Functions

Preprocessing

  • All preprocessing steps can be done with: preprocess()

  • The information about the preprocessing steps can be accessed any time using preprocess_print_info()

Figures

To generate interactive plots, AlphaStats uses the graphing library Plotly and all plotting methods will return a plotly object. The plotly graphs returned by AlphaStats can be customized. A description on how do customize your plots can be found here

Plot Intensity

  • Plot Intensity for indiviual Protein/ProteinGroup plot_intensity()

  • Plot Intensity distribution for each sample plot_sampledistribution()

Dimensionality reduction plots:

  • Principal Component Analysis (PCA): plot_pca()

  • t-SNE: plot_tsne()

  • UMAP plot_umap()

Plot Distance between samples

  • Plot correlation matrix plot_correlation_matrix()

  • Plot Dendrogram plot_dendrogram()

  • Plot Clustermap alphastats.DataSet_Plot.Plot.plot_clustermap()

Volcano Plot

To estimate the differential expression between two groups, the function plot_volcano() either performs a t-test, an ANOVA or a Wald-test using the package diffxpy .

  • Volcano Plot plot_volcano()

The results of the statistical analysis for the volcano plot will be saved within the plot and can be accessed:

plot = DataSet.plot_volcano(column = "disease", group1 = "healthy", group2 = "cancer")
plot.plotting_data

Save Figures

The plots will return a plotly object, thus you can use write_image() from plotly. More details on how to save plotly figures you can find here.

Statistical Analysis

  • Perform Differential Expression Analysis a Wald test or t-test diffxpy. diff_expression_analysis()

  • ANOVA anova()

  • ANCOVA ancova()

  • Tukey - test tukey_test()

GO Analysis

The GO Analysis uses the API from aGOtool.

  • Characterize foreground without performing a statistical test: go_characterize_foreground()

  • Gene Ontology Enrichment Analysis with abundance correction: go_abundance_correction()

  • Gene Ontology Enrichment Analysis without abundance correction: go_compare_samples()

  • Gene Ontology Enrichement Analysis using a Background from UniProt Reference Proteomes: go_genome()

Visualization of GO Analysis results

All GO-analysis functions will return a DataFrame with the results.

  • Plot Scatterplot with -log10(p-value) on x-axis and effect size on y-axis. df.plot_scatter()

  • Plot p-values as Barplot df.plot_bar

Misc

Get an overview over your dataset

  • overview()

  • preprocess_print_info()