Top used analyses¶
- Univariable survival analysis between a molecular variable and patient overall survival (os), profression-free survival (pfs), disease-free survival (dfs), and etc.
- Differential analysis using 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 barplot. Can group by any clinical features 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-). Chi-square test is performed.
- Mutation signature summary heatmap. Hierarchical clustering is performed by default.
- Caterpillar plot can be used to visualize mutation count, number of neoantigens, and any other numeric variables.
- Mutation lollipop. If mutation happened in coding genes, uniprot canonical protein sequence and pfam domain annotation is annotated as x asix. If mutation happened in non-coding regions, chromosome position is used to show mutation positions.
- Boxplot with scatter dots. Can be applied to visualize number of samples, mutation counts, number of neoantigens and any numeric varibales.
- Stacked barplot. Can be used to visualize mutation signatures and any numeric variables.
- Piechart is used to summarize categorical varibales in a sample cohort, such as gender, stage, grade, tissue, race, etc.
- Histogram plot is used to summarize numeric varibales in a sample cohort, such as age, survival time, inital diagnosed time, etc.
- Waterfall plot (mutation landscape) visualizes the mutation and copy number variation in a sample cohort.