Function reference • tidyproteomics

Import / Export

Data can imported from flat tables or file directories, and exported again into flat tables. See vignette("importing") for more details and examples.

import(): Main function for importing data

export_quant(): Export the quantitative data from an tidyproteomics data-object

export_analysis(): Export the quantitative data from an tidyproteomics data-object

export_compexp(): Comparative analysis between two expression tests

export_config(): Helper function to export the config file to current project directory

as.data.frame(<tidyproteomics>): Helper function to convert the data-object into a tibble

load_local(): Load project specific data

save_local(): Store data locally

save_table(): Write table data locally

Helper functions

These functions can save data and tables out to the project folder, add annotations to the data and tables, modify the experimental names, and list out the sequence of data transformations.

merge(): Merge multiple tidyproteomics data-objects

collapse(): Convert peptide quantitative data into protein quantitative data

operations(): Returns the data transformations

reassign(): reassign the sample info

annotate(): Main function for adding annotations to a tidyproteomics data-object

show_annotations(): Display the current annotation data

path_to_package_data(): Helper function for displaying path to data

Subsetting

Subsetting filters an tidyproteomics data-object by a given regular expression to create a subset. See vignette("subsetting") for more details and examples.

subset(<tidyproteomics>): Create a data subset

intersection(): Create a data subset

Summarizing

Summarizing computes quick summary statistics on a given tidyproteomics data-object. See vignette("summary") for more details and examples.

print(<tidyproteomics>): Tidy-Quant data object print definition

plot(<tidyproteomics>): Tidy-Quant data object plot definition

summary(<tidyproteomics>): Summarize the data

plot_counts(): Plot the accounting of proteins. peptides, and other counts

plot_quantrank(): Plot the variation in normalized values

Normalizing

Normalizing adjusts the quantitiative values between samples to remove collection biases, based on a number of methods. See vignette("normalizing") for more details and examples.

normalize(): Main function for normalizing quantitative data in a tidyproteomics data-object

select_normalization(): Select a normalization method

Imputing

Imputing calculates values for missing observations, based on a number of methods. See vignette("imputing") for more details and examples.

impute(): Main method for imputing missing values

impute.randomforest(): Imputes missing values based on the missForest function

Visualization

Visualizing data is important for understanding the underlying quality of the data as well as generating graphical representations for documenting work. See vignette("visualization") for more details and examples.

plot_normalization(): Plot normalized values

plot_variation_cv(): Plot the variation in normalized values

plot_variation_pca(): Plot the PCA variation in normalized values

plot_dynamic_range(): Plot CVs by abundance

plot_venn(): GGplot2 extension to plot a Venn diagram

plot_euler(): GGplot2 extension to plot a Euler diagram

plot_pca(): Plot PCA values

plot_heatmap(): Plot a heatmap of quantitative values by sample

Two-sample Analysis

Two-sample Analysis is a process by which two samples are statistically compared to identify potential observational outliers worth explroing further. See vignette("expression") and vignette("enrichment") for more details and examples.

expression(): Summarize the data

plot_volcano(): Volcano plot of expression values

plot_proportion(): Plot proportional expression values

plot_compexp(): Comparative analysis between two expression tests

enrichment(): Compute protein enrichment

plot_enrichment(): Bubble plot of enrichment values

Automation

**Automating the expression and enrichment analyses. See vignette("expression") and vignette("enrichment") for more details and examples.

analyze_expressions(): Analysis tables and plots of expression values

analyze_enrichments(): Analysis tables and plots of expression values

Protein Sequencing

Protein Sequencing allows for the visulaization of peptide sequences and modifications mapped back onto the full protein sequence. See vignette("sequencing") for more details and examples.

plot_protein(): Visualize mapped sequence data

protein_map(): Align a peptide data to protein sequences for visualization