Package index
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ProtData-class - ProtData Class
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add_entrez() - Add Entrez Gene IDs to a Data Frame
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batch_correct() - Correct for Batch Effects
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compare_protein() - Generate a bar chart of protein intensity values
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convert_numeric_cols() - Safely Convert Character Columns to Numeric Type
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create_protdata() - Create a ProtData Object
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create_protdata_from_olink() - Create a ProtData Object from Olink NPX Data
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create_protdata_from_soma() - Create a ProtData Object from SomaScan Data
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create_se() - This function takes a data frame of proteomics data and its corresponding sample metadata to construct a SummarizedExperiment object. It handles detection of intensity columns, validation, and synchronization of metadata.
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do_limma() - Perform limma differential expression on a ProtData object
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do_limma_by_condition() - Perform DEA using the condition labels of the protdata object
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do_t_test(<ProtData>) - Title
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do_t_test() - Title
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enrich_go() - Perform Gene Ontology (GO) Over-Representation Analysis (ORA)
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enrich_kegg() - Perform KEGG Over-Representation Analysis (ORA)
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enrich_pathways() - Perform Comprehensive GO and KEGG Pathway Enrichment Analysis
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filter_features() - Helper function to filter out sparse proteins
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filter_outlier_samples() - Filter Outlier Samples Based on Protein Counts
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filter_overlap() - get_overlap method for protdata class
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filter_proteins_by_percent() - Filter Proteins by Percentage of Valid Values
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filter_unique_proteins() - Removes duplicate analytes
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get_CVs() - Calculate Coefficient of Variation (CV) for Protein Groups
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get_PCs() - Calculate Principal Components Analysis (PCA)
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get_pg_counts() - Get Protein Group Counts Per Sample
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get_sample_correlation() - Calculate Pairwise Sample Correlations
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get_umap() - Calculate UMAP Dimensionality Reduction
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gse_go() - Perform Gene Ontology (GO) Gene Set Enrichment Analysis (GSEA)
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gse_kegg() - Perform KEGG Gene Set Enrichment Analysis (GSEA)
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has_step() - Check if a Processing Step has been Applied
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impute() - Impute Missing Values with a Constant
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impute_left_dist() - Impute from a Down-Shifted Normal Distribution
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impute_min() - Impute Missing Values with the Row Minimum
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log2_transform() - Performs a log2 transform of protein intensity values
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mean_normalize() - Mean Normalization of Proteomics Data
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median_normalize() - Median Normalization of Proteomics Data
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num_samples() - Number of samples
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plot_CVs() - Plot Coefficient of Variation (CV) Distributions
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plot_PCs() - Plot Principal Component Analysis Results
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plot_correlation_heatmap() - Plot a Sample Correlation Heatmap
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plot_hierarchical_cluster() - Plot a Hierarchical Clustering Dendrogram of Samples
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plot_pg_counts() - Plot Protein Group Counts
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plot_pg_intensities() - Plot Boxplots of Sample Intensity Distributions
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plot_plsda() - Generate a PLS-DA scores plot for a ProtData object
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plot_proteomics_heatmap() - Plot a Proteomics Heatmap
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plot_umap() - Plot UMAP Dimensionality Reduction Results
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plot_volcano() - Plot a Volcano Plot for Differential Expression Results
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z_score() - Z-Score Normalization for Proteins Across Samples