Top used analyses¶
- A univariable survival analysis between a molecular variable and patient overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), etc.
- A differential analysis using a rank-sum test (Wilcoxon or Kruskal-Wallis, default) or student t-test in 2 or more than 2 groups.
- Pearson or spearman (default) correlation analysis between two variables.
- Mutation count bar plot. Can be grouped by any clinical feature, such as ER, PR, Her2 status, if available.
- Driver mutation enrichment analysis. This analysis calculates whether a gene mutation is enriched in two different patient cohorts (e.g., ER+ vs. ER-). A chi-square test is performed.
- Mutation signature summary heatmap. Hierarchical clustering is performed by default.
- A caterpillar plot can be used to visualize mutation count, number of neoantigens, and any other numeric variables.
- Mutation lollipop. If a mutation occurred in a coding gene, UniProt canonical protein sequence and Pfam domain annotation are annotated as the x-axis. If a mutation occurred in a non-coding region, the chromosome position is used to show the mutation position.
- A boxplot with scatter dots can be applied to visualize the number of samples, mutation counts, number of neoantigens, or any other numeric variables.
- A stacked bar plot can be used to visualize mutation signatures and any numeric variables.
- A pie chart is used to summarize categorical variables in a sample cohort, such as gender, stage, grade, tissue, race, etc.
- A histogram plot is used to summarize numeric variables in a sample cohort, such as age, survival time, time of initial diagnosis, etc.
- A waterfall plot (mutation landscape) visualizes the mutation and copy number variation in a sample cohort.