Supported Features
Below is a list of all features available to every Mithrl user. Each one is fully supported in production environments, designed to reduce manual effort and let scientists focus on discovery.
Each section includes:
What the feature is
Why it matters in therapeutic research
What data it works with
Example prompts you can use in Mithrl
🔍 Exploratory Data Analysis (EDA)
What it does: Helps you understand your dataset before running deeper analyses. EDA includes quality control, basic statistics, and detecting sample-level outliers.
Why it matters: It's the first step to catch low-quality samples, confirm input integrity, and make sure you're asking questions of reliable data.
Supports:
Bulk RNA-seq: counts or TPM
scRNA-seq: raw count matrices
Outputs:
Sample correlation heatmaps
Violin plots of distributions
QC stats for each sample
Example Use Cases:
Detect samples with low total counts or technical artifacts
See if treated and control groups separate early on
Flag samples that may skew downstream results
Suggested Prompts:
You can copy these prompts and run them directly in Mithrl.
🔬 Differential Expression Analysis (DEA)
What it does: Identifies genes that are statistically up- or downregulated between groups (e.g. treated vs control).
Why it matters: This is often the core of any biological discovery workflow finding genes affected by your drug, knockout, or condition.
Supports:
Bulk RNA-seq: DESeq2
scRNA-seq: Wilcoxon rank-sum (cell-type-specific)
Outputs:
Volcano plots
Fold change tables
Significance values (p-adj)
Example Use Cases:
Find genes most responsive to a drug
Discover transcriptional signatures of disease or treatment
Validate hits seen in vitro or in vivo
Upset plot showcasing DEGs unique to single treatment or common to many treatments
Suggested Prompts:
🧪 Functional Enrichment Analysis (FEA)
What it does: Maps your differentially expressed genes to known biological pathways to understand their functional impact.
Why it matters: Genes don't act in isolation. Pathways tell you how groups of genes are cooperating or failing under treatment.
Supports:
Bulk RNA-seq (DEA results)
scRNA-seq (DEA results)
Outputs:
Enrichment bar charts
KEGG pathway overlays
Gene-pathway relationship tables
Example Use Cases:
Validate that your drug hits its intended pathway
Suggest additional targets in the same pathway
Spot potential toxicity via enriched off-target pathways
Suggested Prompts:
🎯 Target Discovery
What it does: Uses expression changes, literature, and network features to highlight genes that are strong therapeutic candidates.
Why it matters: Helps prioritize which genes to validate, develop assays for, or present in team meetings as promising targets.
Supports:
Bulk RNA-seq
scRNA-seq
PPI networks + curated knowledge bases
Outputs:
Ranked target list
Proposed target reports and hypothesis
Literature support summaries
Example Use Cases:
Find underexplored targets with strong network roles
Cross-validate known targets using expression and evidence
Identify biomarkers for diagnostics or stratification
Suggested Prompts:
🔗 Protein-Protein Interaction (PPI) Analysis
What it does: Visualizes how your differentially expressed genes interact as part of protein networks and scores key hubs.
Why it matters: Central genes in networks often have outsized biological roles. Targeting a hub can have system-level effects.
Supports:
Bulk RNA-seq + DEA
scRNA-seq + DEA
Outputs:
Network graph visualizations
Hub gene identification
Gene connectivity metrics
Example Use Cases:
Identify master regulators or upstream drivers
Suggest combination therapy based on converging nodes
Explore pathways not apparent from expression alone
Suggested Prompts:
🧬 Clustering Analysis
What it does: Groups samples (or cells) based on similarity in gene expression.
Why it matters: Discover hidden structure in your dataset, such as subtypes, dose-dependent patterns, or unexpected outliers.
Supports:
Bulk RNA-seq
scRNA-seq
Methods:
Leiden / Louvain clustering
Outputs:
Cluster labels per sample or cell
Clusters overlay on any supported dimensionality reduction coordinates (e.g. UMAP, PCA, tSNE, etc.)
Example Use Cases:
Discover molecular subtypes among patients
Cluster cells by expression to annotate cell states
Predict who may respond to treatment
Suggested Prompts:
🧬 Cell Type Identification (scRNA-seq)
What it does: Automatically assigns known cell types to clusters in single-cell RNA-seq datasets using marker genes and literature.
Why it matters: Cell-type resolution is critical in precision biology. Knowing which cells respond to treatment helps you understand selectivity, safety, and mechanism.
Supports:
scRNA-seq only (10x or similar)
Optional: custom marker sets
Methods:
Marker gene scoring
Reference-assisted annotation
Optional: automated literature matching
Outputs:
Cluster-to-cell-type mapping
Interactive dimensionally reduction plots with labeled cell-types
Marker gene enrichment per cluster
Use Cases:
Identify responder or non-responder cell types
Confirm targeting specificity
Profile immune subtypes or tumor microenvironment
Suggested Prompts:
📉 Dimensionality Reduction
What it does: Projects your high-dimensional data into 2D or 3D to help you see global patterns and separation between groups.
Why it matters: Reduces complexity, highlights trends, and helps interpret overall structure.
Supports:
Bulk RNA-seq
scRNA-seq
Methods:
PCA, UMAP, t-SNE, PACMAP
Outputs:
Interactive scatter plots
Color overlays by metadata or gene expression
Example Use Cases:
Visualize how Drug A shifts treated samples
Detect batch effects
Spot rare cell populations
Suggested Prompts:
Useful Tips
Each feature can be used directly through natural language input. Simply describe what you want to see — Mithrl will handle the preprocessing, statistics, and visuals.
If you're unsure how to phrase something, reach out to your Customer Success Manager via Slack, Teams, or email us at support@mithrl.com.
Still have questions? We have answers. Contact us at support@mithrl.com
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