Package 'gINTomics' reference manual

Title:	Multi-Omics data integration
Description:	gINTomics is an R package for Multi-Omics data integration and visualization. gINTomics is designed to detect the association between the expression of a target and of its regulators, taking into account also their genomics modifications such as Copy Number Variations (CNV) and methylation. What is more, gINTomics allows integration results visualization via a Shiny-based interactive app.
Authors:	Angelo Velle [cre, aut] , Francesco Patane' [aut] , Chiara Romualdi [aut]
Maintainer:	Angelo Velle <[email protected]>
License:	AGPL-3
Version:	1.3.0
Built:	2024-10-30 08:02:39 UTC
Source:	https://github.com/bioc/gINTomics

MultiAssayExperiment generation

Description

This function will generate a proper MultiAssayExperiment suitable for the run_multiomics function.

Usage

create_multiassay(
  methylation = NULL,
  cnv_data = NULL,
  gene_exp = NULL,
  miRNA_exp = NULL,
  miRNA_cnv_data = NULL,
  ...
)
create_multiassay(
  methylation = NULL,
  cnv_data = NULL,
  gene_exp = NULL,
  miRNA_exp = NULL,
  miRNA_cnv_data = NULL,
  ...
)

Arguments

`methylation`	Matrix or SummarizedExperiment for Methylation data
`cnv_data`	Matrix or SummarizedExperiment for genes' Copy Number Variation data
`gene_exp`	Matrix or SummarizedExperiment for Gene expression data
`miRNA_exp`	Matrix or SummarizedExperiment for miRNA expression data
`miRNA_cnv_data`	Matrix or SummarizedExperiment for miRNA's Copy Number Variations data
`...`	Additional arguments to be passed to the function

Value

A MultiAssayExperiment object containing the provided assays.

Examples

# Example usage:
library(MultiAssayExperiment)
data('mmultiassay_ov')
gene_exp_matrix <- as.matrix(assay(mmultiassay_ov[['gene_exp']]))
miRNA_exp_matrix <- as.matrix(assay(mmultiassay_ov[['miRNA_exp']]))
meth_matrix <- as.matrix(assay(mmultiassay_ov[['methylation']]))
gene_cnv_matrix <- as.matrix(assay(mmultiassay_ov[['cnv_data']]))
miRNA_cnv_matrix <- as.matrix(assay(mmultiassay_ov[['miRNA_cnv_data']]))
create_multiassay(methylation=meth_matrix, cnv_data=gene_cnv_matrix,
 gene_exp=gene_exp_matrix, miRNA_exp=miRNA_exp_matrix,
 miRNA_cnv_data=miRNA_cnv_matrix)
# Example usage:
library(MultiAssayExperiment)
data('mmultiassay_ov')
gene_exp_matrix <- as.matrix(assay(mmultiassay_ov[['gene_exp']]))
miRNA_exp_matrix <- as.matrix(assay(mmultiassay_ov[['miRNA_exp']]))
meth_matrix <- as.matrix(assay(mmultiassay_ov[['methylation']]))
gene_cnv_matrix <- as.matrix(assay(mmultiassay_ov[['cnv_data']]))
miRNA_cnv_matrix <- as.matrix(assay(mmultiassay_ov[['miRNA_cnv_data']]))
create_multiassay(methylation=meth_matrix, cnv_data=gene_cnv_matrix,
 gene_exp=gene_exp_matrix, miRNA_exp=miRNA_exp_matrix,
 miRNA_cnv_data=miRNA_cnv_matrix)

plotting enrichment

Description

plotting enrichment

Usage

dot_plotly(
  enrich_result,
  title = NULL,
  showCategory = 10,
  width = 800,
  height = 700
)
dot_plotly(
  enrich_result,
  title = NULL,
  showCategory = 10,
  width = 800,
  height = 700
)

Arguments

`enrich_result`	Enrichment analysis results.
`title`	Title of the plot.
`showCategory`	Number of categories to display.
`width`	Width of the plot.
`height`	Height of the plot.

Value

A plotly object containing the dot plot.

Examples

# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
#multiomics_integration <- run_multiomics(data = mmultiassay_ov)
#gen_enr <- run_genomic_enrich(multiomics_integration,
#                              qvalueCutoff = 1,
#                              pvalueCutoff = 0.05,
#                              pAdjustMethod = "none")
#dot_plotly(gen_enr, title = "Enrichment Analysis",showCategory = 10)
# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
#multiomics_integration <- run_multiomics(data = mmultiassay_ov)
#gen_enr <- run_genomic_enrich(multiomics_integration,
#                              qvalueCutoff = 1,
#                              pvalueCutoff = 0.05,
#                              pAdjustMethod = "none")
#dot_plotly(gen_enr, title = "Enrichment Analysis",showCategory = 10)

Setting method for extracting results

Description

Setting method for extracting results

Usage

extract_model_res(model_results, ...)

## S4 method for signature 'list'
extract_model_res(
  model_results,
  outliers = TRUE,
  species = "hsa",
  filters = c("hgnc_symbol", "ensembl_gene_id", "entrezgene_id"),
  genes_info = NULL,
  ...
)

## S4 method for signature 'MultiClass'
extract_model_res(
  model_results,
  outliers = TRUE,
  species = "hsa",
  filters = c("hgnc_symbol", "ensembl_gene_id", "entrezgene_id"),
  genes_info = NULL,
  ...
)

## S4 method for signature 'MultiOmics'
extract_model_res(
  model_results,
  outliers = TRUE,
  species = "hsa",
  filters = c("hgnc_symbol", "ensembl_gene_id", "entrezgene_id"),
  genes_info = NULL,
  ...
)
extract_model_res(model_results, ...)

## S4 method for signature 'list'
extract_model_res(
  model_results,
  outliers = TRUE,
  species = "hsa",
  filters = c("hgnc_symbol", "ensembl_gene_id", "entrezgene_id"),
  genes_info = NULL,
  ...
)

## S4 method for signature 'MultiClass'
extract_model_res(
  model_results,
  outliers = TRUE,
  species = "hsa",
  filters = c("hgnc_symbol", "ensembl_gene_id", "entrezgene_id"),
  genes_info = NULL,
  ...
)

## S4 method for signature 'MultiOmics'
extract_model_res(
  model_results,
  outliers = TRUE,
  species = "hsa",
  filters = c("hgnc_symbol", "ensembl_gene_id", "entrezgene_id"),
  genes_info = NULL,
  ...
)

Arguments

`model_results`	The model results object from which to extract results.
`...`	Additional arguments to be passed to specific methods.
`outliers`	if TRUE (by default), it removes outliers
`species`	species for the analysis
`filters`	Specific filters to apply
`genes_info`	genes info

Value

A dataframe containing the results of all the integration models provided

Examples

# example code
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
gene_cnv_matrix <- t(as.matrix(assay(mmultiassay_ov[["cnv_data"]])))
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
cnv_integration <- run_cnv_integration(
    expression = gene_exp_matrix,
    cnv_data = gene_cnv_matrix
)
data_table <- extract_model_res(cnv_integration)
head(data_table)
# example code
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
gene_cnv_matrix <- t(as.matrix(assay(mmultiassay_ov[["cnv_data"]])))
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
cnv_integration <- run_cnv_integration(
    expression = gene_exp_matrix,
    cnv_data = gene_cnv_matrix
)
data_table <- extract_model_res(cnv_integration)
head(data_table)

miRNA IDs. Dataset containing lastly definition of miRNAs (Names, Accessions, Sequences, Families and others) from different miRBase versions (From miRBase version 6 to version 22).

Description

miRNA IDs. Dataset containing lastly definition of miRNAs (Names, Accessions, Sequences, Families and others) from different miRBase versions (From miRBase version 6 to version 22).

Usage

data(mirna_hsa)
"mirna_hsa"
data(mirna_hsa)
"mirna_hsa"

Value

An object of class data.frame.

Examples

# example code
data(mirna_hsa)
head(mirna_hsa)
# example code
data(mirna_hsa)
head(mirna_hsa)

Example data for a standard workflow. This is an example dataset containing a MultiAssayExperiment of 20 ovarian cancer (OVC) patients extracted from the Cancer Genome Atlas (TCGA) database. The object contains all the available input data types: Gene expression data, miRNA expression data, gene methylation data, gene Copy Number Variations and miRNA Copy Number Variations.

Description

Example data for a standard workflow. This is an example dataset containing a MultiAssayExperiment of 20 ovarian cancer (OVC) patients extracted from the Cancer Genome Atlas (TCGA) database. The object contains all the available input data types: Gene expression data, miRNA expression data, gene methylation data, gene Copy Number Variations and miRNA Copy Number Variations.

Usage

data(mmultiassay_ov)
"mmultiomics_ov"
data(mmultiassay_ov)
"mmultiomics_ov"

Value

An object of class MultiAssayExperiment.

Examples

# example code
data(mmultiassay_ov)
mmultiassay_ov
# example code
data(mmultiassay_ov)
mmultiassay_ov

MultiClass Class

Description

S4 class containing the output of a single integration integration, for which classes has been provided. It's a list in which each element represents the result of the integration for a given class. The length will be equal to the number of classes defined.

Value

MultiOmics Class

Description

S4 class containing the output of a multiomics integration. It's a list in which each element represents the result of an integration. If all the available omics are provided, it will be a list of integrations: gene_genomic_res, mirna_cnv_res, tf_res, tf_mirna_res and mirna_target_res

Value

MultiOmics Class

plotting chr distribution

Description

plotting chr distribution

Usage

plot_chr_distribution(
  data_table,
  class = NULL,
  omics = NULL,
  cnv_met = NULL,
  pval = 0.05
)
plot_chr_distribution(
  data_table,
  class = NULL,
  omics = NULL,
  cnv_met = NULL,
  pval = 0.05
)

Arguments

`data_table`	The data table containing information for plotting chromosome distribution.
`class`	Optional. The class of interactions to include in the plot.
`omics`	Optional. The type of omics data for the plot.
`cnv_met`	Optional. The type of copy number variation or methylation data.
`pval`	Optional. The p-value threshold for significance. Default is 0.05.

Value

A histogram plot showing chromosome distribution.

Examples

# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# data_table <- extract_model_res(multiomics_integration)
# plot_chr_distribution(data_table, omics = "gene_genomic_res")
# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# data_table <- extract_model_res(multiomics_integration)
# plot_chr_distribution(data_table, omics = "gene_genomic_res")

plotting heatmap

Description

plotting heatmap

Usage

plot_heatmap(
  multiomics_integration,
  data_table,
  omics,
  scale = "none",
  genes_number = 50,
  samples_number = 50,
  class = NULL,
  pval = 0.05
)
plot_heatmap(
  multiomics_integration,
  data_table,
  omics,
  scale = "none",
  genes_number = 50,
  samples_number = 50,
  class = NULL,
  pval = 0.05
)

Arguments

`multiomics_integration`	The multiomics integration object.
`data_table`	The data table containing information for the heatmap.
`omics`	The type of omics data for the heatmap.
`scale`	Optional. The scale type for the heatmap. Default is "none".
`genes_number`	Optional. The number of genes to include in the heatmap. Default is 50.
`samples_number`	Number of samples to include in the heatmap. If this number is inferior to the total number of samples, the n most variable samples will be selected
`class`	Optional. The class of interactions to include in the heatmap.
`pval`	Optional. The p-value threshold for significance in the heatmap. Default is 0.05.

Value

A heatmap plot.

Examples

# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# data_table <- extract_model_res(multiomics_integration)
# data_table <- data_table[!is.na(data_table$cnv_met),]
# plot_heatmap(multiomics_integration, data_table, omics = "gene_genomic_res")
# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# data_table <- extract_model_res(multiomics_integration)
# data_table <- data_table[!is.na(data_table$cnv_met),]
# plot_heatmap(multiomics_integration, data_table, omics = "gene_genomic_res")

Plotting network

Description

Plotting network

Usage

plot_network(data_table, num_interactions = 300, class = NULL, pval = 0.05)
plot_network(data_table, num_interactions = 300, class = NULL, pval = 0.05)

Arguments

`data_table`	The data table containing network information.
`num_interactions`	The number of interactions to display in the network (default: 300).
`class`	Optional. The class of interactions to include in the plot.
`pval`	The p-value threshold for selecting interactions (default: 0.05).

Value

A network plot.

Examples

# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# data_table <- extract_model_res(multiomics_integration)
# plot_network(data_table)
# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# data_table <- extract_model_res(multiomics_integration)
# plot_network(data_table)

plotting ridge

Description

plotting ridge

Usage

plot_ridge(data_table, class = NULL, omics = NULL, cnv_met = NULL)
plot_ridge(data_table, class = NULL, omics = NULL, cnv_met = NULL)

Arguments

`data_table`	The data table containing information for the ridge plot.
`class`	Optional. The class of interactions to include in the ridge plot.
`omics`	Optional. The omics type for the ridge plot.
`cnv_met`	Optional. Indicates whether the ridge plot is for CNV or MET omics (only applicable if omics is specified).

Value

A ridge plot.

Examples

# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
gene_cnv_matrix <- t(as.matrix(assay(mmultiassay_ov[["cnv_data"]])))
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
cnv_integration <- run_cnv_integration(
    expression = gene_exp_matrix,
    cnv_data = gene_cnv_matrix
)
data_table <- extract_model_res(cnv_integration)
data_table <- data_table[data_table$cov!="(Intercept)",]
plot_ridge(data_table)
# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
gene_cnv_matrix <- t(as.matrix(assay(mmultiassay_ov[["cnv_data"]])))
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
cnv_integration <- run_cnv_integration(
    expression = gene_exp_matrix,
    cnv_data = gene_cnv_matrix
)
data_table <- extract_model_res(cnv_integration)
data_table <- data_table[data_table$cov!="(Intercept)",]
plot_ridge(data_table)

plotting TF distribution

Description

plotting TF distribution

Usage

plot_tf_distribution(data_table, class = NULL, pval = 0.05)
plot_tf_distribution(data_table, class = NULL, pval = 0.05)

Arguments

`data_table`	The data table containing TF information.
`class`	Optional. The class of interactions to include in the distribution plot.
`pval`	Optional. The p-value threshold for significance in the distribution plot. Default is 0.05.

Value

A TF distribution plot.

Examples

# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# data_table <- extract_model_res(multiomics_integration)
# plot_tf_distribution(data_table, pval=0.5)
# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# data_table <- extract_model_res(multiomics_integration)
# plot_tf_distribution(data_table, pval=0.5)

plotting venn

Description

plotting venn

Usage

plot_venn(data_table, class = NULL)
plot_venn(data_table, class = NULL)

Arguments

`data_table`	The data table containing information for the Venn diagram.
`class`	Optional. The class of interactions to include in the Venn diagram.

Value

A Venn diagram plot.

Examples

# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# data_table <- extract_model_res(multiomics_integration)
# plot_venn(data_table)
# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# data_table <- extract_model_res(multiomics_integration)
# plot_venn(data_table)

plotting volcano

Description

plotting volcano

Usage

plot_volcano(data_table, class = NULL, omics = NULL, cnv_met = NULL)
plot_volcano(data_table, class = NULL, omics = NULL, cnv_met = NULL)

Arguments

`data_table`	The data table containing information for the volcano plot.
`class`	Optional. The class of interactions to include in the volcano plot.
`omics`	Optional. The omics type for the volcano plot.
`cnv_met`	Optional. Indicates whether the volcano plot is for CNV or MET omics (only applicable if omics is specified).

Value

A volcano plot.

Examples

# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
multiomics_integration <- run_multiomics(data = mmultiassay_ov)
data_table <- extract_model_res(multiomics_integration)
plot_volcano(data_table, omics = "gene_genomic_res", cnv_met = "cnv")
# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
multiomics_integration <- run_multiomics(data = mmultiassay_ov)
data_table <- extract_model_res(multiomics_integration)
plot_volcano(data_table, omics = "gene_genomic_res", cnv_met = "cnv")

Integration of expression and Copy Number Variations

Description

This function will perform an integration of expression data and Copy Number Variations data

Usage

run_cnv_integration(
  expression,
  cnv_data,
  sequencing_data = TRUE,
  normalize = TRUE,
  norm_method = "TMM",
  class = NULL,
  run_deg = TRUE,
  BPPARAM = SerialParam(),
  ...
)
run_cnv_integration(
  expression,
  cnv_data,
  sequencing_data = TRUE,
  normalize = TRUE,
  norm_method = "TMM",
  class = NULL,
  run_deg = TRUE,
  BPPARAM = SerialParam(),
  ...
)

Arguments

`expression`	Matrix or data.frame containing the expression values for each model. Rows represent samples, while each column represents the different response variables of the models.
`cnv_data`	Matrix or data.frame containing the Copy Number variation status for the models. Rows represent samples, while columns represent the different covariates. If interactions are not provided, they will be automatically generated and for each gene contained in expression the model will look for the same gene in cnv_data
`sequencing_data`	logical. Are expression data obtained from RNA sequencing ? Default is set to TRUE
`normalize`	logical.Should expression data be normalized ? Default is set to TRUE
`norm_method`	Normalization method to be used for expression data. One of "TMM" (default), "TMMwsp", "RLE", "upperquartile", "none".
`class`	Character vector specifying the classes for differential expression analysis.
`run_deg`	Logical. Should differential expression analysis be performed? Default is set to TRUE.
`BPPARAM`	A BiocParallelParam object specifying the parallel backend to be used.
`...`	Additional arguments to be passed to internal functions.

Value

A list or a MultiClass object if class is provided containing the results of the CNV integration

Examples

# Example usage_multi:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
gene_cnv_matrix <- t(as.matrix(assay(mmultiassay_ov[["cnv_data"]])))
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
cnv_integration <- run_cnv_integration(
    expression = gene_exp_matrix,
    cnv_data = gene_cnv_matrix
)
# Example usage_multi:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
gene_cnv_matrix <- t(as.matrix(assay(mmultiassay_ov[["cnv_data"]])))
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
cnv_integration <- run_cnv_integration(
    expression = gene_exp_matrix,
    cnv_data = gene_cnv_matrix
)

Running genomic enrichment analysis

Description

Running genomic enrichment analysis

Usage

run_genomic_enrich(
  model_results,
  species = "hsa",
  pvalueCutoff = 0.1,
  pAdjustMethod = "BH",
  qvalueCutoff = 0.1,
  ont = "all",
  BPPARAM = BiocParallel::SerialParam(),
  extracted_data = NULL,
  ...
)
run_genomic_enrich(
  model_results,
  species = "hsa",
  pvalueCutoff = 0.1,
  pAdjustMethod = "BH",
  qvalueCutoff = 0.1,
  ont = "all",
  BPPARAM = BiocParallel::SerialParam(),
  extracted_data = NULL,
  ...
)

Arguments

`model_results`	Model integration results, typically a list containing different types of genomic results
`species`	Species to select for the enrichment analysis. Default is 'hsa' (Homo sapiens).
`pvalueCutoff`	P-value cutoff for significant enrichment. Default is 0.1.
`pAdjustMethod`	Method for adjusting p-values. Default is 'BH' (Benjamini & Hochberg).
`qvalueCutoff`	Q-value cutoff for significant enrichment. Default is 0.1.
`ont`	Ontology to use for the enrichment analysis. Default is 'all'.
`BPPARAM`	A BiocParallelParam object specifying parallelization options. Default is BiocParallel::SerialParam().
`extracted_data`	Pre-extracted data for enrichment analysis. If NULL, function will extract relevant data from model_results.
`...`	Additional arguments to be passed to the internal enrichment function.

Value

A list containing enrichment results. If CNV and methylation data are available, it returns a nested list with results for each data type.

Examples

# Example usage:
library(MultiAssayExperiment)
data(mmultiassay_ov)
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:200,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
#multiomics_integration <- run_multiomics(mmultiassay_ov)
#gen_enr <- run_genomic_enrich(multiomics_integration, qvalueCutoff = 1,
#pvalueCutoff = 0.05, pAdjustMethod = 'none')
# Example usage:
library(MultiAssayExperiment)
data(mmultiassay_ov)
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:200,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
#multiomics_integration <- run_multiomics(mmultiassay_ov)
#gen_enr <- run_genomic_enrich(multiomics_integration, qvalueCutoff = 1,
#pvalueCutoff = 0.05, pAdjustMethod = 'none')

Integration of expression, Copy Number Variations and methylation data

Description

This function will perform an integration of expression data and Copy Number Variations data

Usage

run_genomic_integration(
  expression,
  cnv_data,
  methylation,
  sequencing_data = TRUE,
  normalize = TRUE,
  norm_method = "TMM",
  interactions = NULL,
  class = NULL,
  scale = TRUE,
  run_deg = TRUE,
  BPPARAM = SerialParam(),
  ...
)
run_genomic_integration(
  expression,
  cnv_data,
  methylation,
  sequencing_data = TRUE,
  normalize = TRUE,
  norm_method = "TMM",
  interactions = NULL,
  class = NULL,
  scale = TRUE,
  run_deg = TRUE,
  BPPARAM = SerialParam(),
  ...
)

Arguments

`expression`	Matrix or data.frame containing the expression values for each model. Rows represent samples, while each column represents the different response variables of the models.
`cnv_data`	Matrix or data.frame containing the Copy Number variation status for the models. Rows represent samples, while columns represent the different covariates. If interactions are not provided, they will be automatically generated and for each gene contained in expression the model will look for the same gene in cnv_data
`methylation`	Matrix or data.frame containing the methylation values for the models. Rows represent samples, while columns represent the different covariates. If interactions are not provided, they will be automatically generated and for each gene contained in expression the model will look for the same gene in methylation
`sequencing_data`	logical. Are expression data obtained from RNA sequencing ? Default is set to TRUE
`normalize`	logical.Should expression data be normalized ? Default is set to TRUE
`norm_method`	Normalization method to be used for expression data. One of "TMM" (default), "TMMwsp", "RLE", "upperquartile", "none".
`interactions`	A list of character vectors containing the interactions between response variable and covariates. The names of the list should match the response variables while the character contained in each element of the list should match the covariates. If NULL (default), the interactions will be automatically defined according to response variable's colnames.
`class`	Character vector specifying the classes for differential expression analysis.
`scale`	Logical. Should the data be scaled? Default is set to TRUE.
`run_deg`	Logical. Should differential expression analysis be performed? Default is set to TRUE.
`BPPARAM`	A BiocParallelParam object specifying the parallel backend to be used.
`...`	Additional arguments to be passed to internal functions.

Value

A list or a MultiClass object if class is provided containing the results of the Genomic integration

Examples

# Example usage_multi:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
meth_matrix <- t(as.matrix(assay(mmultiassay_ov[["methylation"]])))
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
gene_cnv_matrix <- t(as.matrix(assay(mmultiassay_ov[["cnv_data"]])))
genomic_integration <- run_genomic_integration(
    expression = gene_exp_matrix,
    cnv_data = gene_cnv_matrix, methylation = meth_matrix
)
# Example usage_multi:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
meth_matrix <- t(as.matrix(assay(mmultiassay_ov[["methylation"]])))
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
gene_cnv_matrix <- t(as.matrix(assay(mmultiassay_ov[["cnv_data"]])))
genomic_integration <- run_genomic_integration(
    expression = gene_exp_matrix,
    cnv_data = gene_cnv_matrix, methylation = meth_matrix
)

Integration of expression and methylation

Description

This function will perform an integration of expression data and methylation data

Usage

run_met_integration(
  expression,
  methylation,
  sequencing_data = TRUE,
  normalize = TRUE,
  norm_method = "TMM",
  class = NULL,
  run_deg = TRUE,
  BPPARAM = SerialParam(),
  ...
)
run_met_integration(
  expression,
  methylation,
  sequencing_data = TRUE,
  normalize = TRUE,
  norm_method = "TMM",
  class = NULL,
  run_deg = TRUE,
  BPPARAM = SerialParam(),
  ...
)

Arguments

`expression`	Matrix or data.frame containing the expression values for each model. Rows represent samples, while each column represents the different response variables of the models.
`methylation`	Matrix or data.frame containing the methylation values for the models. Rows represent samples, while columns represent the different covariates. If interactions are not provided, they will be automatically generated and for each gene contained in expression the model will look for the same gene in methylation
`sequencing_data`	logical. Are expression data obtained from RNA sequencing ? Default is set to TRUE
`normalize`	logical.Should expression data be normalized ? Default is set to TRUE
`norm_method`	Normalization method to be used for expression data. One of "TMM" (default), "TMMwsp", "RLE", "upperquartile", "none".
`class`	Character vector specifying the classes for differential expression analysis.
`run_deg`	Logical. Should differential expression analysis be performed? Default is set to TRUE.
`BPPARAM`	A BiocParallelParam object specifying the parallel backend to be used.
`...`	Additional arguments to be passed to internal functions.

Value

A list or a MultiClass object if class is provided containing the results of the Methylation integration

Examples

# Example usage_multi:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
meth_matrix <- t(as.matrix(assay(mmultiassay_ov[["methylation"]])))
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
met_integration <- run_met_integration(
    expression = gene_exp_matrix,
    methylation = meth_matrix
)
# Example usage_multi:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
meth_matrix <- t(as.matrix(assay(mmultiassay_ov[["methylation"]])))
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
met_integration <- run_met_integration(
    expression = gene_exp_matrix,
    methylation = meth_matrix
)

Complete Multi-Omics integration

Description

This function will perform a complete Multi-Omics integration on a MultiAssayExperiment

Usage

run_multiomics(
  data,
  interactions_met = NULL,
  interactions_miRNA_target = NULL,
  interactions_tf = NULL,
  interactions_tf_miRNA = NULL,
  RNAseq = TRUE,
  miRNAseq = TRUE,
  normalize_miRNA_expr = TRUE,
  normalize_gene_expr = TRUE,
  norm_method_gene_expr = "TMM",
  norm_method_miRNA_expr = "TMM",
  class = NULL,
  BPPARAM = SerialParam()
)
run_multiomics(
  data,
  interactions_met = NULL,
  interactions_miRNA_target = NULL,
  interactions_tf = NULL,
  interactions_tf_miRNA = NULL,
  RNAseq = TRUE,
  miRNAseq = TRUE,
  normalize_miRNA_expr = TRUE,
  normalize_gene_expr = TRUE,
  norm_method_gene_expr = "TMM",
  norm_method_miRNA_expr = "TMM",
  class = NULL,
  BPPARAM = SerialParam()
)

Arguments

`data`	A MultiAssayExperiment. It can be generated exploiting the generate_multiassay function.
`interactions_met`	interactions as for run_met_integration
`interactions_miRNA_target`	miRNA-target interactions as requested by run_tf_integration
`interactions_tf`	TF-target interactions as requested by run_tf_integration
`interactions_tf_miRNA`	TF-target interactions as requested by run_tf_integration
`RNAseq`	logical. Are gene expression data obtained from RNA sequencing ? Default is set to TRUE
`miRNAseq`	logical. Are miRNA expression data obtained from miRNA sequencing ? Default is set to TRUE
`normalize_miRNA_expr`	logical.Should miRNA expression data be normalized ? Default is set to TRUE
`normalize_gene_expr`	logical.Should gene expression data be normalized ? Default is set to TRUE
`norm_method_gene_expr`	Normalization method to be used for gene expression data. One of "TMM" (default), "TMMwsp", "RLE", "upperquartile", "none".
`norm_method_miRNA_expr`	Normalization method to be used for miRNA expression data. One of "TMM" (default), "TMMwsp", "RLE", "upperquartile", "none".
`class`	Character vector specifying the classes for differential expression analysis.
`BPPARAM`	A BiocParallelParam object specifying the parallel backend to be used.

Value

A MultiOmics object containing the results of all the possible integration models

Examples

# Example usage_multiomics:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# Example usage_multiomics:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
multiomics_integration <- run_multiomics(data = mmultiassay_ov)

Start a Shiny application for integrated multi-omics data analysis.

Description

The run_shiny function launches an interactive Shiny application that allows users to explore and analyze integrated multi-omics data through various visualizations and analyses.

Usage

run_shiny(multiomics_integration)
run_shiny(multiomics_integration)

Arguments

multiomics_integration

An object representing the integration of multi-omics data, compatible with the extract_model_res function.

Details

The run_shiny function extracts model results from multiomics_integration, performs preprocessing operations to prepare the data for the Shiny user interface, creates the user interface and server for the Shiny application.

Value

No return value. The function starts an interactive Shiny application.

References

Description of the multi-omics data model and integrated analysis techniques used.

Examples

# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# app <- run_shiny(multiomics_integration)

# Example usage:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
# multiomics_integration <- run_multiomics(data = mmultiassay_ov)
# app <- run_shiny(multiomics_integration)

Running TF enrichment analysis

Description

Running TF enrichment analysis

Usage

run_tf_enrich(
  model_results,
  species = "hsa",
  pvalueCutoff = 0.1,
  qvalueCutoff = 0.1,
  pAdjustMethod = "BH",
  ont = "all",
  BPPARAM = BiocParallel::SerialParam(),
  extracted_data = NULL,
  ...
)
run_tf_enrich(
  model_results,
  species = "hsa",
  pvalueCutoff = 0.1,
  qvalueCutoff = 0.1,
  pAdjustMethod = "BH",
  ont = "all",
  BPPARAM = BiocParallel::SerialParam(),
  extracted_data = NULL,
  ...
)

Arguments

`model_results`	Model integration results, typically a list containing TF data.
`species`	Species to select for the enrichment analysis. Default is 'hsa' (Homo sapiens).
`pvalueCutoff`	P-value cutoff for significant enrichment. Default is 0.1.
`qvalueCutoff`	Q-value cutoff for significant enrichment. Default is 0.1.
`pAdjustMethod`	Method for adjusting p-values. Default is 'BH' (Benjamini & Hochberg).
`ont`	Ontology to use for the enrichment analysis. Default is 'all'.
`BPPARAM`	A BiocParallelParam object specifying parallelization options. Default is BiocParallel::SerialParam().
`extracted_data`	Pre-extracted data for enrichment analysis. If NULL, function will extract relevant data from model_results.
`...`	Additional arguments to be passed to the internal enrichment function.

Value

A list containing TF enrichment results.

Examples

# Example usage:
library(MultiAssayExperiment)
data(mmultiassay_ov)
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:200,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
#multiomics_integration <- run_multiomics(mmultiassay_ov)
#run_tf_enrich(multiomics_integration, qvalueCutoff = 1, pvalueCutoff = 0.05,
#pAdjustMethod = 'none')
# Example usage:
library(MultiAssayExperiment)
data(mmultiassay_ov)
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:200,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
#multiomics_integration <- run_multiomics(mmultiassay_ov)
#run_tf_enrich(multiomics_integration, qvalueCutoff = 1, pvalueCutoff = 0.05,
#pAdjustMethod = 'none')

Integration of expression and Transcription Factors / Generic Regulators

Description

This function will perform an integration of gene/miRNA expression data and Transcription Factors expression. Moreover, every type of regulator can be provided to the function as covariate through the tf_expression argument. Interactions for TF-target, miRNA-target and TF-miRNA integration will be automatically downloaded by the function as defined by the type argument. Other types of interactions should be provided through the interactions argument.

Usage

run_tf_integration(
  expression,
  tf_expression = expression,
  interactions = NULL,
  type = "none",
  sequencing_data = TRUE,
  species = "hsa",
  normalize = TRUE,
  norm_method = "TMM",
  normalize_cov = TRUE,
  norm_method_cov = "TMM",
  class = NULL,
  run_deg = TRUE,
  BPPARAM = SerialParam(),
  ...
)
run_tf_integration(
  expression,
  tf_expression = expression,
  interactions = NULL,
  type = "none",
  sequencing_data = TRUE,
  species = "hsa",
  normalize = TRUE,
  norm_method = "TMM",
  normalize_cov = TRUE,
  norm_method_cov = "TMM",
  class = NULL,
  run_deg = TRUE,
  BPPARAM = SerialParam(),
  ...
)

Arguments

`expression`	Matrix or data.frame containing the expression values for each model. Rows represent samples, while each column represents the different response variables of the models.
`tf_expression`	Matrix or data.frame containing the expression values for the models. Rows represent samples, while columns represent the different covariates. If not provided, it will be set equal to expression.
`interactions`	A list of character vectors containing the interactions between response variable and covariates. The names of the list should match the response variables while the character contained in each element of the list should match the covariates. If NULL (default), the interactions will be automatically downloaded according to the type argument.
`type`	A character defining the type of regulation under analysis. Should be one of "tf_miRNA", "tf", "miRNA_target".
`sequencing_data`	logical. Are expression data obtained from RNA sequencing ? Default is set to TRUE
`species`	species information for interactions download. Fully supported species are "hsa" (default) and "mmu".
`normalize`	logical.Should expression data be normalized ? Default is set to TRUE
`norm_method`	Normalization method to be used for expression data. One of "TMM" (default), "TMMwsp", "RLE", "upperquartile", "none".
`normalize_cov`	Same as normalize but for covariates.
`norm_method_cov`	Same as norm_method but for covariates.
`class`	Character vector specifying the classes for differential expression analysis.
`run_deg`	Logical. Should differential expression analysis be performed? Default is set to TRUE.
`BPPARAM`	A BiocParallelParam object specifying the parallel backend to be used.
`...`	Additional arguments to be passed to internal functions.

Value

A list or a MultiClass object if class is provided containing the results of the transcriptional integration

Examples

# Example usage_multi:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
tf_integration <- run_tf_integration(expression = gene_exp_matrix, type="tf")
# Example usage_multi:
library(MultiAssayExperiment)
data("mmultiassay_ov")
tmp <- lapply(experiments(mmultiassay_ov), function(x) x[1:20,])
mmultiassay_ov <- MultiAssayExperiment(experiments = tmp)
gene_exp_matrix <- t(as.matrix(assay(mmultiassay_ov[["gene_exp"]])))
tf_integration <- run_tf_integration(expression = gene_exp_matrix, type="tf")

Package 'gINTomics'

Help Index

MultiAssayExperiment generation

Description

Usage

Arguments

Value

Examples

plotting enrichment

Description

Usage

Arguments

Value

Examples

Setting method for extracting results

Description

Usage

Arguments

Value

Examples

miRNA IDs. Dataset containing lastly definition of miRNAs (Names, Accessions, Sequences, Families and others) from different miRBase versions (From miRBase version 6 to version 22).

Description

Usage

Value

Examples

Description

Usage

Value

Examples

MultiClass Class

Description

Value

MultiOmics Class

Description

Value

plotting chr distribution

Description

Usage

Arguments

Value

Examples

plotting heatmap

Description

Usage

Arguments

Value

Examples

Plotting network

Description

Usage

Arguments

Value

Examples

plotting ridge

Description

Usage

Arguments

Value

Examples

plotting TF distribution

Description

Usage

Arguments

Value

Examples

plotting venn

Description

Usage

Arguments

Value

Examples

plotting volcano

Description

Usage

Arguments

Value

Examples

Integration of expression and Copy Number Variations

Description

Usage