers, and generate trial decision support summaries

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