For the complete documentation index, see llms.txt. This page is also available as Markdown.

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 support@mithrl.com

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

Still have questions? We have answers. Contact us at support@mithrl.com

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