WashU Pan-Cancer miRNome Atlas


Search by miRNA
  Tumor Development
  Tumor Stage and Grade
  • Survival Outcome
  • Expression Profile
Search by Cancer Type
  Tumor Development
  Tumor Stage and Grade
  Survival Outcome
  • Expression Profile
Search for miRNA-Target Correlation
  Search by miRNA
  Search by Gene
Survival Signature Analysis
  Pre-calculated signatures
  Custom signatures
Clustering Analysis
  Hierarchical clustering
  K-means clustering
Create hierarchical clustering heatmaps

Select at least 3 cancer types
Select all cancers
Use all miRNAs or include a list of miRNAs for clustering.
Use all miRs within filter range in creating clusters
Please submit miRNAs using 3p/5p designations. Separate with spaces, semicolons, commas or newlines.
Example submission
Additional cluster options
Color options for hierarchical clustering   Blue/White/Red Red/Black/Green Yellow/Orange/Red
Input the number of clusters of patients to show in tabular format  
Filter type:      Minimum:      Maximum:

Use hierarchical clustering to group cancer types together by average miRNA expression.

Hierarchical clustering orders samples by similarity of features, which here are average miRNA expression levels. The closeness of similarity is shown in the dendrogram.

Provide a list of miRNAs to use in clustering in the text box, or check the box to use all available miRNAs.

The results are expressed visually as a heat map, which can be shown as gradients of the color options.
Mean-based scaling of expression values within samples is highly recommended, but can be turned off.
The dendrogram can be cut to create clusters of patients.

miRNAs can be excluded on the basis of mean expression or standard deviation of expression throughout the dataset. Use the minimum/maximum values to set the filter limits.