Package 'GWENA'

Title: Pipeline for augmented co-expression analysis
Description: The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature (i.e. gene) focus. We propose GWENA (Gene Whole co-Expression Network Analysis) , a tool designed to perform gene co-expression network analysis and explore the results in a single pipeline. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions.
Authors: Gwenaëlle Lemoine [aut, cre] , Marie-Pier Scott-Boyer [ths], Arnaud Droit [fnd]
Maintainer: Gwenaëlle Lemoine <[email protected]>
License: GPL-3
Version: 1.17.0
Built: 2024-12-14 03:54:05 UTC
Source: https://github.com/bioc/GWENA

Help Index

Run checks on an object to test if it's a data_expr

Description

Check an object to be a data.frame or a matrix compatible of genes and samples.

Usage

.check_data_expr(data_expr)

Arguments

data_expr

matrix or data.frame, expression data with genes as column and samples as row.

Value

Throw an error if doesn't correspond

Run checks on an object to test if it's a gost result

Description

Take a list that should be a gost result and check if format is good.

Usage

.check_gost(gost_result)

Arguments

gost_result

list, gprofiler2::gost result

Value

Throw an error if doesn't correspond

Run checks on an object to test if it's a module or a list of modules

Description

Check content of a given object to determine if it's a module or a list of modules, meaning a single vector of characters which are gene names, or a named list of these vectors

Usage

.check_module(module, is_list = FALSE)

Arguments

module

vector or list, object to test to be a module or list of modules
is_list

boolean, indicate if module must be tested as a single module or a list of modules

Value

Throw an error if doesn't correspond

Run checks on an object to test if it's a network

Description

Check content of a given object to determine if it's a network, meaning a squared matrix of similarity score between genes.

Usage

.check_network(network)

Arguments

network

matrix or data.frame, object to test to be a network

Value

Throw an error if doesn't correspond

Calculate a contigency table of module overlap between datasets

Description

Calculate a contigency table of module overlap between datasets

Usage

.contingencyTable(modAssignments, mods, tiNodelist)

Arguments

modAssignments

a list where the first element is the 'moduleAssignments' vector in the discovery dataset, and the second element is the 'moduleAssignments vector in the test dataset.
mods

the 'modules' vector for the discovery dataset.
tiNodelist

a vector of node IDs in the test dataset.

Value

A list containing a contigency table, a vector of the proportion of nodes present in the test dataset for each module, a vector containing the number of nodes present in the test dataset for each module, a vector of the node names present in both the discovery and test datasets, a vector of modules that are both requested and have nodes present in the test dataset, and the modAssignments vector containing only nodes present in the test dataset.

Match a correlation function based on a name

Description

Translate a function name into an R function.

Usage

.cor_func_match(cor_func = c("pearson", "spearman", "bicor"))

Arguments

cor_func

string of the name of the correlation to be use

Value

A function corresponding to the correlation required

Modules phenotpic association

Description

Compute the correlation between all modules and the phenotypic variables

Usage

associate_phenotype(
  eigengenes,
  phenotypes,
  cor_func = c("pearson", "spearman", "kendall", "other"),
  your_func = NULL,
  id_col = NULL,
  ...
)

Arguments

eigengenes

matrix or data.frame, eigengenes of the modules. Provided by the output of modules_detection.
phenotypes

matrix or data.frame, phenotypes for each sample to associate.
cor_func

string, name of the correlation function to be used. Must be one of "pearson", "spearman", "kendall", "other". If "other", your_func must be provided
your_func

function returning a correlation matrix. Final values must be in [-1;1] range
id_col

string or vector of string, optional name of the columns containing the common id between eigengenes and phenotypes.
...

any arguments compatible with cor.

Value

A list of two data.frames : associations modules/phenotype and p.values associated to this associations

Examples

eigengene_mat <- data.frame(mod1 = rnorm(20, 0.1, 0.2),
mod2 = rnorm(20, 0.2, 0.2))
phenotype_mat <- data.frame(phenA = sample(c("X", "Y", "Z"), 20,
                            replace = TRUE),
                            phenB = sample(c("U", "V"), 20, replace = TRUE),
                            stringsAsFactors = FALSE)
association <- associate_phenotype(eigengene_mat, phenotype_mat)

Modules enrichment

Description

Enrich genes list from modules.

Usage

bio_enrich(module, custom_gmt = NULL, ...)

Arguments

module

vector or list, vector of gene names representing a module or a named list of this modules.
custom_gmt

string or list, path to a gmt file or a list of these path.
...

any other parameter you can provide to gprofiler2::gost function.

Value

A gprofiler2::gost output, meaning a named list containing a 'result' data.frame with enrichement information on the differents databases and custom gmt files, and a 'meta' list containing informations on the input args, the version of gost, timestamp, etc. For more detail, see ?gprofiler2::gost.

Examples

custom_path <- system.file("extdata", "h.all.v6.2.symbols.gmt",
                           package = "GWENA", mustWork = TRUE)

single_module <- c("BIRC3", "PMAIP1", "CASP8", "JUN", "BCL2L11", "MCL1",
                   "IL1B", "SPTAN1", "DIABLO", "BAX", "BIK", "IL1A", "BID",
                   "CDKN1A", "GADD45A")
single_module_enriched <- bio_enrich(single_module, custom_path)

multi_module <- list(mod1 = single_module,
                     mod2 = c("TAF1C", "TARBP2", "POLH", "CETN2", "POLD1",
                              "CANT1", "PDE4B", "DGCR8", "RAD51", "SURF1",
                               "PNP", "ADA", "NME3", "GTF3C5", "NT5C"))
multi_module_enriched <- bio_enrich(multi_module, custom_path)

Return graph from squared matrix network

Description

Takes a squared matrix containing the pairwise similarity scores for each gene and return a igraph object.

Usage

build_graph_from_sq_mat(sq_mat)

Arguments

sq_mat

matrix or data.frame, squared matrix representing

Value

An igraph object

Examples

mat <- matrix(runif(40*40), 40)
build_graph_from_sq_mat(mat)

Network building by co-expression score computation

Description

Compute the adjacency matrix, then the TOM to build the network. Than detect the modules by hierarchical clustering and thresholding

Usage

build_net(
  data_expr,
  fit_cut_off = 0.9,
  cor_func = c("pearson", "spearman", "bicor", "other"),
  your_func = NULL,
  power_value = NULL,
  block_size = NULL,
  stop_if_fit_pb = FALSE,
  pct_power_ic = 0.7,
  network_type = c("unsigned", "signed", "signed hybrid"),
  tom_type = c("unsigned", "signed", "signed Nowick", "unsigned 2", "signed 2", "none"),
  keep_matrices = c("none", "cor", "adja", "both"),
  n_threads = NULL,
  ...
)

Arguments

data_expr

matrix or data.frame or SummarizedExperiment, expression data with genes as column and samples as row.
fit_cut_off

float, cut off by which R^2 (coefficient of determination) will be thresholded. Must be in ]0;1[.
cor_func

string, name of the correlation function to be used. Must be one of "pearson", "spearman", "bicor", "other". If "other", your_func must be provided
your_func

function returning correlation values. Final values must be in [-1;1]
power_value

integer, power to be applied to the adjacency matrix. If NULL, will be estimated by trying different power law fitting.
block_size

integer, size of blocks by which operations can be proceed. Helping if working with low capacity computers. If null, will be estimated.
stop_if_fit_pb

boolean, does not finding a fit above fit_cut_off, or having a power too low or too high (based on WGCNA FAQ recommended powers) should stop process, or just print a warning and return the highest fitting power.
pct_power_ic

float, confidence interval by which the power fitted should be evaluated for too high or too low a power.
network_type

string, type of network to be used. Either "unsigned", "signed", "signed hybrid". See details.
tom_type

string, type of the topological overlap matrix to be computed. Either "none", "unsigned", "signed", "signed Nowick", "unsigned 2", "signed 2" and "signed Nowick 2". See detail at TOMsimilarityFromExpr.
keep_matrices

string, matrices to keep in final object. Can be one of "none", "cor", "adja", "both". It is usefull to keep both if you plant to use compare_conditions.
n_threads

integer, number of threads that can be used to paralellise the computing
...

any other parameter compatible with adjacency.fromSimilarity

Value

list containing network matrix, metadata of input parameters and power fitting information.

Examples

net <- build_net(kuehne_expr[, seq_len(350)], n_threads = 1)

Compare modules topology between conditions

Description

Take modules built from multiples conditions and search for preservation, non-preservation or one of them, against one or mutliple conditions of reference. Use 7 topological features to perform the differents test, and use permutation to validate results.

Usage

compare_conditions(
  data_expr_list,
  adja_list,
  cor_list = NULL,
  modules_list,
  ref = names(data_expr_list)[1],
  test = NULL,
  cor_func = c("pearson", "spearman", "bicor", "other"),
  your_func = NULL,
  n_perm = 10000,
  test_alternative_hyp = c("greater", "less", "two.sided"),
  pvalue_th = 0.01,
  n_threads = NULL,
  ...
)

Arguments

data_expr_list

list of matrix or data.frame or SummarizedExperiment, list of expression data by condition, with genes as column and samples as row.
adja_list

list of adjacency matrices, list of square tables by condition, representing connectivity between each genes as returned by build_net.
cor_list

list of matrices and/or data.frames, list of square tables by condition, representing correlation between each gene. Must be the same used to create networks in build_net. If NULL, will be re-calculated according to cor_func.
modules_list

list of modules or nested list of modules, list of modules in one condition (will be considered as the one from reference) or a condition named list with list of modules built in each one.
ref

string or vector of strings, condition(s) name to be used as reference for permutation tests, or "cross comparison" if you want to compare each condition with the other as reference. Default will be the name of the first element in data_expr_list.
test

string or vector of strings, condition(s) name to be tested for permutation tests. If NULL, all conditions except these in ref will be taken. If ref is set to "cross comparison", any test specified will be ignored.
cor_func

string, name of the correlation function to be used. Must be one of "pearson", "spearman", "bicor", "other". If "other", your_func must be provided
your_func

function returning correlation values. Final values must be in [-1;1]
n_perm

integer, number of permutation, meaning number of random gene name re-assignment inside network to compute all tests and statistics for module comparison between condition.
test_alternative_hyp

string, either "greater", "less" or "two.sided". Alternative hypothesis (H1) used for the permutation test. Determine if the metrics computed on permuted values are expected to be greater, less or both than the observed ones. More details:modulePreservation
pvalue_th

decimal, threshold of pvalue below which test_alternative_hyp is considered significant. If "two.sided", then pvalue_th is splitted in two for each side (preserved/not preserved).
n_threads

integer, number of threads that can be used to paralellise the computing
...

any other parameter compatible with modulePreservation

Details

Conditions will be based on names of data_expr_list. Please do not use numbers for conditions names as modules are often named this way
The final comparison output is a combination of the permutation test and the Z summary statistic. Comparison value is set to "preserved" when both return "preserved", "moderately preserved" when Z summary return it, "unpreserved" when permutation test is not significant and Z summary return "unpreserved", and "inconclusive" when the two values are opposite
To avoid recalculation, correlations matrices can be obtain by setting keep_cor_mat in build_net to TRUE.
Description of the 7 topological features used for preservation testing is available in modulePreservation.

Value

A nested list where first element is each ref provided, second level each condition to test, and then elements containing information on the comparison. See NetRep::modulePreservation() for more detail.

Examples

expr_by_cond <- list(cond1 = kuehne_expr[1:24, 1:350],
                     cond2 = kuehne_expr[25:48, 1:350])
net_by_cond <- lapply(expr_by_cond, build_net, cor_func = "spearman",
                      n_threads = 1, keep_matrices = "both")
mod_by_cond <- mapply(detect_modules, expr_by_cond,
                      lapply(net_by_cond, `[[`, "network"),
                      MoreArgs = list(detailled_result = TRUE),
                      SIMPLIFY = FALSE)
comparison <- compare_conditions(expr_by_cond,
                                 lapply(net_by_cond, `[[`, "adja_mat"),
                                 lapply(net_by_cond, `[[`, "cor_mat"),
                                 lapply(mod_by_cond, `[[`, "modules"),
                                 n_perm = 100)

Modules detection in a network

Description

Detect the modules by hierarchical clustering .

Usage

detect_modules(
  data_expr,
  network,
  min_module_size = min(20, ncol(data_expr)/2),
  clustering_th = NULL,
  merge_close_modules = TRUE,
  merge_threshold = 0.75,
  detailled_result = TRUE,
  pam_respects_dendro = FALSE,
  ...
)

Arguments

data_expr

matrix or data.frame or SummarizedExperiment, expression data with genes as column and samples as row.
network

matrix or data.frame, strengh of gene co-expression (edge values).
min_module_size

integer, lowest number of gene allowed in a module. If none provided, estimated.
clustering_th

float, threshold to be used by the clustering method. For now cutreeDynamic.
merge_close_modules

boolean, does closest modules (based on eigengene) should be merged together.
merge_threshold

float, eigengenes correlation value over which close modules will be merged. Must be in ]0;1[. See mergeCloseModules
detailled_result

boolean, does pre-merge modules (if applicable) and dendrogram included in output.
pam_respects_dendro

boolean, If TRUE, the Partitioning Around Medoids (PAM) stage will respect the dendrogram in the sense that objects and small clusters will only be assigned to clusters that belong to the same branch that the objects or small clusters being assigned belong to.
...

any other parameter compatible with mergeCloseModules

Value

list containing modules detected, modules_eigengenes, and if asked for, modules pre-merge and dendrograms of genes and merged modules

Examples

df <- kuehne_expr[1:24, 1:350]
net <- build_net(df, n_threads = 1)
detect_modules(df, net$network)

Filtering genes with low variability

Description

Remove low variating genes based on the percentage given and the type of variation specified.

Usage

filter_low_var(data_expr, pct = 0.8, type = c("mean", "median", "mad"))

Arguments

data_expr

matrix or data.frame or SummarizedExperiment, table of expression values (either microarray or RNA-seq), with genes as column and samples as row
pct

float, percentage of gene to keep, value must be in ]0;1[
type

string, function name used for filtration. Should be either "mean", "median", or "mad"

Value

A data.frame of filtered genes

Examples

df <- matrix(abs(rnorm(15*45)), 15)
colnames(df) <- paste0("gene_", seq_len(ncol(df)))
rownames(df) <- paste0("sample_", seq_len(nrow(df)))
df_filtered <- filter_low_var(df)

Filtering of low counts

Description

Keeping genes with at least one sample with count above min_count in RNA-seq data.

Usage

filter_RNA_seq(
  data_expr,
  min_count = 5,
  method = c("at least one", "mean", "all")
)

Arguments

data_expr

matrix or data.frame or SummarizedExperiment, table of expression values (either microarray or RNA-seq), with genes as column and samples as row.
min_count

integer, minimal number of count to be considered in method.
method

string, name of the method for filtering. Must be one of "at least one", "mean", or " all"

Details

Low counts in RNA-seq can bring noise to gene co-expression module building, so filtering them help to improve quality.

Value

A data.frame of filtered genes

Examples

df <- matrix(abs(rnorm(15*45)), 15) * 3
colnames(df) <- paste0("gene_", seq_len(ncol(df)))
rownames(df) <- paste0("sample_", seq_len(nrow(df)))
df_filtered <- filter_RNA_seq(df)

Calculating best fit of a power low on correlation matrix computed on expression data

Description

Adjust a correlation matrix depending of the type of network, then try to parameter a power law for best fit

Usage

get_fit.cor(
  cor_mat,
  fit_cut_off = 0.9,
  network_type = c("unsigned", "signed", "signed hybrid"),
  block_size = NULL,
  ...
)

Arguments

cor_mat

matrix or data.frame of genes correlation.
fit_cut_off

float, cut off by which R^2 (coefficient of determination) will be thresholded. Must be in ]0;1[.
network_type

string giving type of network to be used. Either "unsigned", "signed", "signed hybrid". See details.
block_size

integer giving size of blocks by which operations can be proceed. Helping if working with low capacity computers. If null, will be estimated.
...

any other parameter compatible with pickSoftThreshold.fromSimilarity

Details

network_type indicate which transformation will be applied on the correlation matrix to return the similarity score.

signed

will modify the range [-1;1] to [0.5;1.5] (because of log10 beeing used for scale free index computation)

unsigned

will return absolute value (moving from [-1;1] to [0;1])

signed hybrid

will replace all negative values by 0 (moving from [-1;1] to [0;1])

Value

A list containing power of the law for best fit, fit table, and metadata about the arguments used.

Examples

get_fit.cor(cor_mat = cor(kuehne_expr[, seq_len(100)]))

Calculating best fit of a power low on expression data

Description

Computes correlation matrix of the gene expression data, adjust it depending of the type of network, then try to parameter a power law for best fit

Usage

get_fit.expr(
  data_expr,
  fit_cut_off = 0.9,
  cor_func = c("pearson", "spearman", "bicor", "other"),
  your_func = NULL,
  network_type = c("unsigned", "signed", "signed hybrid"),
  block_size = NULL,
  ...
)

Arguments

data_expr

matrix or data.frame or SummarizedExperiment, expression data with genes as column and samples as row.
fit_cut_off

float, cut off by which R^2 (coefficient of determination) will be thresholded. Must be in ]0;1[.
cor_func

string specifying correlation function to be used. Must be one of "pearson", "spearman", "bicor", "other". If "other", your_func must be provided
your_func

function returning correlation values. Final values must be in [-1;1]
network_type

string giving type of network to be used. Either "unsigned", "signed", "signed hybrid". See details.
block_size

integer giving size of blocks by which operations can be proceed. Helping if working with low capacity computers. If null, will be estimated.
...

any other parameter compatible with pickSoftThreshold.fromSimilarity

Details

network_type indicate which transformation will be applied on the correlation matrix to return the similarity score.

signed

will modify the range [-1;1] to [0.5;1.5] (because of log10 beeing used for scale free index computation)

unsigned

will return absolute value (moving from [-1;1] to [0;1])

signed hybrid

will replace all negative values by 0 (moving from [-1;1] to [0;1])

Value

A list containing power of the law for best fit, fit table, and metadata about the arguments used.

Examples

get_fit.expr(kuehne_expr[, seq_len(100)])

Determine hub genes based on degree

Description

Remove edges from the graph which value is under weight_th then compute degree of each node (gene). Hub gene are genes whose degree value is above average degree value of the thresholded network.

Usage

get_hub_degree(network, modules = NULL, weight_th = 0.2)

Arguments

network

matrix or data.frame, square table representing connectivity between each genes as returned by build_net. Can be whole network or a single module.
modules

list, modules defined as list of gene vectors. If null, network is supposed to be the whole network or an already split module
weight_th

decimal, weight threshold under or equal to which edges will be removed

Details

GWENA natively build networks using WGCNA. These networks are complete in a graph theory sens, meaning all nodes are connected to each other. Therefore a threshold need to be applied so degree of all nodes isn't the same.

Value

A list of vectors, or single vector of gene names

Examples

mat <- matrix(runif(40*40), 40)
colnames(mat) <- paste0("gene_", seq_len(ncol(mat)))
rownames(mat) <- paste0("gene_", seq_len(nrow(mat)))
get_hub_degree(mat)

Determine hub genes inside each module

Description

Return genes considered as hub genes inside each module of a network following the selected method. Method will be lauched with default parameters. If specific parameters desired, please use directly the function get_hub_... itself.

Usage

get_hub_genes(
  network,
  modules = NULL,
  method = c("highest connectivity", "superior degree", "Kleinberg's score")
)

Arguments

network

matrix or data.frame, square table representing connectivity between each genes as returned by build_net. Can be whole network or a single module.
modules

list, modules defined as list of gene vectors. If null, network is supposed to be the whole network or an already split module
method

string, name of the method to be used for hub gene detection. See details.

Details

highest connectivity

Select the top n (n depending on parameter given) highest connected genes. Similar to WGCNA::chooseTopHubInEachModule.

superior degree

Select genes which degree is greater than average connection degree of the network. Definition from network theory.

Kleinberg's score

Select genes which Kleinberg's score superior to provided threshold.

Value

A list of vectors representing hub genes, by module

Examples

mat <- matrix(runif(40*40), 40)
colnames(mat) <- paste0("gene_", seq_len(ncol(mat)))
rownames(mat) <- paste0("gene_", seq_len(nrow(mat)))
get_hub_genes(mat)

Determine hub genes based on connectivity

Description

Compute connectivity of each gene by module if provided or for whole network if not, and return the top_n highest connected ones.

Usage

get_hub_high_co(network, modules = NULL, top_n = 5)

Arguments

network

matrix or data.frame, square table representing connectivity between each genes as returned by build_net. Can be whole network or a single module.
modules

list, modules defined as list of gene vectors. If null, network is supposed to be the whole network or an already split module
top_n

integer, number of genes to be considered as hub genes

Value

A list of vectors, or single vector of gene names

Examples

mat <- matrix(runif(40*40), 40)
colnames(mat) <- paste0("gene_", seq_len(ncol(mat)))
rownames(mat) <- paste0("gene_", seq_len(nrow(mat)))
get_hub_high_co(mat)

Determine hub genes based on Kleinberg's score

Description

Compute Kleinberg's score (defined as the principal eigenvector of A*t(A), where A is the similarity matrix of the graph) of each gene by module if provided or for whole network if not, and return the top_n highest ones.

Usage

get_hub_kleinberg(network, modules = NULL, top_n = 5, k_th = NULL)

Arguments

network

matrix or data.frame, square table representing connectivity between each genes as returned by build_net. Can be whole network or a single module.
modules

list, modules defined as list of gene vectors. If null, network is supposed to be the whole network or an already split module
top_n

integer, number genes to be considered as hub genes
k_th

decimal, Kleinberg's score threshold above or equal to which genes are considered as hubs

Details

If you provide a top_n value, you can't provide a k_th value and vice versa. If none of them is provided, top_n = 5. For more information on Kleinberg's score, look at hub_score from igraph.

Value

A list of vectors, or single vector of gene names

Examples

mat <- matrix(runif(40*40), 40)
colnames(mat) <- paste0("gene_", seq_len(ncol(mat)))
rownames(mat) <- paste0("gene_", seq_len(nrow(mat)))
get_hub_degree(mat)
get_hub_kleinberg(mat, top_n = NULL, k_th = 0.9)

Detect sub clusters

Description

Use a partitioning around medoid (PAM, or k-medoid) clustering method to detect clusters into a provided module using the strength matrix of the network

Usage

get_sub_clusters(network, seq_k = seq_len(15), fit_plot = TRUE, ...)

Arguments

network

matrix or data.frame, strength of gene co-expression (edge values).
seq_k

vector, sequence of k number of cluster to test
fit_plot

boolean, does the plot with silhouette coefficient depending on the k tested should be plotted.
...

any other parameter compatible with the pam function.

Value

data.frame, a two cols table with the gene id in the first one, and the cluster number assignation in the second one.

Examples

df <- kuehne_expr[1:24, 1:350]
net <- build_net(df, n_threads = 1)
mods <- detect_modules(df, net$network)
net_mod_1 <- net$network[mods$modules$`1`, mods$modules$`1`]
get_sub_clusters(net_mod_1)

Mimicking ggplot palette Source : https://stackoverflow.com/questions/8197559/emulate-ggplot2-default-color-palette

Description

Mimicking ggplot palette Source : https://stackoverflow.com/questions/8197559/emulate-ggplot2-default-color-palette

Usage

gg_palette(n)

Arguments

n

integer, number of colors wanted

Value

character vector, haxadecimal colors of length n

Transcriptomic muscle data from GTEx consorsium RNA-seq data

Description

A subset of GTEx RNA-seq dataset containing read counts collapsed to gene level.

Usage

gtex_expr

Format

A data frame with 50 rows (samples) and 15000 columns (genes)

Source

https://gtexportal.org/home/datasets

Traits data linked to samples in transcriptomic data from GTEx

Description

A dataset containing phenotypes of donors. From public data. Note: protected data contain more information but require dbGap accessh (see https://gtexportal.org/home/protectedDataAccess).

Usage

gtex_traits

Format

A data frame with 50 rows (samples) and 4 columns :

SUBJID

Subject ID, GTEx Public Donor ID

SEX

Sex, donor's Identification of sex based upon self-report : 1=Male, 2=Female

AGE

Age range, elapsed time since birth in years

DTHHRDY

Hardy Scale : 0=Ventilator Case, 1=Violent and fast death, 2=Fast death of natural causes, 3=Intermediate death, 4=Slow death)

Source

https://gtexportal.org/home/datasets

Determine if an object is a data_expr in sens of GWENA

Description

Check an object to be a data.frame or a matrix compatible of genes and samples.

Usage

is_data_expr(data_expr)

Arguments

data_expr

matrix or data.frame, expression data with genes as column and samples as row.

Value

list, a boolean as first element and in second element NULL or the reason why boolean is set to FALSE

Examples

expr <- matrix(runif(15*40), 15)
colnames(expr) <- paste0("gene_", seq_len(ncol(expr)))
rownames(expr) <- paste0("gene_", seq_len(nrow(expr)))
is_data_expr(expr)

Determine if an object is a gost object

Description

Check content of a given object to determine if it's a gost object

Usage

is_gost(gost_result)

Arguments

gost_result

list, gprofiler2::gost result

Value

list, a boolean as first element and in second element NULL or the reason why boolean is set to FALSE

Examples

single_module <- c("BIRC3", "PMAIP1", "CASP8", "JUN", "BCL2L11", "MCL1",
                   "IL1B", "SPTAN1", "DIABLO", "BAX", "BIK", "IL1A", "BID",
                   "CDKN1A", "GADD45A")
single_module_enriched <- bio_enrich(single_module)
is_gost(single_module_enriched)

Determine if an object is a module or a list of modules

Description

Check content of a given object to determine if it's a module or a list of modules, meaning a single vector of characters which are gene names, or a named list of these vectors

Usage

is_module(module, is_list = FALSE)

Arguments

module

vector or list, object to test to be a module or list of modules
is_list

boolean, indicate if module must be tested as a single module or a list of modules

Value

list, a boolean as first element and in second element NULL or the reason why boolean is set to FALSE

Examples

single_module <- c("BIRC3", "PMAIP1", "CASP8", "JUN", "BCL2L11", "MCL1",
                   "IL1B", "SPTAN1", "DIABLO", "BAX", "BIK", "IL1A", "BID",
                   "CDKN1A", "GADD45A")
is_module(single_module)

multi_module <- list(mod1 = single_module,
                     mod2 = c("TAF1C", "TARBP2", "POLH", "CETN2", "POLD1",
                     "CANT1", "PDE4B", "DGCR8", "RAD51", "SURF1", "PNP",
                     "ADA", "NME3", "GTF3C5", "NT5C"))
is_module(multi_module$modules, is_list = TRUE)

Determine if an object is a network

Description

Check content of a given object to determine if it's a network, meaning a squared matrix of similarity score between genes.

Usage

is_network(network)

Arguments

network

matrix or data.frame, object to test to be a network

Value

list, a boolean as first element and in second element NULL or the reason why boolean is set to FALSE

Examples

net <- matrix(runif(40*40), 40)
colnames(net) <- paste0("gene_", seq_len(ncol(net)))
rownames(net) <- paste0("gene_", seq_len(nrow(net)))
is_network(net)

Join gprofiler2::gost results

Description

Takes list of gprofiler2::gost results and join them. Usefull to join results of gprofiler2::gost with custom gmt to other gprofiler2::gost results.

Usage

join_gost(gost_result)

Arguments

gost_result

list of gprofiler2::gost result

Details

First element of the list is taken as reference for checks on gost_result elements compatibility. If warnings returned, value from reference will be used. Also, timestamp is set to timestamp of the join

Value

A gprofiler2::gost result

Examples

query <- c("ENSG00000184349", "ENSG00000158955", "ENSG00000091140",
           "ENSG00000163114", "ENSG00000163132", "ENSG00000019186")
g1 <- gprofiler2::gost(query, sources = "GO")
g2 <- gprofiler2::gost(query, sources = "REAC")
gj <- join_gost(list(g1,g2))

Transcriptomic data from the Kuehne et al. publication

Description

A dataset containing the expression levels collapsed to the gene level. Obtained from script provided in additional data n°10 runned on GSE85358 and reduced from probe to gene by WGCNA::collapseRows with median as fucntion.

Usage

kuehne_expr

Format

A data frame with 48 rows (samples) and 15801 columns (genes).

Source

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-3547-3

Traits data linked to samples in transcriptomic data from the Kuehne et al. publication

Description

A dataset containing the phenotype of the donors and technical information about the experiment

Usage

kuehne_traits

Format

A data frame with 48 rows (samples) and 5 columns :

Slide

Reference number of the microarray's slide.

Array

Array number, 8 by slide usually

Exp

Experiment number

Condition

Either old (between 55 and 66 years old) or young (between 20 to 25 years old)

Age

Real age of the donor

Source

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-3547-3

Heatmap of comparison statistics

Description

Plot heatmap of p values for the module comparison statistics evaluated through the permutation test.

Usage

plot_comparison_stats(
  comparison_pvalues,
  pvalue_th = 0.05,
  low_color = "#031643",
  pvalue_th_color = "#A0A3D3",
  unsignificant_color = "#FFFFFF",
  text_angle = 90
)

Arguments

comparison_pvalues

matrix or data.frame, table containing the p values for the statistics on each module
pvalue_th

decimal, threshold of pvalue below which statistics are considered as significant
low_color, pvalue_th_color, unsignificant_color

string, color to use as lower, middle, and higher end of the legend. Can either be the color name or hexadecimal code (e.g.: “red” or “#FF1234” )
text_angle

integer, angle in [0,360] of the x axis labels.

Value

A ggplot object representing a heatmap of the comparison statistics for each module

Examples

df <- data.frame(avg.weight = abs(rnorm(4, 0.1, 0.1)),
                 coherence = abs(rnorm(4, 0.1, 0.1)),
                 cor.cor = abs(rnorm(4, 0.1, 0.1)),
                 cor.degree = abs(rnorm(4, 0.1, 0.1)),
                 cor.contrib = abs(rnorm(4, 0.1, 0.1)),
                 avg.cor = abs(rnorm(4, 0.1, 0.1)),
                 avg.contrib = abs(rnorm(4, 0.1, 0.1)))
plot_comparison_stats(df)

Plot module from bio_enrich

Description

Wrapper of the gprofiler2::gostplot function. Adding support of colorblind palet and selection of subsets if initial multiple query, and/or sources to plot.

Usage

plot_enrichment(
  enrich_output,
  modules = "all",
  sources = "all",
  colorblind = TRUE,
  custom_palette = NULL,
  ...
)

Arguments

enrich_output

list, bio_enrich result which are in fact gprofiler2::gost output.
modules

string or vector of characters designing the modules to plot. "all" by default to plot every module.
sources

string or vector of characters designing the sources to plot. "all" by default to plot every source.
colorblind

boolean, indicates if a colorblind friendly palette should be used.
custom_palette

vector of character, colors to be used for plotting.
...

any other parameter you can provide to gprofiler2::gostplot.

Details

Note: The colorblind friendly palette is limited to maximum 8 colors, therefore 8 sources of enrichment.

Value

A plotly object representing enrichment for specified modules

Examples

custom_path <- system.file("extdata", "h.all.v6.2.symbols.gmt",
                           package = "GWENA", mustWork = TRUE)
multi_module <- list(mod1 = c("BIRC3", "PMAIP1", "CASP8", "JUN", "BCL2L11",
                              "MCL1", "IL1B", "SPTAN1", "DIABLO", "BAX",
                              "BIK", "IL1A", "BID", "CDKN1A", "GADD45A"),
                     mod2 = c("TAF1C", "TARBP2", "POLH", "CETN2", "POLD1",
                              "CANT1", "PDE4B", "DGCR8", "RAD51", "SURF1",
                              "PNP", "ADA", "NME3", "GTF3C5", "NT5C"))
multi_module_enriched <- bio_enrich(multi_module, custom_path)
plot_enrichment(multi_module_enriched)

Modules expression profiles

Description

Plot expression profiles for all modules with eigengene highlighted

Usage

plot_expression_profiles(
  data_expr,
  modules,
  eigengenes = NULL,
  alpha_expr = 0.3,
  ...
)

Arguments

data_expr

matrix or data.frame or SummarizedExperiment, expression data with genes as column and samples as row.
modules

vector, id (whole number or string) of modules associated to each gene.
eigengenes

matrix or data.frame, eigeingenes of the provided modules. If null, new ones will be computed with a PCA.
alpha_expr

numeric, transparency of the expression lines. Must be a value betweem 0 (transparent) and 1 (opaque)
...

additional parameters to pass to ggplot2::theme

Details

The sign of the eigengenes from detect_modules may differ from the ones computed by the pca if no eigengenes is provided to plot_expression_profiles and therefore the plot itself. This is du to the sign indeterminancy property from the singular value decomposition.

Value

A ggplot representing expression profile and eigengene by module

Examples

df <- kuehne_expr[1:24, 1:350]
net <- build_net(df, n_threads = 1)
detection <- detect_modules(df, net$network, detailled_result = TRUE)
plot_expression_profiles(df, detection$modules, detection$modules_eigengenes)

Plot co-expression network

Description

Display a graph representing the co-expression network and different informations like hubs, enrichments

Usage

plot_module(
  graph_module,
  hubs = NULL,
  groups = NULL,
  lower_weight_th = NULL,
  upper_weight_th = NULL,
  title = "Module",
  degree_node_scaling = TRUE,
  node_scaling_min = 1,
  edge_scaling_min = 0.2,
  node_scaling_max = 6,
  edge_scaling_max = 1,
  nb_row_legend = 6,
  layout = "auto",
  zoom = 1,
  vertex.label.cex = 0.7,
  vertex.label.color = "gray20",
  vertex.label.family = "Helvetica",
  edge.color = "gray70",
  vertex.frame.color = "white",
  vertex.color = "gray60",
  vertex.label.dist = 1,
  legend_cex = 0.8,
  groups_palette = NULL,
  window_x_min = -1,
  window_x_max = 1,
  window_y_min = -1,
  window_y_max = 1,
  legend = TRUE,
  ...
)

Arguments

graph_module

igraph object, module to plot.
hubs

character vector or numeric vector with names, optionnal, vector of gene names or vector of numeric values named with gene names.
groups

matrix or data.frame, a two cols table with the gene id in the first one, and the group assignation in the second one.
lower_weight_th, upper_weight_th

decimal, weight threshold above lower_weight_th or below upper_weight_th which edges will be removed.
title

string, main title that will be displayed on the plot.
degree_node_scaling

boolean, indicates if node size should represent the degree of this node.
node_scaling_min, node_scaling_max

integer, if degree_node_scaling is TRUE, it is the min/max size of the node, else it is the exact size of all node.
edge_scaling_min, edge_scaling_max

integer, min/max width of the edge
nb_row_legend

integer, number of levels in the legend.
layout

numeric matrix or function or string, numeric matrix for nodes coordinates, or function for layout, or name of a layout function available in igraph. Default "auto" will choose the best layout depending on the graph. For more information, see igraph.plotting.
zoom

integer, scaling factor by which it's possible to have compact graph (< 1) or larger graph (> 1) display.
vertex.label.cex, legend_cex

float, font size for vertex labels. It is interpreted as a multiplication factor of some device-dependent base font size. If 0, no labels displayed.
vertex.label.color, edge.color, vertex.frame.color, vertex.color

character and/or integer vector , color of the labels. It may either contain integer values, named colors or RGB specified colors with three or four bytes. All strings starting with ‘#’ are assumed to be RGB color specifications. It is possible to mix named color and RGB colors.
vertex.label.family

character, font family to be used for vertex labels.
vertex.label.dist

integer, distance of the label from the center of the vertex. If it is 0 then the label is centered on the vertex. If it is 1 then the label is displayed beside the vertex.
groups_palette

character and/or integer vector, vertices group palette of colors for the groups specified. It may either contain integer values, named colors or RGB specified colors with three or four bytes. All strings starting with ‘#’ are assumed to be RGB color specifications. It is possible to mix named color and RGB colors.
window_x_min

decimal, value for the bottom limit of the window.
window_x_max

decimal, value for the top limit of the window.
window_y_min

decimal, value for the left limit of the window.
window_y_max

decimal, value for the right limit of the window.
legend

boolean, indicates if the legend should be plotted.
...

any other parameter compatible with the plot.igraph function.

Details

Take care of you intend to compare modules' graphs, the same size of node will not correspond to the same values because of the scaling.

Value

matrix, layout of the graph as a two column matrix (x, y)

Examples

mat <- matrix(runif(40*40), 40)
g <- build_graph_from_sq_mat(mat)
plot_module(g, lower_weight_th = -0.5, upper_weight_th = 0.5)

Modules merge plot

Description

Plot a bipartite graph to see in which modules all modules have been merged

Usage

plot_modules_merge(
  modules_premerge,
  modules_merged,
  zoom = 1,
  vertex_size = 6,
  vertex_label_color = "gray20",
  vertex_label_family = "Helvetica",
  vertex_label_cex = 0.8,
  vertex_color = "lightskyblue",
  vertex_frame_color = "white",
  window_x_min = -1,
  window_x_max = 1,
  window_y_min = -1,
  window_y_max = 1,
  ...
)

Arguments

modules_premerge

vector, id (whole number or string) of module before merge associated to each gene.
modules_merged

vector, id (whole number or string) of module after merge associated to each gene.
zoom

decimal, value to which the display will be increased/decreased.
vertex_size

integer, size of the vertices.
vertex_label_color, vertex_color, vertex_frame_color

string, name of the color or hexadecimal code.
vertex_label_family

string, font family name.
vertex_label_cex

decimal, value for font size.
window_x_min

decimal, value for the bottom limit of the window.
window_x_max

decimal, value for the top limit of the window.
window_y_min

decimal, value for the left limit of the window.
window_y_max

decimal, value for the right limit of the window.
...

additional arguments to be passed to igraph::plot.igraph().

Details

Both vectors must be in the same gene order before passing them to the function. No check is applied on this.

Value

The layout of the plot

Examples

df <- kuehne_expr[1:24, 1:350]
net <- build_net(df, n_threads = 1)
detection <- detect_modules(df, net$network, detailled_result = TRUE)
plot_modules_merge(modules_premerge = detection$modules_premerge,
                   modules_merged = detection$modules)

Heatmap of modules phenotpic association

Description

Plot a heatmap of the correlation between all modules and the phenotypic variables and the p value associated

Usage

plot_modules_phenotype(
  modules_phenotype,
  pvalue_th = 0.05,
  text_angle = 90,
  ...
)

Arguments

modules_phenotype

list, data.frames of correlation and pvalue associated
pvalue_th

float, threshold in ]0;1[ under which module will be considered as significantly associated
text_angle

integer, angle in [0,360] of the x axis labels.
...

any other parameter you can provide to ggplot2::theme

Value

A ggplot object representing a heatmap with phenotype association and related pvalues

Examples

eigengene_mat <- data.frame(mod1 = rnorm(20, 0.1, 0.2),
mod2 = rnorm(20, 0.2, 0.2))
phenotype_mat <- data.frame(phenA = sample(c("X", "Y", "Z"), 20,
                            replace = TRUE),
                            phenB = sample(c("U", "V"), 20,
                            replace = TRUE),
                            stringsAsFactors = FALSE)
association <- associate_phenotype(eigengene_mat, phenotype_mat)
plot_modules_phenotype(association)

Muting a function

Description

Prevent a function to output multiple message. Source: https://r.789695.n4.nabble.com/Suppressing-output-e-g-from-cat-td859876.html

Usage

quiet(func)

Arguments

func

Function who need to be muted.

Value

Nothing, just mute the called function

Calculating Z summary

Description

Use the topological metrics and permutations from output of modulePreservation to compute a Z summary (a composite preservation statistic) as defined by https://doi.org/10.1371/journal.pcbi.1001057

Usage

z_summary(observed_stat, permutations_array)

Arguments

observed_stat

matrix, bidimensional matrix containing the topological matrix computed for each module by modulePreservation (the element observed). Modules are in row, metrics are in column.
permutations_array

matrix, tridimensional matrix containing the topological matrix computed for each module by modulePreservation (the element observed). Modules are in dim 1, metrics are in dim 2, permutations are in dim 3.

Details

The original Zsummary composite preservation statistic was defined by Langfelder et al. (2011). However this method use the metric from modulePreservation since they it handle better large and multiple testing correction.

Value

A named vector of the z summary statistic with the module id as name.

Examples

expr_by_cond <- list(cond1 = kuehne_expr[1:24, 1:350],
                     cond2 = kuehne_expr[25:48, 1:350])
net_by_cond <- lapply(expr_by_cond, build_net, cor_func = "spearman",
                      n_threads = 1, keep_matrices = "both")

mods_labels <- setNames(
  sample(1:6, 350, replace = TRUE,
         prob = c(0.05, 0.4, 0.25, 0.15, 0.1, 0.05)),
  colnames(expr_by_cond$cond1))

netrep_res <- NetRep::modulePreservation(
  network = lapply(net_by_cond, `[[`, "adja_mat"),
  data = lapply(expr_by_cond, as.matrix),
  correlation = lapply(net_by_cond, `[[`, "cor_mat"),
  moduleAssignments = mods_labels,  nPerm = 100)

z_summary(netrep_res$observed, netrep_res$nulls)

mod_by_cond <- mapply(detect_modules, expr_by_cond,
                      lapply(net_by_cond, `[[`, "network"),
                      MoreArgs = list(detailled_result = TRUE),
                      SIMPLIFY = FALSE)

comparison <- compare_conditions(expr_by_cond,
                                 lapply(net_by_cond, `[[`, "adja_mat"),
                                 lapply(net_by_cond, `[[`, "cor_mat"),
                                 lapply(mod_by_cond, `[[`, "modules"),
                                 n_perm = 100)

z_summary(comparison$result$cond1$cond2$observed,
          comparison$result$cond1$cond2$nulls)