Package index • ProtPipe

All functions

ProtData-class: ProtData Class
add_entrez(): Add Entrez Gene IDs to a Data Frame
apply_min_intenisty(): Apply Limit of Detection Threshold
batch_correct(): Correct for Batch Effects
compare_protein(): Creates a ggplot bar chart comparing the intensity of a single protein either across all samples or grouped by a condition.
convert_numeric_cols(): Safely Convert Character Columns to Numeric Type
create_protdata(): Create a ProtData Object
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.
create_se_from_olink(): Create a SummarizedExperiment Object from Olink NPX Data
create_se_from_soma(): Create a SummarizedExperiment Object from SomaScan Data
do_comparison_continuous(): Perform limma differential expression for a continuous outcome
do_limma(): Perform limma differential expression on a ProtData object
do_limma_binary(): Perform limma differential expression on a SummarizedExperiment
do_limma_by_condition(): Perform DEA using the condition labels of the protdata object
do_t_test(<ProtData>): Title
do_t_test(): Title
enrich_go(): Perform Gene Ontology (GO) Over-Representation Analysis (ORA)
enrich_kegg(): Perform KEGG Over-Representation Analysis (ORA)
enrich_pathways(): Perform Comprehensive GO and KEGG Pathway Enrichment Analysis
filter_features(): Helper function to filter out sparse proteins
filter_outlier_samples(): Filter Outlier Samples Based on Protein Counts
filter_overlap(): get_overlap method for protdata class
filter_proteins_by_percent(): Filter Proteins by Percentage of Valid Values
filter_unique_proteins(): Removes duplicate analytes
generate_preprocessing_report(): Generate Markdown Report of Preprocessing Steps
get_CVs(): Calculate Coefficient of Variation (CV) for Protein Groups
get_PCs(): Calculate Principal Components Analysis (PCA)
get_pg_counts(): Get Protein Group Counts Per Sample
get_sample_correlation(): Calculate Pairwise Sample Correlations
get_umap(): Calculate UMAP Dimensionality Reduction
gse_go(): Perform Gene Ontology (GO) Gene Set Enrichment Analysis (GSEA)
gse_kegg(): Perform KEGG Gene Set Enrichment Analysis (GSEA)
has_step(): Check if a Processing Step has been Applied
impute(): Impute Missing Values with a Constant
impute_left_dist(): Impute from a Down-Shifted Normal Distribution
impute_min(): Impute Missing Values with the Row Minimum
lod_filter(): Filter Assay Based on Limit of Detection (LOD)
log2_transform(): Performs a log2 transform of protein intensity values
mean_normalize(): Mean Normalization of Proteomics Data
median_normalize(): Median Normalization of Proteomics Data
num_samples(): Number of samples
olink_all_output(): Title
plot_CVs(): Plot Coefficient of Variation (CV) Distributions
plot_PCs(): Plot Principal Component Analysis Results
plot_correlation_heatmap(): Plot a Sample Correlation Heatmap
plot_correlation_volcano(): Plot a Volcano Plot for Correlation Results
plot_hierarchical_cluster(): Plot a Hierarchical Clustering Dendrogram of Samples
plot_pg_counts(): Plot Protein Group Counts
plot_pg_intensities(): Plot Boxplots of Sample Intensity Distributions
plot_plsda(): Generate a PLS-DA scores plot for a ProtData object
plot_proteomics_heatmap(): Plot a Proteomics Heatmap
plot_umap(): Plot UMAP Dimensionality Reduction Results
plot_volcano(): Plot a Volcano Plot for Differential Expression Results
run_protpipe_shiny(): Run the protpipe Shiny App
z_score(): Z-Score Normalization for Proteins Across Samples
soma_all_output(): format soma adat into data and condition dataframes