Package 'CEMiTool' reference manual

Title:	Co-expression Modules identification Tool
Description:	The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network.
Authors:	Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger [aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata [aut], Melissa Lever [aut], Helder Nakaya [aut, cre]
Maintainer:	Helder Nakaya <[email protected]>
License:	GPL-3
Version:	1.29.0
Built:	2024-07-25 05:47:54 UTC
Source:	https://github.com/bioc/CEMiTool

Get or set adjacency matrix value

Description

This function takes a CEMiTool object containing expression values and returns a CEMiTool object with an adjacency matrix in the adjacency slot.

Usage

adj_data(cem, ...)

## S4 method for signature 'CEMiTool'
adj_data(cem)

adj_data(cem) <- value

## S4 replacement method for signature 'CEMiTool'
adj_data(cem) <- value
adj_data(cem, ...)

## S4 method for signature 'CEMiTool'
adj_data(cem)

adj_data(cem) <- value

## S4 replacement method for signature 'CEMiTool'
adj_data(cem) <- value

Arguments

`cem`	Object of class `CEMiTool`
`...`	Optional parameters.
`value`	Object of class `matrix` containing adjacency data. Only used for setting adjacency values to CEMiTool object.

Value

Object of class matrix with adjacency values or object of class CEMiTool.

Examples

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Calculate adjacency matrix with example beta value 8
cem <- get_adj(cem, beta=8)
# Return adjacency matrix
adj <- adj_data(cem)
# Check result
adj[1:5, 1:5]
# Set adjacency matrix to CEMiTool object
adj_data(cem) <- adj

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Calculate adjacency matrix with example beta value 8
cem <- get_adj(cem, beta=8)
# Return adjacency matrix
adj <- adj_data(cem)
# Check result
adj[1:5, 1:5]
# Set adjacency matrix to CEMiTool object
adj_data(cem) <- adj

CEMiTool Object

Description

This object can be used as input for CEMiTool functions. Data used are from expr and sample_annot.

Usage

data(cem)
data(cem)

Format

An object of class CEMiTool

Examples

# Get example CEMiTool object
data(cem)
cem
# Get example CEMiTool object
data(cem)
cem

Full gene co-expression analysis

Description

Defines co-expression modules and runs several different analyses.

Usage

cemitool(
  expr,
  annot,
  gmt,
  interactions,
  filter = TRUE,
  filter_pval = 0.1,
  apply_vst = FALSE,
  n_genes,
  eps = 0.1,
  cor_method = c("pearson", "spearman"),
  cor_function = "cor",
  network_type = "unsigned",
  tom_type = "signed",
  set_beta = NULL,
  force_beta = FALSE,
  sample_name_column = "SampleName",
  class_column = "Class",
  merge_similar = TRUE,
  rank_method = "mean",
  ora_pval = 0.05,
  gsea_scale = TRUE,
  gsea_min_size = 15,
  gsea_max_size = 1000,
  min_ngen = 30,
  diss_thresh = 0.8,
  plot = TRUE,
  plot_diagnostics = TRUE,
  order_by_class = TRUE,
  center_func = "mean",
  directed = FALSE,
  verbose = FALSE
)
cemitool(
  expr,
  annot,
  gmt,
  interactions,
  filter = TRUE,
  filter_pval = 0.1,
  apply_vst = FALSE,
  n_genes,
  eps = 0.1,
  cor_method = c("pearson", "spearman"),
  cor_function = "cor",
  network_type = "unsigned",
  tom_type = "signed",
  set_beta = NULL,
  force_beta = FALSE,
  sample_name_column = "SampleName",
  class_column = "Class",
  merge_similar = TRUE,
  rank_method = "mean",
  ora_pval = 0.05,
  gsea_scale = TRUE,
  gsea_min_size = 15,
  gsea_max_size = 1000,
  min_ngen = 30,
  diss_thresh = 0.8,
  plot = TRUE,
  plot_diagnostics = TRUE,
  order_by_class = TRUE,
  center_func = "mean",
  directed = FALSE,
  verbose = FALSE
)

Arguments

`expr`	Gene expression `data.frame`.
`annot`	Sample annotation `data.frame`.
`gmt`	A data.frame containing two columns, one with pathways and one with genes
`interactions`	A data.frame containing two columns with gene names.
`filter`	Logical. If TRUE, will filter expression data.
`filter_pval`	P-value threshold for filtering. Default `0.1`.
`apply_vst`	Logical. If TRUE, will apply Variance Stabilizing Transform before filtering genes. Currently ignored if parameter `filter` is FALSE.
`n_genes`	Number of genes left after filtering.
`eps`	A value for accepted R-squared interval between subsequent beta values. Default is 0.1.
`cor_method`	A character string indicating which correlation coefficient is to be computed. One of `"pearson"` or `"spearman"`. Default is `"pearson"`.
`cor_function`	A character string indicating the correlation function to be used. Supported functions are currently 'cor' and 'bicor'. Default is `"cor"`
`network_type`	A character string indicating if network type should be computed as `"signed"` or `"unsigned"`. Default is `"unsigned"`
`tom_type`	A character string indicating if the TOM type should be computed as `"signed"` or `"unsigned"`. Default is `"signed"`
`set_beta`	A value to override the automatically selected beta value. Default is NULL.
`force_beta`	Whether or not to automatically force a beta value based on number of samples. Default is FALSE.
`sample_name_column`	A character string indicating the sample column name of the annotation table.
`class_column`	A character string indicating the class column name of the annotation table.
`merge_similar`	Logical. If `TRUE`, merge similar modules.
`rank_method`	Character string indicating how to rank genes. Either "mean" (the default) or "median".
`ora_pval`	P-value for overrepresentation analysis. Default `0.05`.
`gsea_scale`	If TRUE, apply z-score transformation for GSEA analysis. Default is `TRUE`
`gsea_min_size`	Minimum size of gene sets for GSEA analysis. Default is `15`
`gsea_max_size`	Maximum size of gene sets for GSEA analysis. Default is `1000`
`min_ngen`	Minimal number of genes per submodule. Default `30`.
`diss_thresh`	Module merging correlation threshold for eigengene similarity. Default `0.8`.
`plot`	Logical. If `TRUE`, plots all figures.
`plot_diagnostics`	Logical. If `TRUE`, creates diagnostic plots. Overwritten if plot=FALSE.
`order_by_class`	Logical. If `TRUE`, samples in profile plot are ordered by the groups defined by the class_column slot in the sample annotation file. Ignored if there is no sample_annotation file. Default `TRUE`.
`center_func`	Character string indicating the centrality measure to show in the plot. Either 'mean' (the default) or 'median'.
`directed`	Logical. If `TRUE`, the igraph objects in interactions slot will be directed.
`verbose`	Logical. If `TRUE`, reports analysis steps.

Value

Object of class CEMiTool

Examples

# Get example expression data
data(expr0)
# Run CEMiTool analyses
cem <- cemitool(expr=expr0)
# Run CEMiTool applying Variance Stabilizing Transformation to data
cem <- cemitool(expr=expr0, apply_vst=TRUE)
# Run CEMiTool with additional processing messages
cem <- cemitool(expr=expr0, verbose=TRUE)

## Not run: 
# Run full CEMiTool analysis
## Get example sample annotation data
data(sample_annot)
## Read example pathways file
gmt_fname <- system.file("extdata", "pathways.gmt", package = "CEMiTool")
gmt_in <- read_gmt(gmt_fname)
## Get example interactions file
int_df <- read.delim(system.file("extdata", "interactions.tsv", package = "CEMiTool"))
## Run CEMiTool
cem <- cemitool(expr=expr0, annot=sample_annot, gmt=gmt_in,
    interactions=int_df, verbose=TRUE, plot=TRUE)

# Create report as html file
generate_report(cem, directory = "./Report", output_format="html_document")

# Write analysis results into files
write_files(cem, directory="./Tables", force=TRUE)

# Save all plots
save_plots(cem, "all", directory="./Plots")

## End(Not run)
# Get example expression data
data(expr0)
# Run CEMiTool analyses
cem <- cemitool(expr=expr0)
# Run CEMiTool applying Variance Stabilizing Transformation to data
cem <- cemitool(expr=expr0, apply_vst=TRUE)
# Run CEMiTool with additional processing messages
cem <- cemitool(expr=expr0, verbose=TRUE)

## Not run: 
# Run full CEMiTool analysis
## Get example sample annotation data
data(sample_annot)
## Read example pathways file
gmt_fname <- system.file("extdata", "pathways.gmt", package = "CEMiTool")
gmt_in <- read_gmt(gmt_fname)
## Get example interactions file
int_df <- read.delim(system.file("extdata", "interactions.tsv", package = "CEMiTool"))
## Run CEMiTool
cem <- cemitool(expr=expr0, annot=sample_annot, gmt=gmt_in,
    interactions=int_df, verbose=TRUE, plot=TRUE)

# Create report as html file
generate_report(cem, directory = "./Report", output_format="html_document")

# Write analysis results into files
write_files(cem, directory="./Tables", force=TRUE)

# Save all plots
save_plots(cem, "all", directory="./Plots")

## End(Not run)

An S4 class to represent the CEMiTool analysis.

Description

An S4 class to represent the CEMiTool analysis.

Slots

expression: Gene expression data.frame.
sample_annotation: Sample annotation data.frame.
fit_indices: data.frame containing scale-free model fit, soft-threshold and network parameters.
selected_genes: Character vector containing the names of genes selected for analysis
module: Genes in modules information data.frame.
enrichment: list with modules enrichment results for sample classes.
ora: Over-representation analysis results data.frame.
interactions: list containing gene interactions present in modules.
interaction_plot: list of ggplot graphs with module gene interactions.
profile_plot: list of ggplot graphs with gene expression profile per module.
enrichment_plot: ggplot graph for enrichment analysis results.
beta_r2_plot: ggplot graph with scale-free topology fit results for each soft-threshold.
mean_k_plot: ggplot graph with mean network connectivity.
barplot_ora: list of ggplot graphs with over-representation analysis results per module.
sample_tree_plot: gtable containing sample dendrogram with class labels and clinical data (if available in sample_annotation(cem)).
mean_var_plot: Mean x variance scatterplot.
hist_plot: Expression histogram.
qq_plot: Quantile-quantile plot.
sample_name_column: character string containing the name of the column with sample names in the annotation file.
class_column: character string containing the name of the column with class names in the annotation file.
mod_colors: character vector containing colors associated with each network module.
parameters: list containing analysis parameters.
adjacency: matrix containing gene adjacency values based on correlation

Examples

# Get example expression data
data(expr0)
# Initialize CEMiTool object with expression
cem <- new("CEMiTool", expression=expr0)
# Get example expression data
data(expr0)
# Initialize CEMiTool object with expression
cem <- new("CEMiTool", expression=expr0)

Diagnostic report

Description

Creates report for identifying potential problems with data.

Usage

diagnostic_report(cem, ...)

## S4 method for signature 'CEMiTool'
diagnostic_report(
  cem,
  title = "Diagnostics",
  directory = "./Reports/Diagnostics",
  force = FALSE,
  ...
)
diagnostic_report(cem, ...)

## S4 method for signature 'CEMiTool'
diagnostic_report(
  cem,
  title = "Diagnostics",
  directory = "./Reports/Diagnostics",
  force = FALSE,
  ...
)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	parameters to rmarkdown::render
`title`	Character string with the title of the report.
`directory`	Directory name for results.
`force`	If the directory exists, execution will not stop.

Value

An HTML file with an interactive diagnostic report.

Retrieve and set expression attribute

Description

Retrieve and set expression attribute

Usage

expr_data(cem, ...)

## S4 method for signature 'CEMiTool'
expr_data(cem, filter = TRUE, apply_vst = FALSE, filter_pval = 0.1, ...)

expr_data(cem) <- value

## S4 replacement method for signature 'CEMiTool'
expr_data(cem) <- value
expr_data(cem, ...)

## S4 method for signature 'CEMiTool'
expr_data(cem, filter = TRUE, apply_vst = FALSE, filter_pval = 0.1, ...)

expr_data(cem) <- value

## S4 replacement method for signature 'CEMiTool'
expr_data(cem) <- value

Arguments

`cem`	Object of class `CEMiTool`
`...`	Additional parameters to `filter_genes` or `select_genes` functions.
`filter`	logical. If TRUE, retrieves filtered expression data (Default: TRUE)
`apply_vst`	logical. If TRUE, applies variance stabilizing transformation to expression data (Default: FALSE)
`filter_pval`	logical. Threshold for filter p-value. Ignored if filter = FALSE (Default: 0.1)
`value`	Object of class `data.frame` with gene expression data

Value

Object of class data.frame with gene expression data

Examples

# Initialize an empty CEMiTool object
cem <- new_cem()
# Get example expression data
data(expr0)
# Add expression file to CEMiTool object
expr_data(cem) <- expr0
# Check expression file
head(expr_data(cem))
# Initialize an empty CEMiTool object
cem <- new_cem()
# Get example expression data
data(expr0)
# Add expression file to CEMiTool object
expr_data(cem) <- expr0
# Check expression file
head(expr_data(cem))

Yellow Fever gene expression data from GEO study GSE13485

Description

Modified data from a yellow fever vaccination study by Querec et al, 2009. In order to reduce package size, only the 4000 genes with the highest variance were selected for this dataset.

Usage

data(expr0)
data(expr0)

Format

An object of class data.frame

Source

GEO

References

Querec TD, Akondy RS, Lee EK, Cao W et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol 2009 Jan;10(1):116-25. PMID: 19029902 PubMed

Examples

data(expr0)
# Run CEMiTool analysis
## Not run: cemitool(expr0)
data(expr0)
# Run CEMiTool analysis
## Not run: cemitool(expr0)

Filter a gene expression data.frame

Description

Filter a gene expression data.frame

Usage

filter_genes(expr, pct = 0.75, apply_vst = FALSE)
filter_genes(expr, pct = 0.75, apply_vst = FALSE)

Arguments

`expr`	A data.frame containing expression data
`pct`	Percentage of most expressed genes to keep.
`apply_vst`	Logical. If TRUE, will apply variance stabilizing transform before filtering data.

Details

This function takes a gene expression data.frame and applies a percentage filter (keeps the pct) most expressed genes). If apply_vst is TRUE, a variance stabilizing transformation is applied on gene expression values as long as mean and variance values have a Spearman's rho of over 0.5. This transformation is intended to remove this dependence between the parameters. One should then apply the select_genes function to get significant genes.

Value

A data.frame containing filtered expression data

Examples

# Get example expression data
data(expr0)
# Filter genes
expr_f <- filter_genes(expr0)
# Check selected genes
expr_f[1:5, 1:5]
# Filter genes and apply variance stabilizing transformation
expr_f2 <- filter_genes(expr0, apply_vst=TRUE)
# Check results
expr_f2[1:5, 1:5]
# Selected genes
selected <- select_genes(expr_f2)
# Get data.frame with only selected genes
expr_s <- expr_f2[selected, ]
# Check results
expr_s[1:5, 1:5]
# Get example expression data
data(expr0)
# Filter genes
expr_f <- filter_genes(expr0)
# Check selected genes
expr_f[1:5, 1:5]
# Filter genes and apply variance stabilizing transformation
expr_f2 <- filter_genes(expr0, apply_vst=TRUE)
# Check results
expr_f2[1:5, 1:5]
# Selected genes
selected <- select_genes(expr_f2)
# Get data.frame with only selected genes
expr_s <- expr_f2[selected, ]
# Check results
expr_s[1:5, 1:5]

Co-expression modules definition

Description

Defines co-expression modules

Usage

find_modules(cem, ...)

## S4 method for signature 'CEMiTool'
find_modules(
  cem,
  cor_method = c("pearson", "spearman"),
  cor_function = "cor",
  eps = 0.1,
  set_beta = NULL,
  force_beta = FALSE,
  min_ngen = 20,
  merge_similar = TRUE,
  network_type = "unsigned",
  tom_type = "signed",
  diss_thresh = 0.8,
  verbose = FALSE
)
find_modules(cem, ...)

## S4 method for signature 'CEMiTool'
find_modules(
  cem,
  cor_method = c("pearson", "spearman"),
  cor_function = "cor",
  eps = 0.1,
  set_beta = NULL,
  force_beta = FALSE,
  min_ngen = 20,
  merge_similar = TRUE,
  network_type = "unsigned",
  tom_type = "signed",
  diss_thresh = 0.8,
  verbose = FALSE
)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`cor_method`	A character string indicating which correlation coefficient is to be computed. Default `"pearson"`
`cor_function`	A character string indicating the correlation function to be used. Default `'cor'`.
`eps`	A value for accepted R-squared interval between subsequent beta values. Default is 0.1.
`set_beta`	A value to override the automatically selected beta value. Default is NULL.
`force_beta`	Whether or not to automatically force a beta value based on number of samples. Default is FALSE.
`min_ngen`	Minimal number of genes per submodule. Default `20`.
`merge_similar`	Logical. If `TRUE`, (the default) merge similar modules.
`network_type`	A character string indicating to use either "unsigned" (default) or "signed" networks. Default `"unsigned"`
`tom_type`	A character string indicating to use either "unsigned" or "signed" (default) TOM similarity measure.
`diss_thresh`	Module merging correlation threshold for eigengene similarity. Default `0.8`.
`verbose`	Logical. If `TRUE`, reports analysis steps. Default `FALSE`

Value

Object of class CEMiTool

Examples

# Get example expression data
data(expr0)
# Initialize CEMiTool object with expression
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Define network modules
cem <- find_modules(cem)
# Check results
head(module_genes(cem))

# Get example expression data
data(expr0)
# Initialize CEMiTool object with expression
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Define network modules
cem <- find_modules(cem)
# Check results
head(module_genes(cem))

Retrieve scale-free model fit data

Description

Retrieve scale-free model fit data

Usage

fit_data(cem)

## S4 method for signature 'CEMiTool'
fit_data(cem)
fit_data(cem)

## S4 method for signature 'CEMiTool'
fit_data(cem)

Arguments

cem

Object of class CEMiTool

Value

Object of class data.frame

Examples

# Get example CEMiTool object
data(cem)
# Get modules and beta data
cem <- find_modules(cem)
# Get fit data
fit_data(cem)

# Get example CEMiTool object
data(cem)
# Get modules and beta data
cem <- find_modules(cem)
# Get fit data
fit_data(cem)

CEMiTool report

Description

Creates report for CEMiTool results

Usage

generate_report(cem, ...)

## S4 method for signature 'CEMiTool'
generate_report(
  cem,
  max_rows_ora = 50,
  title = "Report",
  directory = "./Reports/Report",
  force = FALSE,
  ...
)
generate_report(cem, ...)

## S4 method for signature 'CEMiTool'
generate_report(
  cem,
  max_rows_ora = 50,
  title = "Report",
  directory = "./Reports/Report",
  force = FALSE,
  ...
)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	parameters to rmarkdown::render
`max_rows_ora`	maximum number of rows in Over Representation Analysis table results
`title`	Character string with the title of the report.
`directory`	Directory name for results.
`force`	If the directory exists, execution will not stop.

Value

An HTML file with an interactive report of CEMiTool analyses.

Examples

## Not run:   
# Get example CEMiTool object
data(cem)
generate_report(cem, output_format=c("pdf_document", "html_document"))

## End(Not run)

## Not run:   
# Get example CEMiTool object
data(cem)
generate_report(cem, output_format=c("pdf_document", "html_document"))

## End(Not run)

Calculate adjacency matrix

Description

This function takes a CEMiTool object and returns an adjacency matrix.

Usage

get_adj(cem, ...)

## S4 method for signature 'CEMiTool'
get_adj(
  cem,
  beta,
  network_type = "unsigned",
  cor_function = "cor",
  cor_method = "pearson"
)
get_adj(cem, ...)

## S4 method for signature 'CEMiTool'
get_adj(
  cem,
  beta,
  network_type = "unsigned",
  cor_function = "cor",
  cor_method = "pearson"
)

Arguments

`cem`	Object of class `CEMiTool`
`...`	Optional parameters.
`beta`	Selected soft-threshold value
`network_type`	A character string indicating to use either "unsigned" (default) or "signed" networks. Default `"unsigned"`.
`cor_function`	A character string indicating the correlation function to be used. Default `'cor'`.
`cor_method`	A character string indicating which correlation coefficient is to be computed. Default `"pearson"`.

Value

Object of class CEMiTool with adjacency data

Examples

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Calculate adjacency matrix with example beta value 8
cem <- get_adj(cem, beta=8)
# Check results
adj <- adj_data(cem)
adj[1:5, 1:5]

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Calculate adjacency matrix with example beta value 8
cem <- get_adj(cem, beta=8)
# Check results
adj <- adj_data(cem)
adj[1:5, 1:5]

Soft-threshold beta data

Description

This function takes the input parameters from find_modules and calculates the WGCNA soft-threshold parameters and returns them.

Usage

get_beta_data(cem, ...)

## S4 method for signature 'CEMiTool'
get_beta_data(
  cem,
  network_type = "unsigned",
  cor_function = "cor",
  cor_method = "pearson",
  verbose = FALSE
)
get_beta_data(cem, ...)

## S4 method for signature 'CEMiTool'
get_beta_data(
  cem,
  network_type = "unsigned",
  cor_function = "cor",
  cor_method = "pearson",
  verbose = FALSE
)

Arguments

`cem`	A CEMiTool object containing expression data
`...`	Optional parameters.
`network_type`	A character string indicating to use either "unsigned" (default) or "signed" networks. Default `"unsigned"`.
`cor_function`	A character string indicating the correlation function to be used. Default `'cor'`.
`cor_method`	A character string indicating which correlation coefficient is to be computed. Default `"pearson"`
`verbose`	Logical. If `TRUE`, reports analysis steps. Default `FALSE`

Value

A list containing the soft-threshold selected by WGCNA and scale-free model parameters

Examples

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Get beta data
beta_data <- get_beta_data(cem)

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Get beta data
beta_data <- get_beta_data(cem)

Calculate CEMiTool beta and R2 values

Description

This function takes a CEMiTool object with beta data and returns a vector containing the chosen beta and corresponding R squared value.

Usage

get_cemitool_r2_beta(cem, ...)

## S4 method for signature 'CEMiTool'
get_cemitool_r2_beta(cem, eps = 0.1)
get_cemitool_r2_beta(cem, ...)

## S4 method for signature 'CEMiTool'
get_cemitool_r2_beta(cem, eps = 0.1)

Arguments

`cem`	A CEMiTool object containing the fit_indices slot
`...`	Optional parameters.
`eps`	A value indicating the accepted interval between successive values of R squared to use to calculate the selected beta. Default: 0.1.

Value

A vector containing R squared value and the chosen beta parameter.

Examples

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Get modules and beta data
cem <- find_modules(cem)
# Get CEMiTool R2 and beta values
get_cemitool_r2_beta(cem)

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Get modules and beta data
cem <- find_modules(cem)
# Get CEMiTool R2 and beta values
get_cemitool_r2_beta(cem)

Calculate network connectivity

Description

This function takes a CEMiTool object and returns the network connectivity.

Usage

get_connectivity(cem, ...)

## S4 method for signature 'CEMiTool'
get_connectivity(cem, beta)
get_connectivity(cem, ...)

## S4 method for signature 'CEMiTool'
get_connectivity(cem, beta)

Arguments

`cem`	Object of class `CEMiTool` containing the fit_indices slot
`...`	Optional parameters.
`beta`	A soft-thresholding value to be used for the network.

Value

The value of the network's connectivity.

Examples

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Get modules and beta data
cem <- find_modules(cem)
# Get network connectivity with example beta value 8
get_connectivity(cem, beta=8)

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Get modules and beta data
cem <- find_modules(cem)
# Get network connectivity with example beta value 8
get_connectivity(cem, beta=8)

Get hubs

Description

Returns n genes in each module with high connectivity.

Usage

get_hubs(cem, ...)

## S4 method for signature 'CEMiTool'
get_hubs(cem, n = 5, method = "adjacency")
get_hubs(cem, ...)

## S4 method for signature 'CEMiTool'
get_hubs(cem, n = 5, method = "adjacency")

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`n`	Number of hubs to return from each module. If "all", returns all genes in decreasing order of connectivity. Default: 5.
`method`	Method for hub calculation. Either "adjacency" or "kME". Default: "adjacency"

Value

A list containing hub genes for each module and the value of the calculated method.

Examples

# Get example CEMiTool object
data(cem)
# Get module hubs
hubs <- get_hubs(cem, n=10, "adjacency")

# Get example CEMiTool object
data(cem)
# Get module hubs
hubs <- get_hubs(cem, n=10, "adjacency")

Merge similar modules

Description

This function takes a CEMiTool object with expression and a vector of numeric labels to merge similar modules.

Usage

get_merged_mods(cem, ...)

## S4 method for signature 'CEMiTool'
get_merged_mods(cem, mods, diss_thresh = 0.8, verbose = FALSE)
get_merged_mods(cem, ...)

## S4 method for signature 'CEMiTool'
get_merged_mods(cem, mods, diss_thresh = 0.8, verbose = FALSE)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`mods`	A vector containing numeric labels for each module gene
`diss_thresh`	A threshold of dissimilarity for modules. Default is 0.8.
`verbose`	Logical. If `TRUE`, reports analysis steps. Default `FALSE`

Value

Numeric labels assigning genes to modules.

Examples

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Calculate adjacency matrix with example beta value 8
cem <- get_adj(cem, beta=8)
# Get modules
mods <- get_mods(cem)
# Merge similar modules
merged_mods <- get_merged_mods(cem, mods)

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Calculate adjacency matrix with example beta value 8
cem <- get_adj(cem, beta=8)
# Get modules
mods <- get_mods(cem)
# Merge similar modules
merged_mods <- get_merged_mods(cem, mods)

Calculate co-expression modules

Description

This function takes a CEMiTool object containing an adjacency matrix together with the given network parameters, and returns the given co-expression modules.

Usage

get_mods(cem, ...)

## S4 method for signature 'CEMiTool'
get_mods(
  cem,
  cor_function = "cor",
  cor_method = "pearson",
  tom_type = "signed",
  min_ngen = 20
)
get_mods(cem, ...)

## S4 method for signature 'CEMiTool'
get_mods(
  cem,
  cor_function = "cor",
  cor_method = "pearson",
  tom_type = "signed",
  min_ngen = 20
)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`cor_function`	A character string indicating the correlation function to be used. Default `'cor'`.
`cor_method`	A character string indicating which correlation coefficient is to be computed. Default `"pearson"`.
`tom_type`	A character string indicating to use either "unsigned" or "signed" (default) TOM similarity measure.
`min_ngen`	Minimal number of genes per module (Default: 20).

Value

Numeric labels assigning genes to modules.

Examples

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Calculate adjacency matrix with example beta value 8
cem <- get_adj(cem, beta=8)
# Get module labels
mods <- get_mods(cem)
# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Calculate adjacency matrix with example beta value 8
cem <- get_adj(cem, beta=8)
# Get module labels
mods <- get_mods(cem)

Calculate phi

Description

This function takes a CEMiTool object and returns the phi parameter.

Usage

get_phi(cem, ...)

## S4 method for signature 'CEMiTool'
get_phi(cem)
get_phi(cem, ...)

## S4 method for signature 'CEMiTool'
get_phi(cem)

Arguments

`cem`	A CEMiTool object containing the fit_indices slot
`...`	Optional parameters.

Value

The phi parameter

Examples

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Get modules and beta data
cem <- find_modules(cem)
# Get phi
get_phi(cem)

# Get example expression data
data(expr0)
# Initialize new CEMiTool object with expression data
cem <- new_cem(expr0, filter=TRUE, apply_vst=FALSE)
# Get modules and beta data
cem <- find_modules(cem)
# Get phi
get_phi(cem)

Retrieve Gene Set Enrichment Analysis (GSEA) results

Description

Retrieve Gene Set Enrichment Analysis (GSEA) results

Usage

gsea_data(cem)

## S4 method for signature 'CEMiTool'
gsea_data(cem)
gsea_data(cem)

## S4 method for signature 'CEMiTool'
gsea_data(cem)

Arguments

cem

Object of class CEMiTool

Value

Object of class list with GSEA data

Examples

# Get example CEMiTool object
data(cem)
# Look at example annotation file
sample_annotation(cem)
# Run GSEA on network modules
cem <- mod_gsea(cem)
# Check results
gsea_data(cem)
# Get example CEMiTool object
data(cem)
# Look at example annotation file
sample_annotation(cem)
# Run GSEA on network modules
cem <- mod_gsea(cem)
# Check results
gsea_data(cem)

Retrieve and set interaction data to CEMiTool object

Description

Retrieve and set interaction data to CEMiTool object

Usage

interactions_data(cem, ...)

## S4 method for signature 'CEMiTool'
interactions_data(cem)

interactions_data(cem) <- value

## S4 replacement method for signature 'CEMiTool'
interactions_data(cem) <- value
interactions_data(cem, ...)

## S4 method for signature 'CEMiTool'
interactions_data(cem)

interactions_data(cem) <- value

## S4 replacement method for signature 'CEMiTool'
interactions_data(cem) <- value

Arguments

`cem`	Object of class `CEMiTool`.
`...`	parameters for igraph::graph_from_data_frame
`value`	a data.frame or matrix containing two columns

Value

Object of class CEMiTool

Examples

# Get example CEMiTool object
data(cem)
# Read example interactions data
int_df <- read.delim(system.file("extdata", "interactions.tsv", 
    package = "CEMiTool"))
# Insert interactions data
interactions_data(cem) <- int_df
# Check interactions data
interactions_data(cem)

# Get example CEMiTool object
data(cem)
# Read example interactions data
int_df <- read.delim(system.file("extdata", "interactions.tsv", 
    package = "CEMiTool"))
# Insert interactions data
interactions_data(cem) <- int_df
# Check interactions data
interactions_data(cem)

Retrieve and set mod_colors attribute

Description

Retrieve and set mod_colors attribute

Usage

mod_colors(cem)

## S4 method for signature 'CEMiTool'
mod_colors(cem)

mod_colors(cem) <- value

## S4 replacement method for signature 'CEMiTool,character'
mod_colors(cem) <- value
mod_colors(cem)

## S4 method for signature 'CEMiTool'
mod_colors(cem)

mod_colors(cem) <- value

## S4 replacement method for signature 'CEMiTool,character'
mod_colors(cem) <- value

Arguments

`cem`	Object of class `CEMiTool`
`value`	a character vector containing colors for each module. Names should match with module names

Value

A vector with color names.

Examples

# Get example CEMiTool object
data(cem)
# See module colors
mod_colors(cem)
# Get example CEMiTool object
data(cem)
# See module colors
mod_colors(cem)

Get the number of genes in modules in a CEMiTool object

Description

Get the number of genes in modules in a CEMiTool object

Usage

mod_gene_num(cem, module = NULL)

## S4 method for signature 'CEMiTool'
mod_gene_num(cem, module = NULL)
mod_gene_num(cem, module = NULL)

## S4 method for signature 'CEMiTool'
mod_gene_num(cem, module = NULL)

Arguments

`cem`	Object of class `CEMiTool`
`module`	Default is NULL. If a character string designating a module is given, the number of genes in that module is returned instead.

Value

The number of genes in module(s)

Examples

# Get example CEMiTool object
data(cem)
# Get the number of genes in modules
mod_gene_num(cem)
# Get the number of genes in module M1
mod_gene_num(cem, "M1")

# Get example CEMiTool object
data(cem)
# Get the number of genes in modules
mod_gene_num(cem)
# Get the number of genes in module M1
mod_gene_num(cem, "M1")

Module Gene Set Enrichment Analysis

Description

Perfoms Gene Set Enrichment Analysis (GSEA) for each co-expression module found.

Usage

mod_gsea(cem, ...)

## S4 method for signature 'CEMiTool'
mod_gsea(
  cem,
  gsea_scale = TRUE,
  rank_method = "mean",
  gsea_min_size = 15,
  gsea_max_size = 1000,
  verbose = FALSE
)
mod_gsea(cem, ...)

## S4 method for signature 'CEMiTool'
mod_gsea(
  cem,
  gsea_scale = TRUE,
  rank_method = "mean",
  gsea_min_size = 15,
  gsea_max_size = 1000,
  verbose = FALSE
)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`gsea_scale`	If TRUE, transform data using z-score transformation. Default: TRUE
`rank_method`	Character string indicating how to rank genes. Either "mean" (the default) or "median".
`gsea_min_size`	Minimum gene set size (Default: 15).
`gsea_max_size`	Maximum gene set size (Default: 1000).
`verbose`	logical. Report analysis steps.

Value

GSEA results.

Examples

# Get example CEMiTool object
data(cem)
# Look at example annotation file
sample_annotation(cem)
# Run GSEA on network modules
cem <- mod_gsea(cem)
# Check results
gsea_data(cem)

# Get example CEMiTool object
data(cem)
# Look at example annotation file
sample_annotation(cem)
# Run GSEA on network modules
cem <- mod_gsea(cem)
# Check results
gsea_data(cem)

Get module names in a CEMiTool object

Description

Get module names in a CEMiTool object

Usage

mod_names(cem, include_NC = TRUE)

## S4 method for signature 'CEMiTool'
mod_names(cem, include_NC = TRUE)
mod_names(cem, include_NC = TRUE)

## S4 method for signature 'CEMiTool'
mod_names(cem, include_NC = TRUE)

Arguments

`cem`	Object of class `CEMiTool`
`include_NC`	Logical. Whether or not to include "Not.Correlated" module. Defaults to `TRUE`.

Value

Module names

Examples

# Get example CEMiTool object
data(cem)
# Get module names
mod_names(cem)

# Get example CEMiTool object
data(cem)
# Get module names
mod_names(cem)

Module Overrepresentation Analysis

Description

Performs overrepresentation analysis for each co-expression module found.

Usage

mod_ora(cem, ...)

## S4 method for signature 'CEMiTool'
mod_ora(cem, gmt, verbose = FALSE)
mod_ora(cem, ...)

## S4 method for signature 'CEMiTool'
mod_ora(cem, gmt, verbose = FALSE)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`gmt`	Object of class `data.frame` with 2 columns, one with pathways and one with genes
`verbose`	logical. Report analysis steps.

Value

Object of class CEMiTool

Examples

# Get example CEMiTool object
data(cem)
# Read gmt file
gmt <- read_gmt(system.file('extdata', 'pathways.gmt',
                   package='CEMiTool'))
# Run module overrepresentation analysis
cem <- mod_ora(cem, gmt)
# Check results
head(ora_data(cem))

# Get example CEMiTool object
data(cem)
# Read gmt file
gmt <- read_gmt(system.file('extdata', 'pathways.gmt',
                   package='CEMiTool'))
# Run module overrepresentation analysis
cem <- mod_ora(cem, gmt)
# Check results
head(ora_data(cem))

Co-expression module summarization

Description

Summarizes modules using mean or eigengene expression.

Usage

mod_summary(cem, ...)

## S4 method for signature 'CEMiTool'
mod_summary(cem, method = c("mean", "median", "eigengene"), verbose = FALSE)
mod_summary(cem, ...)

## S4 method for signature 'CEMiTool'
mod_summary(cem, method = c("mean", "median", "eigengene"), verbose = FALSE)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`method`	A character string indicating which summarization method is to be used. Can be 'eigengene', 'mean' or 'median'. Default is 'mean'.
`verbose`	Logical. If `TRUE`, reports analysis steps.

Value

A data.frame with summarized values.

Examples

# Get example CEMiTool object
data(cem)
# Summarize results
mod_summary <- mod_summary(cem)

# Get example CEMiTool object
data(cem)
# Summarize results
mod_summary <- mod_summary(cem)

Get the module genes in a CEMiTool object

Description

Get the module genes in a CEMiTool object

Usage

module_genes(cem, module = NULL)

## S4 method for signature 'CEMiTool'
module_genes(cem, module = NULL)
module_genes(cem, module = NULL)

## S4 method for signature 'CEMiTool'
module_genes(cem, module = NULL)

Arguments

`cem`	Object of class `CEMiTool`
`module`	A character string with the name of the module of which genes are to be returned. Defaults to `NULL`, which returns the full list of genes and modules.

Value

Object of class data.frame containing genes and their respective module

Examples

# Get example CEMiTool object
data(cem)
# Get the module genes
module_genes(cem)
# Get genes for module M1
module_genes(cem, module="M1")
# Get example CEMiTool object
data(cem)
# Get the module genes
module_genes(cem)
# Get genes for module M1
module_genes(cem, module="M1")

Create a CEMiTool object

Description

Create a CEMiTool object

Usage

new_cem(
  expr = data.frame(),
  sample_annot = data.frame(),
  sample_name_column = "SampleName",
  class_column = "Class",
  filter = TRUE,
  apply_vst = FALSE,
  filter_pval = 0.1
)
new_cem(
  expr = data.frame(),
  sample_annot = data.frame(),
  sample_name_column = "SampleName",
  class_column = "Class",
  filter = TRUE,
  apply_vst = FALSE,
  filter_pval = 0.1
)

Arguments

`expr`	Object of class `data.frame` with gene expression data
`sample_annot`	Object of `data.frame` containing the sample annotation. It should have at least two columns containing group Class and the Sample Name that should match with samples in expression file
`sample_name_column`	A string specifying the column to be used as sample identification. Default: "SampleName".
`class_column`	A string specifying the column to be used as a grouping factor for samples. Default: "Class"
`filter`	Logical. Used to define if posterior functions should use filtered expression data or not (Default: TRUE)
`apply_vst`	Logical. Used to define if posterior functions should use a variance stabilizing transformation on expression data before analyses. Only valid if argument `filter` is TRUE. (Default: FALSE)
`filter_pval`	logical. Threshold for filter p-value. Ignored if filter = FALSE (Default: 0.1)

Value

Object of class CEMiTool

Examples

# Create new CEMiTool object
cem <- new_cem()
# Create new CEMiTool object with expression and sample_annotation data
data(expr0)
data(sample_annot)
cem <- new_cem(expr0, sample_annot, "SampleName", "Class")
# Equivalent to a call to new()
cem2 <- new("CEMiTool", expression=expr0, sample_annotation=sample_annot)
identical(cem, cem2)
# Create new CEMiTool object
cem <- new_cem()
# Create new CEMiTool object with expression and sample_annotation data
data(expr0)
data(sample_annot)
cem <- new_cem(expr0, sample_annot, "SampleName", "Class")
# Equivalent to a call to new()
cem2 <- new("CEMiTool", expression=expr0, sample_annotation=sample_annot)
identical(cem, cem2)

Get the number of modules in a CEMiTool object

Description

Get the number of modules in a CEMiTool object

Usage

nmodules(cem)

## S4 method for signature 'CEMiTool'
nmodules(cem)
nmodules(cem)

## S4 method for signature 'CEMiTool'
nmodules(cem)

Arguments

cem

Object of class CEMiTool

Value

number of modules

Examples

# Get example CEMiTool object
data(cem)
# Get the number of modules
nmodules(cem)

# Get example CEMiTool object
data(cem)
# Get the number of modules
nmodules(cem)

Retrieve over representation analysis (ORA) results

Description

Retrieve over representation analysis (ORA) results

Usage

ora_data(cem)

## S4 method for signature 'CEMiTool'
ora_data(cem)
ora_data(cem)

## S4 method for signature 'CEMiTool'
ora_data(cem)

Arguments

cem

Object of class CEMiTool

Details

This function returns the results of the mod_ora function on the CEMiTool object. The ID column corresponds to pathways in the gmt file for which genes in the modules were enriched. The Count column shows the number of genes in the module that are enriched for each pathway. The GeneRatio column shows the proportion of genes in the module enriched for a given pathway out of all the genes in the module enriched for any given pathway. The BgRatio column shows the proportion of genes in a given pathway out of all the genes in the gmt file. For more details, please refer to the clusterProfiler package documentation.

Value

Object of class data.frame with ORA data

References

Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.

Examples

# Get example CEMiTool object
data(cem)
# Read gmt file
gmt <- read_gmt(system.file('extdata', 'pathways.gmt',
                   package='CEMiTool'))
# Run module overrepresentation analysis
cem <- mod_ora(cem, gmt)
# Check results
head(ora_data(cem))
# Get example CEMiTool object
data(cem)
# Read gmt file
gmt <- read_gmt(system.file('extdata', 'pathways.gmt',
                   package='CEMiTool'))
# Run module overrepresentation analysis
cem <- mod_ora(cem, gmt)
# Check results
head(ora_data(cem))

Soft-threshold beta selection graph

Description

Creates a graph showing each possible soft-threshold value and its corresponding R squared value

Usage

plot_beta_r2(cem, ...)

## S4 method for signature 'CEMiTool'
plot_beta_r2(cem, plot_title = "Scale independence (beta selection)")
plot_beta_r2(cem, ...)

## S4 method for signature 'CEMiTool'
plot_beta_r2(cem, plot_title = "Scale independence (beta selection)")

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`plot_title`	title of the graph

Value

Object of class CEMiTool with beta x R squared plot

Examples

# Get example CEMiTool object
data(cem)
# Plot scale-free model fit as a function of the soft-thresholding beta parameter choice
cem <- plot_beta_r2(cem)
# Check resulting plot
show_plot(cem, "beta_r2")

# Get example CEMiTool object
data(cem)
# Plot scale-free model fit as a function of the soft-thresholding beta parameter choice
cem <- plot_beta_r2(cem)
# Check resulting plot
show_plot(cem, "beta_r2")

GSEA visualization

Description

Creates a heatmap with the results of gene set enrichment analysis (GSEA) of co-expression modules

Usage

plot_gsea(cem, ...)

## S4 method for signature 'CEMiTool'
plot_gsea(cem, pv_cut = 0.05)
plot_gsea(cem, ...)

## S4 method for signature 'CEMiTool'
plot_gsea(cem, pv_cut = 0.05)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`pv_cut`	P-value cut-off. Default `0.05`

Value

Object of class CEMiTool with GSEA plots

Examples

# Get example CEMiTool object
data(cem)
# Get example sample annotation file
# Run GSEA on network modules
cem <- mod_gsea(cem)
# Plot GSEA results
cem <- plot_gsea(cem)
# Check resulting plot
show_plot(cem, "gsea")

# Get example CEMiTool object
data(cem)
# Get example sample annotation file
# Run GSEA on network modules
cem <- mod_gsea(cem)
# Plot GSEA results
cem <- plot_gsea(cem)
# Check resulting plot
show_plot(cem, "gsea")

Plot histogram

Description

This function plots a histogram of the distribution of gene expression, to help assess the normality of the data.

Usage

plot_hist(cem, ...)

## S4 method for signature 'CEMiTool'
plot_hist(cem, filter = FALSE)
plot_hist(cem, ...)

## S4 method for signature 'CEMiTool'
plot_hist(cem, filter = FALSE)

Arguments

`cem`	Object of class `CEMiTool`
`...`	Optional parameters
`filter`	Logical. Whether or not to use filtered data for CEMiTool objects (Default: FALSE).

Value

Object of class CEMiTool containing expression histogram

Examples

# Get example CEMiTool object
data(cem)
# Plot histogram
cem <- plot_hist(cem)
# Check results
show_plot(cem, "hist")

# Get example CEMiTool object
data(cem)
# Plot histogram
cem <- plot_hist(cem)
# Check results
show_plot(cem, "hist")

Network visualization

Description

Creates a graph based on interactions provided

Usage

plot_interactions(cem, ...)

## S4 method for signature 'CEMiTool'
plot_interactions(cem, n = 10, ...)
plot_interactions(cem, ...)

## S4 method for signature 'CEMiTool'
plot_interactions(cem, n = 10, ...)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`n`	number of nodes to label

Value

Object of class CEMiTool with profile plots

Examples

# Get example CEMiTool object
data(cem)
# Get example gene interactions data
int <- system.file("extdata", "interactions.tsv", package = "CEMiTool")
int_df <- read.delim(int)
# Include interaction data into CEMiTool object
interactions_data(cem) <- int_df
# Plot resulting networks
cem <- plot_interactions(cem)
# Check resulting plot
show_plot(cem, "interaction")

# Get example CEMiTool object
data(cem)
# Get example gene interactions data
int <- system.file("extdata", "interactions.tsv", package = "CEMiTool")
int_df <- read.delim(int)
# Include interaction data into CEMiTool object
interactions_data(cem) <- int_df
# Plot resulting networks
cem <- plot_interactions(cem)
# Check resulting plot
show_plot(cem, "interaction")

Network mean connectivity

Description

Creates a graph showing the mean connectivity of genes in the network

Usage

plot_mean_k(cem, ...)

## S4 method for signature 'CEMiTool'
plot_mean_k(cem, title = "Mean connectivity")
plot_mean_k(cem, ...)

## S4 method for signature 'CEMiTool'
plot_mean_k(cem, title = "Mean connectivity")

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`title`	title of the graph

Value

Object of class CEMiTool with connectivity plot

Examples

# Get example CEMiTool object
data(cem)
# Plot scale-free model fit as a function of the soft-thresholding beta parameter choice
cem <- plot_mean_k(cem)
# Check resulting plot
show_plot(cem, "mean_k")

# Get example CEMiTool object
data(cem)
# Plot scale-free model fit as a function of the soft-thresholding beta parameter choice
cem <- plot_mean_k(cem)
# Check resulting plot
show_plot(cem, "mean_k")

Plot mean and variance

Description

This plot returns a scatterplot of the mean by the variance of gene expression. A linear relationship between these values for RNAseq data suggest that an appropriate transformation such as the Variance Stabilizing Transformation should be applied.

Usage

plot_mean_var(cem, ...)

## S4 method for signature 'CEMiTool'
plot_mean_var(cem, filter = FALSE)
plot_mean_var(cem, ...)

## S4 method for signature 'CEMiTool'
plot_mean_var(cem, filter = FALSE)

Arguments

`cem`	Object of class `CEMiTool`
`...`	Optional parameters
`filter`	Logical. Whether or not to use filtered data for CEMiTool objects (Default: FALSE).

Value

Object of class CEMiTool containing a mean and variance plot

Examples

# Get example CEMiTool object
data(cem)
# Plot mean and variance plot
cem <- plot_mean_var(cem)
# Check results
show_plot(cem, 'mean_var')

# Get example CEMiTool object
data(cem)
# Plot mean and variance plot
cem <- plot_mean_var(cem)
# Check results
show_plot(cem, 'mean_var')

ORA visualization

Description

Creates a bar plot with the results of module overrepresentation analysis

Usage

plot_ora(cem, ...)

## S4 method for signature 'CEMiTool'
plot_ora(cem, n = 10, pv_cut = 0.05, ...)
plot_ora(cem, ...)

## S4 method for signature 'CEMiTool'
plot_ora(cem, n = 10, pv_cut = 0.05, ...)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	parameters to plot_ora_single
`n`	number of modules to show
`pv_cut`	p-value significance cutoff. Default is 0.05.

Value

Object of class CEMiTool with ORA plots

Examples

# Get example CEMiTool object
data(cem)
# Read example gmt file
gmt <- read_gmt(system.file('extdata', 'pathways.gmt',
                   package='CEMiTool'))
# Run overrepresentation analysis
cem <- mod_ora(cem, gmt)
# Plot module gene expression profiles
cem <- plot_ora(cem)
# Check resulting plot
show_plot(cem, "ora")

# Get example CEMiTool object
data(cem)
# Read example gmt file
gmt <- read_gmt(system.file('extdata', 'pathways.gmt',
                   package='CEMiTool'))
# Run overrepresentation analysis
cem <- mod_ora(cem, gmt)
# Plot module gene expression profiles
cem <- plot_ora(cem)
# Check resulting plot
show_plot(cem, "ora")

Expression profile visualization

Description

Creates a plot with module gene expression profiles along samples

Usage

plot_profile(cem, ...)

## S4 method for signature 'CEMiTool'
plot_profile(cem, order_by_class = TRUE, center_func = "mean")
plot_profile(cem, ...)

## S4 method for signature 'CEMiTool'
plot_profile(cem, order_by_class = TRUE, center_func = "mean")

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters.
`order_by_class`	Logical. Only used if a sample annotation file is present. Whether or not to order by the class column in the sample annotation file (as defined by the class_column slot in `cem`).
`center_func`	Character string indicating the centrality measure to show in the plot. Either 'mean' (the default) or 'median'.

Value

Object of class CEMiTool with profile plots

Examples

# Get example CEMiTool object
data(cem)
# Plot module gene expression profiles
cem <- plot_profile(cem)
# Check resulting plot
show_plot(cem, "profile")

# Get example CEMiTool object
data(cem)
# Plot module gene expression profiles
cem <- plot_profile(cem)
# Check resulting plot
show_plot(cem, "profile")

Plot quantile-quantile plot

Description

This function creates a normal QQ plot of the expression values.

Usage

plot_qq(cem, ...)

## S4 method for signature 'CEMiTool'
plot_qq(cem, filter = FALSE)
plot_qq(cem, ...)

## S4 method for signature 'CEMiTool'
plot_qq(cem, filter = FALSE)

Arguments

`cem`	Object of class `CEMiTool`
`...`	Optional parameters
`filter`	Logical. Whether or not to use filtered data for CEMiTool objects (Default: FALSE).

Value

Object of class CEMiTool containing qqplot

Examples

# Get example CEMiTool object
data(cem)
# Plot quantile-quantile plot
cem <- plot_qq(cem)
# Check results
show_plot(cem, 'qq')

# Get example CEMiTool object
data(cem)
# Plot quantile-quantile plot
cem <- plot_qq(cem)
# Check results
show_plot(cem, 'qq')

Sample clustering

Description

Creates a dendrogram showing the similarities between samples in the expression data.

Usage

plot_sample_tree(cem, ...)

## S4 method for signature 'CEMiTool'
plot_sample_tree(
  cem,
  col_vector = NULL,
  sample_name_column = NULL,
  class_column = NULL,
  filter = FALSE
)
plot_sample_tree(cem, ...)

## S4 method for signature 'CEMiTool'
plot_sample_tree(
  cem,
  col_vector = NULL,
  sample_name_column = NULL,
  class_column = NULL,
  filter = FALSE
)

Arguments

`cem`	Object of class `CEMiTool` or `data.frame`.
`...`	Optional parameters.
`col_vector`	A vector of columns to use for visualizing the clustering. See Details.
`sample_name_column`	A string specifying the column to be used as sample identification. For CEMiTool objects, this will be the string specified in the sample_name_column slot.
`class_column`	A string specifying the column to be used as sample group identification. For CEMiTool objects, this will be the string specified in the class_column slot.
`filter`	Logical. Whether or not to use filtered data for CEMiTool objects (Default: FALSE).

Value

Object of class CEMiTool with dendrogram or a plot object.

Examples

# Get example CEMiTool object
data(cem)
# Plot sample dendrogram
cem <- plot_sample_tree(cem)
# Check resulting plot
show_plot(cem, "sample_tree")

# Get example CEMiTool object
data(cem)
# Plot sample dendrogram
cem <- plot_sample_tree(cem)
# Check resulting plot
show_plot(cem, "sample_tree")

Read a GMT file

Description

Read a GMT file

Usage

read_gmt(fname)
read_gmt(fname)

Arguments

fname

GMT file name.

Value

A list containing genes and description of each pathway

Examples

# Read example gmt file
gmt_fname <- system.file("extdata", "pathways.gmt", package = "CEMiTool")
gmt_in <- read_gmt(gmt_fname)

# Read example gmt file
gmt_fname <- system.file("extdata", "pathways.gmt", package = "CEMiTool")
gmt_in <- read_gmt(gmt_fname)

Yellow Fever Sample Annotation data

Description

Modified data from a yellow fever vaccination study by Querec et al, 2009. This dataset, together with expr can be used as input for CEMiTool functions

Usage

data(sample_annot)
data(sample_annot)

Format

An object of class data.frame

Source

GEO

References

Querec TD, Akondy RS, Lee EK, Cao W et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol 2009 Jan;10(1):116-25. PMID: 19029902 PubMed

Examples

data(expr)
data(sample_annot)
# Run CEMiTool analysis
## Not run: cemitool(expr, sample_annot)
data(expr)
data(sample_annot)
# Run CEMiTool analysis
## Not run: cemitool(expr, sample_annot)

Retrive or set the sample_annotation attribute

Description

Retrive or set the sample_annotation attribute

Usage

sample_annotation(cem)

## S4 method for signature 'CEMiTool'
sample_annotation(cem)

sample_annotation(
  cem,
  sample_name_column = "SampleName",
  class_column = "Class"
) <- value

## S4 replacement method for signature 'CEMiTool'
sample_annotation(
  cem,
  sample_name_column = "SampleName",
  class_column = "Class"
) <- value
sample_annotation(cem)

## S4 method for signature 'CEMiTool'
sample_annotation(cem)

sample_annotation(
  cem,
  sample_name_column = "SampleName",
  class_column = "Class"
) <- value

## S4 replacement method for signature 'CEMiTool'
sample_annotation(
  cem,
  sample_name_column = "SampleName",
  class_column = "Class"
) <- value

Arguments

`cem`	Object of class `CEMiTool`
`sample_name_column`	A string containing the name of a column which should be used as a unique identifier for samples in the file. Only used when assigning a sample annotation data.frame. Default: "SampleName".
`class_column`	A string containing the name of a column which should be used to identify different sample groups. Only used when assigning a sample annotation data.frame. Default: "Class"
`value`	A data.frame containing the sample annotation, should have at least two columns containing the Class and the Sample Name that should match with samples in expression

Value

A data.frame containing characteristics of each sample.

Examples

# Get example expression data
data(expr0)
# Get example sample_annotation data
data(sample_annot)
# Initialize CEMiTool object with expression
cem <- new_cem(expr0)
# Add sample annotation file to CEMiTool object
sample_annotation(cem,
    sample_name_column="SampleName",
    class_column="Class") <- sample_annot
# Check annotation
head(sample_annotation(cem))

# Get example expression data
data(expr0)
# Get example sample_annotation data
data(sample_annot)
# Initialize CEMiTool object with expression
cem <- new_cem(expr0)
# Add sample annotation file to CEMiTool object
sample_annotation(cem,
    sample_name_column="SampleName",
    class_column="Class") <- sample_annot
# Check annotation
head(sample_annotation(cem))

Save CEMiTool object plots

Description

Save plots into the directory specified by the directory argument.

Usage

save_plots(cem, ...)

## S4 method for signature 'CEMiTool'
save_plots(
  cem,
  value = c("all", "profile", "gsea", "ora", "interaction", "beta_r2", "mean_k",
    "sample_tree", "mean_var", "hist", "qq"),
  force = FALSE,
  directory = "./Plots"
)
save_plots(cem, ...)

## S4 method for signature 'CEMiTool'
save_plots(
  cem,
  value = c("all", "profile", "gsea", "ora", "interaction", "beta_r2", "mean_k",
    "sample_tree", "mean_var", "hist", "qq"),
  force = FALSE,
  directory = "./Plots"
)

Arguments

`cem`	Object of class `CEMiTool`.
`...`	Optional parameters One of "all", "profile", "gsea", "ora", "interaction", "beta_r2", "mean_k", "sample_tree", "mean_var", "hist", "qq".
`value`	A character string containing the name of the plot to be saved.
`force`	If the directory exists, execution will not stop.
`directory`	Directory into which the files will be saved.

Value

A pdf file or files with the desired plot(s)

Examples

# Get example CEMiTool object
data(cem)
# Plot beta x R squared graph
cem <- plot_beta_r2(cem)
# Save plot
## Not run: save_plots(cem, value="beta_r2", directory="./Plots")
# Get example CEMiTool object
data(cem)
# Plot beta x R squared graph
cem <- plot_beta_r2(cem)
# Save plot
## Not run: save_plots(cem, value="beta_r2", directory="./Plots")

Select genes based on variance

Description

Select genes based on variance

Usage

select_genes(expr, n_genes, filter_pval = 0.1)
select_genes(expr, n_genes, filter_pval = 0.1)

Arguments

`expr`	A data.frame containing expression values
`n_genes`	(Optional) Number of genes to be selected
`filter_pval`	P-value cutoff for gene selection

Value

A vector containing the names of selected genes

Examples

# Get example expression data
data(expr0)
# Filter genes
expr_f <- filter_genes(expr0)
# Check selected genes
expr_f[1:5, 1:5]
# Filter genes and apply variance stabilizing transformation
expr_f2 <- filter_genes(expr0, apply_vst=TRUE)
# Check results
expr_f2[1:5, 1:5]
# Selected genes
selected <- select_genes(expr_f2)
# Get data.frame with only selected genes
expr_s <- expr_f2[selected, ]
# Check results
expr_s[1:5, 1:5]
# Get example expression data
data(expr0)
# Filter genes
expr_f <- filter_genes(expr0)
# Check selected genes
expr_f[1:5, 1:5]
# Filter genes and apply variance stabilizing transformation
expr_f2 <- filter_genes(expr0, apply_vst=TRUE)
# Check results
expr_f2[1:5, 1:5]
# Selected genes
selected <- select_genes(expr_f2)
# Get data.frame with only selected genes
expr_s <- expr_f2[selected, ]
# Check results
expr_s[1:5, 1:5]

Retrieve CEMiTool object plots

Description

Retrieve CEMiTool object plots

Usage

show_plot(cem, value)

## S4 method for signature 'CEMiTool'
show_plot(
  cem,
  value = c("profile", "gsea", "ora", "interaction", "beta_r2", "mean_k",
    "sample_tree", "mean_var", "hist", "qq")
)
show_plot(cem, value)

## S4 method for signature 'CEMiTool'
show_plot(
  cem,
  value = c("profile", "gsea", "ora", "interaction", "beta_r2", "mean_k",
    "sample_tree", "mean_var", "hist", "qq")
)

Arguments

`cem`	Object of class `CEMiTool`.
`value`	A character string containing the name of the plot to be shown. One of "profile", "gsea", "ora", "interaction", "beta_r2", "mean_k", "sample_tree", "mean_var", "hist", "qq".

Value

A plot corresponding to a CEMiTool analysis

Examples

# Get example CEMiTool object
data(cem)
# Plot beta x R squared graph
cem <- plot_beta_r2(cem)
# Check plot
show_plot(cem, "beta_r2")
# Get example CEMiTool object
data(cem)
# Plot beta x R squared graph
cem <- plot_beta_r2(cem)
# Check plot
show_plot(cem, "beta_r2")

Print a cemitool object

Description

Print a cemitool object

Usage

## S4 method for signature 'CEMiTool'
show(object)
## S4 method for signature 'CEMiTool'
show(object)

Arguments

object

Object of class CEMiTool

Value

A CEMiTool object.

Save the CEMiTool object in files

Description

Save the CEMiTool object in files

Usage

write_files(cem, ...)

## S4 method for signature 'CEMiTool'
write_files(cem, directory = "./Tables", force = FALSE)
write_files(cem, ...)

## S4 method for signature 'CEMiTool'
write_files(cem, directory = "./Tables", force = FALSE)

Arguments

`cem`	Object of class `CEMiTool`
`...`	Optional parameters
`directory`	a directory
`force`	if the directory exists the execution will not stop

Value

A directory containing CEMiTool results in files.

Examples

# Get example CEMiTool object
data(cem)
# Save CEMiTool results in files
write_files(cem, directory=".", force=TRUE)

# Get example CEMiTool object
data(cem)
# Save CEMiTool results in files
write_files(cem, directory=".", force=TRUE)

Package 'CEMiTool'

Help Index

Get or set adjacency matrix value

Description

Usage

Arguments

Value

Examples

CEMiTool Object

Description

Usage

Format

Examples

Full gene co-expression analysis

Description

Usage

Arguments

Value

Examples

An S4 class to represent the CEMiTool analysis.

Description

Slots

Examples

Diagnostic report

Description

Usage

Arguments

Value

Retrieve and set expression attribute

Description

Usage

Arguments

Value

Examples

Yellow Fever gene expression data from GEO study GSE13485

Description

Usage

Format

Source

References

Examples

Filter a gene expression data.frame

Description

Usage

Arguments

Details

Value

Examples

Co-expression modules definition

Description

Usage

Arguments

Value

Examples

Retrieve scale-free model fit data

Description

Usage

Arguments

Value

Examples

CEMiTool report

Description

Usage

Arguments

Value

Examples

Calculate adjacency matrix

Description

Usage

Arguments

Value

Examples

Soft-threshold beta data

Description

Usage

Arguments

Value

Examples

Calculate CEMiTool beta and R2 values

Description