CoExpress

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Reference Data

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Data from DepMap 25Q3:

CRISPRGeneEffect.csv (gene effects)
Model.csv (cell line metadata)
OmicsSomaticMutationsMatrixHotspot.csv (hotspot mutations)
OmicsFusionFiltered.csv (translocations/fusions)

Please acknowledge DepMap if you use this data.

Open

1. Set Parameters

Analysis Mode:

Analysis (within input genes) Design (all genes) Mutation Analysis (differential expression) Synonym/Ortholog Lookup

Correlation Cutoff: 0.50

Min Slope: 0.10

Analysis Sub-type:

Hotspot Mutation Translocation/Fusion

Hotspot Mutation (required):

Min Cell Lines:

Lineage Filter:

Subtype Filter:

Exclude Tissues: ▼ click to expand

Hotspot Mutation Filter:

Selecting for mutations may introduce bias — mutations are often enriched in specific cancer types.

Translocation/Fusion Filter:

2. Input Genes

Gene symbols (one per line):

Enter Gene, LFC, FDR (tab or comma separated): Stats auto-loaded when you click Run

3. Results

New to CoExpress?

Click Test Genes and then Run ▶

(both in "2. Input Genes")

Next, double-click nodes and edges in the generated network for more functions. Decrease correlation cutoff and press Run again to identify interactions of weaker strength.

Tip: Click Correlate to view a scatter plot, or By Tissue for tissue breakdown. Ctrl/Cmd+click a column header to copy that column. Use Filters to set numeric cutoffs on columns.

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Gene1	Gene2	Correlation	Slope	N	Cluster	Analyze
Run analysis to view correlations

Mean (All) = all cells, Mean (Filt) = filtered cells. * indicates genes from your input list (Design mode). All input genes are shown; "Corr" column indicates if correlations were found.

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Gene	Cluster	Mean Effect	SD
Run analysis to view clusters

Note: Differential expression may reflect selection bias (e.g., mutations enriched in certain cancer types) rather than direct functional consequences of the mutation. Consider filtering by lineage to control for tissue-specific effects.

Statistics: p-values are calculated using Welch's t-test comparing expression scores between wild-type (WT, 0 mutations) and mutated cells (1+2 or 2 mutations). Δ Expr = Mean(mutated) − Mean(WT).

0 genes with p < 0.001

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	Gene	N (WT)	Mean Expr (WT)	N (1+2)	Mean Expr (1+2)	Δ Expr ▲	p-value	N (2)	Mean Expr (2)	Δ Expr (2v0)	p-value (2v0)	N (Fused)	Mean Expr (F)	Δ Expr (F)	p (F)

Low Risk: Exact synonyms from HGNC. Mid Risk: Less certain synonyms. Orthologs: Mouse-to-human gene mappings.

Input Gene	Status	Official Symbol	Low Risk Synonyms	Mid Risk Synonyms	Orthologs (Mouse→Human)
Select "Synonym/Ortholog Lookup" mode and click Run

Run analysis to view summary

Expression:

Min n: View:

Statistics (click row to focus)

p-values: Welch's t-test comparing each cancer type vs all other cell lines.

Group	N	Mean Expr	SD

Export Chart: Export CSV:

What is CoExpress?

CoExpress analyses gene expression patterns (RNA, TPM) across 1,400+ cancer cell lines from the DepMap project. Genes with correlated expression across cell lines are likely to share functional relationships — shared pathways, regulatory programs, or protein complexes.

Understanding Gene–Gene Correlation

Each gene pair is compared by their expression values across all cell lines. The Pearson correlation (r) measures how similarly two genes behave:

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Positive (r > 0)

Expression levels rise and fall together across cell lines — suggesting co-regulation or shared function.

⟷

No correlation (r ≈ 0)

Expression patterns are unrelated across cell lines.

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Negative (r < 0)

Expression levels move in opposite directions across cell lines — suggesting opposing regulation or mutually exclusive expression programs.

BRCA1 vs BRCA2 scatter plot showing strong positive correlation (r=0.71)

Example: BRCA1 vs BRCA2 expression across 1,699 cell lines (r = 0.71). These DNA repair genes are co-regulated, reflected by their correlated expression.

The Correlation Network

All pairwise correlations between your genes are combined into an interactive network graph. Each node is a gene; edges connect correlated pairs. Edge color and thickness encode the correlation:

Correlation network showing gene relationships

Example network: Genes cluster by functional relationships. BRCA1–BRCA2 (thick blue edge) are tightly co-expressed; edge thickness reflects correlation strength.
Double-click a node to see its expression profile, or an edge to view the scatter plot.

From Screen Data to Hypotheses

CoExpress is designed for two complementary purposes: analysing screen data (e.g. CRISPR hits, transcriptomic signatures) and generating new hypotheses using its built-in tools:

🔍 Design Mode

Enter seed genes and discover what co-expresses with them. Expand beyond your input list to find unexpected functional partners.

🧬 Mutation Analysis

Compare expression in cells with specific hotspot mutations or translocations vs wild-type. Identify genes differentially expressed in mutant backgrounds.

📊 Cell Line Browser

Explore individual cell lines — filter by tissue, mutation, or expression. View top expressed genes and select cell line subsets for focused analyses.

🧪 Enrichr Integration

Send any gene list to Enrichr for pathway enrichment analysis. Available from correlations, clusters, mutation results, and cell line views.

Quick start: Click Test Genes, then Run ▶ to see an example analysis.
Lower the correlation cutoff and re-run to reveal weaker interactions.