Data processing
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InteRact()
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Analysis of AP-MS data |
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analyse_interactome()
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Construct an interactome by comparing bait and control background across experimental conditions |
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append_FDR()
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Append a FDR column to an InteRactome |
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append_annotations()
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Append annotations to an InteRactome |
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average_technical_replicates()
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Average protein intensities over technical replicates |
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compare_stoichio()
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Compare stoichiometries between two conditions using a t-test |
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compute_FDR_from_asymmetry()
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Compute the FDR from the asymmetry of the volcano plot |
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compute_correlations()
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Compute correlation in protein recruitment |
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discretize_values()
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Discretize values in a vector based on a finite set of values |
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dot_plot()
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Dot plot representation of matrices |
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estimate_Npep()
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Get the number of theoretically observable peptides per protein |
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filter_Proteins()
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Filtering of a data frame using a threshold on protein identification score and
gene names |
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filter_conditions()
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Filter conditions from an interactome |
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geom_mean()
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Perform the geometric mean of a numeric vector |
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global_analysis()
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Adds global variables by analysing values
across all conditions of an InteRactome |
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identify_conditions()
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Identify conditions (background, time of stimulation, biological and technical replicates)
from column names |
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identify_indirect_interactions()
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Identify indirect interactions by comparing stoichiometries between two interactomes. |
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identify_interactors()
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Identify specific interactors in an InteRactome |
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mean_analysis()
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Compute the mean InteRactome (on variables 'p_val', 'fold_cahnge', 'stoichio' and 'stoichio_bio')
from a list of InteRactomes |
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merge_conditions()
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Merge different conditions from different interactomes into a single data.frame |
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merge_duplicate_groups()
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Merge protein groups with the same gene name. |
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merge_proteome()
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Add protein abundance to an InteRactome |
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moving_average()
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Performs a running average on a numeric vector |
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normalize_interactome()
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Normalize the log fold change by its standard deviation for each condition |
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order_interactome()
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Order proteins within an InteRactome |
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preprocess_data()
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Preprocessing of raw data |
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proteinGroups_Cbl
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Characterization of protein groups identified from AP-MS data using MaxQuant |
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rescale_median()
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Normalize data frame by columns using the median |
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restrict_network_degree()
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Limit the number of edges per node within a network |
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row_mean()
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Compute the mean by row |
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row_sd()
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Compute the standard deviation by row |
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row_stoichio()
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Compute the stoichiometry of interaction using the method described in ... |
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row_ttest()
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Perform a t-test comparison between two groups by row |
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smooth_interactome()
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Smooth, using a moving average across conditions, selected variables of an InteRactome |
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summary_protein()
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Create a summary for selected proteins in an InteRactome |
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summary_table()
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Create a summary table for an InteRactome |
Visualization
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plot_2D_stoichio()
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Plot abundance versus interaction stoichiometries |
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plot_FDR_map()
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Plot FDR as a function of parameters used to divide the volcano plot |
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plot_QC()
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Quality check plots for preprocessed AP-MS data |
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plot_comparison()
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Plot protein intensities per biological replicate and background |
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plot_correlation_network()
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Plot an interactive correlation network with communities highlighted |
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plot_density()
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Plot points with density background with correlation coefficient |
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plot_indirect_interactions()
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Plot indirect interactions |
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plot_per_condition()
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Dot plot representation of interaction as a function of experimental conditions |
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plot_stoichio()
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Plot interaction stoichiometries per biological replicate |
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plot_volcanos()
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Plot protein enrichement fold-change versus p-value |