XYomics: Analysis of Sex Differences in Omics Data

Description

The **XYomics** package provides functions for performing differential expression analysis, pathway enrichment, gene regulatory network analysis, and comprehensive report generation for omics data.

Details

This package is designed to integrate various omics analyses (e.g., functional omics and single-cell data) with advanced visualization and reporting tools.

Author(s)

Maintainer: Enrico Glaab enrico.glaab@uni.lu

Authors:

• Sophie Le Bars sophie.lebars@uni.lu

• Mohamed Soudy mohamed.soudy@uni.lu

Other contributors:

• Murodzhon Akhmedov [contributor, copyright holder]

Prize-collecting Steiner Forest (PCSF)

Description

PCSF returns a subnetwork obtained by solving the PCSF on the given interaction network.

Usage

PCSF(ppi, terminals, w = 2, b = 1, mu = 5e-04, dummies)

Arguments

Details

The PCSF is a well-know problem in graph theory. Given an undirected graph G = (V, E), where the vertices are labeled with prizes p_{v} and the edges are labeled with costs c_{e} > 0, the goal is to identify a subnetwork G' = (V', E') with a forest structure. The target is to minimize the total edge costs in E', the total node prizes left out of V', and the number of trees in G'. This is equivalent to minimization of the following objective function:

F(G')= Minimize \sum_{ e \in E'} c_{e} + \beta*\sum_{v \not\in V'} p_v + \omega*k

where, k is the number of trees in the forest, and it is regulated by parameter \omega. The parameter \beta is used to tune the prizes of nodes.

This optimization problem nicely maps onto the problem of finding differentially enriched subnetworks in the cell protein-protein interaction (PPI) network. The vertices of interaction network correspond to genes or proteins, and edges represent the interactions among them. We can assign prizes to vertices based on measurements of differential expression, copy number, or mutation, and costs to edges based on confidence scores for those intra-cellular interactions from experimental observation, yielding a proper input to the PCSF problem. Vertices that are assigned a prize are referred to terminal nodes, whereas the vertices which are not observed in patient data are not assigned a prize and are called Steiner nodes. After scoring the interactome, the PCSF is used to detect a relevant subnetwork (forest), which corresponds to a portion of the interactome, where many genes are highly correlated in terms of their functions and may regulate the differentially active biological process of interest. The PCSF aims to identify neighborhoods in interaction networks potentially belonging to the key dysregulated pathways of a disease. In order to avoid a bias towards the hub nodes of PPI networks to appear in solution of PCSF, we penalize the prizes of Steiner nodes according to their degree distribution in PPI, and it is regulated by parameter \mu:

p'_{v} = p_{v} - \mu*degree(v)

The parameter \mu also affects the total number of Steiner nodes in the solution. Higher the value of \mu smaller the number of Steiners in the subnetwork, and vice-versa. Based on our previous analysis the recommended range of \mu for biological networks is between 1e-4 and 5e-2, and users can choose the values resulting subnetworks with vertex sets that have desirable Steiner/terminal node ratio and average Steiner/terminal in-degree ratio in the template interaction network.

Value

The final subnetwork obtained by the PCSF. It return an igraph object with the node prize and edge cost attributes.

Author(s)

Murodzhon Akhmedov

References

Akhmedov M., LeNail A., Bertoni F., Kwee I., Fraenkel E., and Montemanni R. (2017) A Fast Prize-Collecting Steiner Forest Algorithm for Functional Analyses in Biological Networks. Lecture Notes in Computer Science, to appear.

Internal function call_sr

Description

This function is internally used to solve the PCST.

Usage

call_sr(from, to, cost, node_names, node_prizes)

Arguments

Value

R object (SEXP) which is converted from C++ List.

Author(s)

Murodzhon Akhmedov

Compute sex-specific differentially expressed genes (DEGs) per category

Description

Identifies male-specific, female-specific, sex-dimorphic, and sex-neutral DEGs from differential expression results.

Usage

categorize_sex_sc(
  male_degs,
  female_degs,
  target_fdr = 0.05,
  exclude_pval = 0.5,
  min_abs_logfc = 0.25
)

Arguments

Value

Data frame containing categorized DEGs with associated statistics.

Perform Pathway Enrichment Analysis for Pre-Categorized Differentially Expressed Genes (DEGs)

Description

This function performs pathway enrichment analysis for differentially expressed genes (DEGs), which are already categorized into different types (e.g., Dimorphic, Neutral, Sex-specific) via the 'categorize_sex_sc' function. The function analyzes their enrichment in KEGG, GO, or Reactome pathways.

Usage

categorized_enrich_sc(
  DEGs_category,
  enrichment_db = "KEGG",
  organism = "hsa",
  org_db = org.Hs.eg.db,
  pvalueCutoff = 0.05,
  qvalueCutoff = 0.2
)

Arguments

Details

- The input DEGs are already categorized by the 'categorize_sex_sc' function. - For GO enrichment, an appropriate OrgDb object (e.g., org.Hs.eg.db for humans) must be available. - For KEGG and Reactome enrichment, gene symbols are first converted to ENTREZ IDs. - Requires the 'clusterProfiler' package for enrichment analysis. - Ensures appropriate error handling for missing genes or database issues.

Value

A named list of enriched pathways for each DEG category, structured as a data frame.

Construct Protein-protein interaction Network using Prize-Collecting Steiner Forest

Description

Constructs a condition-specific gene regulatory network based on differential expression results using the PCSF algorithm.

Usage

construct_ppi_pcsf(
  g,
  prizes,
  w = 2,
  b = 1,
  mu = 5e-04,
  seed = 1,
  min_nodes = 1
)

Arguments

Value

An igraph object representing the extracted subnetwork. Returns NULL invisibly if no prize genes are present, the subnetwork is too small, or the PCSF algorithm fails.

Convert Data Frame to enrichResult

Description

Converts a data frame containing enrichment results into a clusterProfiler enrichResult object. Assumes the data frame has columns: ID, geneID, pvalue, and optionally p.adjust.

Usage

convertdf2enr(df, pvalueCutoff = 0.1, pAdjustMethod = "BH")

Arguments

Value

An enrichResult object compatible with clusterProfiler plotting functions.

Generate Boxplots for Expression Data

Description

Creates boxplots to visualize expression differences across conditions and genders.

Usage

generate_boxplot(
  x,
  index,
  phenotype,
  gender,
  title = "Expression Boxplot",
  xlab = "Conditions",
  ylab = "Expression Level"
)

Arguments

Value

A boxplot is generated.

Generate a Comprehensive Analysis Report

Description

This function creates an integrated report that combines key analysis outputs,

Usage

generate_cat_report(
  results_cat = results_cat,
  enrichment_cat = results_cat,
  grn_object = grn_object,
  output_file = "cat_analysis_report.html",
  output_dir = tempdir(),
  template_path = NULL,
  quiet = TRUE
)

Arguments

Value

A character string with the path to the rendered report.

Generate a Comprehensive Analysis Report

Description

Creates an integrated HTML report combining differential expression results, enrichment analyses (GO, KEGG, GSEA), and gene regulatory network (GRN) data. Uses a parameterized R Markdown template for rendering.

Usage

generate_report(
  de_results,
  enrichment_results,
  grn_object,
  output_file = "analysis_report.html",
  template_path = NULL,
  params_list = list(),
  quiet = TRUE
)

Arguments

Value

Character string with the path to the rendered report.

Download and Process STRING Protein-Protein Interaction Network

Description

Downloads and processes the STRING protein-protein interaction network, converting it to a simplified igraph object. The function downloads the network from STRING database, filters interactions by confidence score, converts STRING IDs to ENTREZ IDs, and returns the largest connected component as an undirected graph.

Usage

get_string_network(
  organism = "9606",
  score_threshold = 700,
  use_default = TRUE
)

Arguments

Details

The function performs the following steps:

1. Downloads protein interactions from STRING database

2. Filters interactions based on combined score

3. Downloads and processes STRING ID to ENTREZ ID mappings

4. Creates an igraph object with filtered interactions

5. Removes self-loops and multiple edges

6. Extracts the largest connected component

Value

An igraph object representing the largest connected component of the filtered STRING network, with the following properties:

• Undirected edges

• No self-loops

• No multiple edges

• Edge weights (1000 - combined_score)

• Vertex names as ENTREZ IDs

Identify sex-specific and sex-dimorphic genes

Description

This function identifies truly sex-specific and sex-dimorphic genes by analyzing differential expression results from both sexes.

Usage

identify_sex_specific_genes(
  male_results,
  female_results,
  target_fdr = 0.05,
  exclude_fdr = 0.5
)

Arguments

Details

This function implements a two-step approach to identify sex-specific effects: 1. Identifies genes significantly affected in one sex (target_fdr) 2. Confirms lack of effect in the other sex (exclude_fdr) Additionally identifies genes with opposite (dimorphic) or same (shared) effects in both sexes.

Value

A data frame with identified genes categorized as: - male-specific: significant in males, not significant in females - female-specific: significant in females, not significant in males - sex-dimorphic: significant in both sexes with opposite effects - sex-shared: significant in both sexes with same direction Including columns for gene IDs, logFC values, and FDR values for both sexes.

Improved Pathway Enrichment Analysis

Description

Performs pathway enrichment analysis on a set of sex-biased genes using clusterProfiler.

Usage

improved_pathway_enrichment(
  gene_list,
  enrichment_db = "KEGG",
  organism = "hsa",
  org_db = org.Hs.eg.db,
  pvalueCutoff = 0.05,
  qvalueCutoff = 0.2
)

Arguments

Value

An enrichment result object.

Plot a Condition-Specific protein-protein interaction network with DEG Annotations

Description

Visualizes a gene regulatory or protein–protein interaction network for a given cell type and differential expression group. Nodes are sized and colored by degree, and key hub genes are optionally annotated with their barplots of log fold-changes across sexes.

Usage

plot_network(g, cell_type, DEG_type, result_categories)

Arguments

Value

A 'ggplot' object representing the visualized network.

Perform Sex-Phenotype Interaction Analysis for Bulk Data (Interaction Term)

Description

This function performs a formal interaction analysis on bulk expression data to identify genes whose expression is significantly modulated by the interaction between sex and a given phenotype/condition. It uses a linear model with a multiplicative interaction term ('phenotype * sex').

Usage

sex_interaction_analysis_bulk(
  x,
  phenotype,
  gender,
  phenotype_labels = c("WT", "TG"),
  sex_labels = c("F", "M")
)

Arguments

Details

This function constructs a design matrix that includes a formal interaction term between the phenotype and sex (e.g., '~ phenotype * sex'). It then uses 'limma' to test for genes where the effect of the phenotype differs significantly between sexes. This is a statistically rigorous approach to identify sex-modulated genes.

Value

A data frame with differential expression statistics for the interaction term, including logFC, t-statistic, P-value, and adjusted P-value.

Perform Sex-Phenotype Interaction Analysis for Single-Cell Data

Description

Performs differential difference analysis for a given cell type to identify genes modulated by sex-phenotype interactions using limma.

Usage

sex_interaction_analysis_sc(
  seurat_obj,
  target_cell_type,
  sex_col = "sex",
  phenotype_col = "status",
  celltype_col = "cell_type",
  min_logfc = 0.25,
  fdr_threshold = 0.05,
  sex_labels = c("F", "M"),
  phenotype_labels = c("WT", "TG")
)

Arguments

Value

A list with complete DE results, significant results, and summary statistics.

Perform differential expression analysis within each sex

Description

This function identifies differentially expressed genes between conditions separately for each sex using a linear modeling approach.

Usage

sex_stratified_analysis_bulk(
  x,
  phenotype,
  gender,
  analysis_type = c("male", "female")
)

Arguments

Details

This function performs differential expression analysis within each sex separately. For male analysis, it compares conditions within males. For female analysis, it compares conditions within females. For dimorphic analysis, it tests for difference in condition effects between sexes. Note: To identify truly sex-specific genes, use the output of this function as input for identify_sex_specific_genes().

Value

A data frame with differential expression statistics including logFC, AveExpr, t-statistic, P-value, and adjusted P-value.

Compute sex-specific differentially expressed genes (DEGs)

Description

Identifies differentially expressed genes (DEGs) separately for male and female samples within different cell types using the Seurat package. Compares gene expression between control and perturbed groups in each sex.

Usage

sex_stratified_analysis_sc(
  seurat_obj,
  sex_column = "sex",
  phenotype_column = "status",
  celltype_column = "cell_type",
  sex_labels_vector = c("F", "M"),
  min_logfc = 0.25,
  phenotype_labels_vector = c("WT", "TG"),
  method = "wilcox"
)

Arguments

Value

A list with male and female DEGs results.

Visualize Gene Regulatory Network with Pie Charts

Description

Plots a network with nodes represented by pie charts that display male and female effects.

Usage

visualize_network(
  g,
  female_res,
  male_res,
  vertex.size = 5,
  vertex.label.cex = 0.8,
  ...
)

Arguments

Value

The modified igraph object with visualization attributes.