Package 'GeDi' reference manual

Title:	Defining and visualizing the distances between different genesets
Description:	The package provides different distances measurements to calculate the difference between genesets. Based on these scores the genesets are clustered and visualized as graph. This is all presented in an interactive Shiny application for easy usage.
Authors:	Annekathrin Nedwed [aut, cre] , Federico Marini [aut]
Maintainer:	Annekathrin Nedwed <[email protected]>
License:	MIT + file LICENSE
Version:	1.1.1
Built:	2024-07-26 03:34:31 UTC
Source:	https://github.com/bioc/GeDi

Check genesets format

Description

Check if the input genesets have the expected format for this app

Usage

.checkGenesets(
  genesets,
  col_name_genesets = "Genesets",
  col_name_genes = "Genes"
)
.checkGenesets(
  genesets,
  col_name_genesets = "Genesets",
  col_name_genes = "Genes"
)

Arguments

`genesets`	a `list`, A `list` of genesets where each genesets is represented by `list` of genes.
`col_name_genesets`	character, the name of the column in which the geneset ids are listed. Defaults to "Genesets".
`col_name_genes`	character, the name of the column in which the genes are listed. Defaults to "Genes".

Value

A validated and formatted genesets data frame.

a data.frame, Protein-protein interaction (PPI) network data frame. The object is expected to have three columns, Gene1 and Gene2 which specify the gene names of the interacting proteins in no particular order (symmetric interaction) and a column combined_score which is a numerical value of the strength of the interaction.

Value

A validated and formatted PPI data frame.

Check distance scores format

Description

Check if the provided distance scores have the expected format for this app

Usage

.checkScores(genesets, distance_scores)
.checkScores(genesets, distance_scores)

Arguments

`genesets`	a `list`, A `list` of genesets where each genesets is represented by `list` of genes.
`distance_scores`	A `Matrix::Matrix()` or object, A matrix with numerical (distance) scores.

Value

A validated and formatted distance_scores Matrix::Matrix().

Filter Genesets from the input data

Description

Filter a preselected list of genesets from a data.frame of genesets

Usage

.filterGenesets(remove, df_genesets)
.filterGenesets(remove, df_genesets)

Arguments

`remove`	a `list`, A list of geneset names to be removed
`df_genesets`	a `data.frame`, A `data.frame` with at least two columns. One should be called `Geneset`, containing the names/identifiers of the genesets in the data. The second column should be called `Genes` and contains one string of the genes contained in each geneset.

Value

A data.frame containing information about filtered genesets

Make an educated guess on the separator character

Description

This function tries to guess which separator was used in a list of delimited strings.

Usage

.findSeparator(stringList, sepList = c(",", "\t", ";", " ", "/"))
.findSeparator(stringList, sepList = c(",", "\t", ";", " ", "/"))

Arguments

`stringList`	`list`, a `list` of strings
`sepList`	`list`, containing the candidates for being identified as separators. Defaults to `c(",", "\t", ";"," ", "/")`.

Value

character, corresponding to the guessed separator. One of "," (comma), "\t" (tab), ";" (semicolon)," " (whitespace) or "/" (backslash).

References

See https://github.com/federicomarini/ideal for details on the original implementation.

Map each geneset to the cluster it belongs

Description

Map each geneset to the cluster it belongs and return the information as a data.frame

Usage

.getClusterDatatable(cluster, gs_names, gs_description)
.getClusterDatatable(cluster, gs_names, gs_description)

Arguments

`cluster`	A `list` of clusters
`gs_names`	A vector of geneset names
`gs_description`	A vector of descriptions for each geneset

Value

A data.frame mapping each geneset to the cluster(s) it belongs to

Title

Description

Title

Usage

.getGenesetDescriptions(genesets)
.getGenesetDescriptions(genesets)

Arguments

genesets

a data.frame, A data.frame with at least two columns. One should be called Geneset, containing the names/identifiers of the genesets in the data. The second column should be called Genes and contains one string of the genes contained in each geneset.

Value

a list of geneset descriptions

Determine the number of cores to use for a function

Description

Determine the number of CPU cores the scoring functions should use when computing the distance scores.

Usage

.getNumberCores(n_cores = NULL)
.getNumberCores(n_cores = NULL)

Arguments

n_cores

numeric, number of cores to use for the function. Defaults to Null in which case the function takes half of the available cores.

Value

Number of CPU cores to be used.

Generate a `data.frame` of graph metrics

Description

Generate a data.frame of the graph metrics degree, betweenness, harmonic centrality and clustering coefficient for each node in a given graph.

Usage

.graphMetricsGenesetsDT(g, genesets)
.graphMetricsGenesetsDT(g, genesets)

Arguments

`g`	A igraph graph object
`genesets`	A `data.frame` of genesets with a column `Genesets` containing geneset identifiers and a column `Genes` containing the genes belonging to each geneset

Value

A data.frame of geneset extended by columns for the degree, betweenness, harmonic centrality and clustering coefficient for each geneset.

Make an educated guess on the separator character

Description

This function tries to guess which separator was used in a text delimited file.

Usage

.sepguesser(file, sep_list = c(",", "\t", ";", " ", "/"))
.sepguesser(file, sep_list = c(",", "\t", ";", " ", "/"))

Arguments

`file`	a character, location of a file to read data from.
`sep_list`	a `list`, containing the candidates for being identified as separators. Defaults to `c(",", "\t", ";"," ", "/")`.

Value

A character, corresponding to the guessed separator. One of "," (comma), "\t" (tab), ";" (semicolon)," " (whitespace) or "/" (backslash).

References

See https://github.com/federicomarini/ideal for details on the original implementation.

Build a cluster graph

Description

Build a igraph from cluster information, connecting nodes which belong to the same cluster.

Usage

buildClusterGraph(
  cluster,
  geneset_df,
  gs_ids,
  color_by = NULL,
  gs_names = NULL
)
buildClusterGraph(
  cluster,
  geneset_df,
  gs_ids,
  color_by = NULL,
  gs_names = NULL
)

Arguments

`cluster`	list, a `list` of clusters, where each cluster member is indicated by a numeric value.
`geneset_df`	`data.frame`, a `data.frame` of genesets with at least two columns, one called `Genesets` containing geneset identifiers and one called `Genes` containing a list of genes belonging to the individual genesets.
`gs_ids`	vector, a vector of geneset identifiers, e.g. the `Genesets` column of `geneset_df`.
`color_by`	character, a column name of `geneset_df` which is used to color the nodes of the resulting graph. The column should ideally contain a numeric measurement. Defaults to NULL and nodes will remain uncolored.
`gs_names`	vector, a vector of geneset descriptions/names, e.g. the `Term` / `Description` column of `geneset_df`.

Value

An igraph object to be further manipulated or processed/plotted (e.g. via igraph::plot.igraph() or visNetwork::visIgraph())

Examples

cluster <- list(c(1:5), c(6:9, 1))
genes <- list(
  c("PDHB", "VARS2"), c("IARS2", "PDHA1"),
  c("AAAS", "ABCE1"), c("ABI1", "AAR2"), c("AATF", "AMFR"),
  c("BMS1", "DAP3"), c("AURKAIP1", "CHCHD1"), c("IARS2"),
  c("AHI1", "ALMS1")
)
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h", "i")
gs_ids <- c(1:9)
geneset_df <- data.frame(
  Genesets = gs_names,
  value = rep(1, 9)
)
geneset_df$Genes <- genes
graph <- buildClusterGraph(
  cluster = cluster,
  geneset_df = geneset_df,
  gs_ids = gs_ids,
  color_by = "value",
  gs_names = gs_names
)
cluster <- list(c(1:5), c(6:9, 1))
genes <- list(
  c("PDHB", "VARS2"), c("IARS2", "PDHA1"),
  c("AAAS", "ABCE1"), c("ABI1", "AAR2"), c("AATF", "AMFR"),
  c("BMS1", "DAP3"), c("AURKAIP1", "CHCHD1"), c("IARS2"),
  c("AHI1", "ALMS1")
)
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h", "i")
gs_ids <- c(1:9)
geneset_df <- data.frame(
  Genesets = gs_names,
  value = rep(1, 9)
)
geneset_df$Genes <- genes
graph <- buildClusterGraph(
  cluster = cluster,
  geneset_df = geneset_df,
  gs_ids = gs_ids,
  color_by = "value",
  gs_names = gs_names
)

Construct a graph

Description

Construct a graph from a given adjacency matrix

Usage

buildGraph(adjMatrix, geneset_df = NULL, gs_names = NULL)
buildGraph(adjMatrix, geneset_df = NULL, gs_names = NULL)

Arguments

`adjMatrix`	A `Matrix::Matrix()` indicating for which pair of nodes an edge should be added; 1 indicating an edge, 0 indicating no edge.
`geneset_df`	`data.frame`, a `data.frame` of genesets with at least two columns, one called `Genesets` containing geneset identifiers and one called `Genes` containing a list of genes belonging to the individual genesets.
`gs_names`	vector, a vector of geneset descriptions/names, e.g. the `Term` / `Description` column of `geneset_df`.

Value

An igraph object to be further manipulated or processed/plotted (e.g. via igraph::plot.igraph() or visNetwork::visIgraph())

Examples

adj <- Matrix::Matrix(0, 100, 100)
adj[c(80:100), c(80:100)] <- 1
geneset_names <- as.character(stats::runif(100, min = 0, max = 1))
rownames(adj) <- colnames(adj) <- geneset_names
graph <- buildGraph(adj)
adj <- Matrix::Matrix(0, 100, 100)
adj[c(80:100), c(80:100)] <- 1
geneset_names <- as.character(stats::runif(100, min = 0, max = 1))
rownames(adj) <- colnames(adj) <- geneset_names
graph <- buildGraph(adj)

Prepare data for `gsHistogram()`.

Description

Prepare the data for the gsHistogram() by generating a data.frame which maps geneset names / identifiers to the size of their size.

Usage

buildHistogramData(
  genesets,
  gs_names,
  gs_description = NULL,
  start = 0,
  end = 0
)
buildHistogramData(
  genesets,
  gs_names,
  gs_description = NULL,
  start = 0,
  end = 0
)

Arguments

`genesets`	a `list`, A `list` of genesets where each genesets is represented by `list` of genes.
`gs_names`	character vector, Name / identifier of the genesets in `genesets`
`gs_description`	Optional, a character vector containing a short description for each geneset
`start`	numeric, Optional, describes the minimum gene set size to include. Defaults to 0.
`end`	numeric, Optional, describes the maximum gene set size to include. Defaults to 0.

Value

A data.frame mapping geneset names to sizes

Examples

## Mock example showing how the data should look like
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h", "i")
genesets <- list(
  c("PDHB", "VARS2"), c("IARS2", "PDHA1"),
  c("AAAS", "ABCE1"), c("ABI1", "AAR2"), c("AATF", "AMFR"),
  c("BMS1", "DAP3"), c("AURKAIP1", "CHCHD1"), c("IARS2"),
  c("AHI1", "ALMS1")
)

p <- buildHistogramData(genesets, gs_names)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
p <- buildHistogramData(genes, macrophage_topGO_example_small$Genesets)
## Mock example showing how the data should look like
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h", "i")
genesets <- list(
  c("PDHB", "VARS2"), c("IARS2", "PDHA1"),
  c("AAAS", "ABCE1"), c("ABI1", "AAR2"), c("AATF", "AMFR"),
  c("BMS1", "DAP3"), c("AURKAIP1", "CHCHD1"), c("IARS2"),
  c("AHI1", "ALMS1")
)

p <- buildHistogramData(genesets, gs_names)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
p <- buildHistogramData(genes, macrophage_topGO_example_small$Genesets)

Calculate the Jaccard distance

Description

Calculate the Jaccard distance between two genesets.

Usage

calculateJaccard(a, b)
calculateJaccard(a, b)

Arguments

a, b

character vector, set of gene identifiers.

Value

The Jaccard distance of the sets.

Examples

## Mock example showing how the data should look like
a <- c("PDHB", "VARS2")
b <- c("IARS2", "PDHA1")
c <- calculateJaccard(a, b)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
jaccard <- calculateJaccard(genes[1], genes[2])
## Mock example showing how the data should look like
a <- c("PDHB", "VARS2")
b <- c("IARS2", "PDHA1")
c <- calculateJaccard(a, b)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
jaccard <- calculateJaccard(genes[1], genes[2])

Calculate the Kappa distance

Description

Calculate the Kappa distance between two genesets.

Usage

calculateKappa(a, b, all_genes)
calculateKappa(a, b, all_genes)

Arguments

`a`, `b`	character vector, set of gene identifiers.
`all_genes`	character vector, list of all (unique) genes available in the input data.

Value

The Kappa distance of the sets.

Examples

## Mock example showing how the data should look like
a <- c("PDHB", "VARS2")
b <- c("IARS2", "PDHA1")
all_genes <- c("PDHB", "VARS2", "IARS2", "PDHA1")
c <- calculateKappa(a, b, all_genes)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
c <- calculateKappa(genes[1], genes[2], unique(genes))
## Mock example showing how the data should look like
a <- c("PDHB", "VARS2")
b <- c("IARS2", "PDHA1")
all_genes <- c("PDHB", "VARS2", "IARS2", "PDHA1")
c <- calculateKappa(a, b, all_genes)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
c <- calculateKappa(genes[1], genes[2], unique(genes))

Calculate the Sorensen-Dice distance

Description

Calculate the Sorensen-Dice distance between two genesets.

Usage

calculateSorensenDice(a, b)
calculateSorensenDice(a, b)

Arguments

a, b

character vector, set of gene identifiers.

Value

The Sorensen-Dice distance of the sets.

Examples

#' ## Mock example showing how the data should look like
a <- c("PDHB", "VARS2")
b <- c("IARS2", "PDHA1")
c <- calculateSorensenDice(a, b)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
sd <- calculateSorensenDice(genes[1], genes[2])
#' ## Mock example showing how the data should look like
a <- c("PDHB", "VARS2")
b <- c("IARS2", "PDHA1")
c <- calculateSorensenDice(a, b)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
sd <- calculateSorensenDice(genes[1], genes[2])

Check for subset inclusion

Description

Remove subsets from a given list of sets, i.e. remove sets which are completely contained in any other larger set in the list.

Usage

checkInclusion(seeds)
checkInclusion(seeds)

Arguments

seeds

A list of sets

Value

A list of unique sets

Examples

## Mock example showing how the data should look like

seeds <- list(c(1:5), c(2:5), c(6:10))
s <- checkInclusion(seeds)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

seeds <- seedFinding(scores_macrophage_topGO_example_small,
                     simThreshold = 0.3,
                     memThreshold = 0.5)
seeds <- checkInclusion(seeds)
## Mock example showing how the data should look like

seeds <- list(c(1:5), c(2:5), c(6:10))
s <- checkInclusion(seeds)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

seeds <- seedFinding(scores_macrophage_topGO_example_small,
                     simThreshold = 0.3,
                     memThreshold = 0.5)
seeds <- checkInclusion(seeds)

Cluster genesets.

Description

This function performs clustering on a set of scores using either the Louvain or Markov method.

Usage

clustering(scores, threshold, cluster_method = "louvain")
clustering(scores, threshold, cluster_method = "louvain")

Arguments

`scores`	A `Matrix::Matrix()` of (distance) scores
`threshold`	numerical, A threshold used to determine which genesets are considered similar. Genesets are considered similar if (distance) score <= threshold. similar.
`cluster_method`	character, the clustering method to use. The options are `louvain` and `markov`. Defaults to `louvain`.

Value

A list of clusters

Examples

## Mock example showing how the data should look like
m <- Matrix::Matrix(stats::runif(100, min = 0, max = 1), 10, 10)
rownames(m) <- colnames(m) <- c("a", "b", "c", "d", "e",
                                "f", "g", "h", "i", "j")
cluster <- clustering(m, 0.3, "markov")

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

clustering <- clustering(scores_macrophage_topGO_example_small,
                        threshold = 0.5)
## Mock example showing how the data should look like
m <- Matrix::Matrix(stats::runif(100, min = 0, max = 1), 10, 10)
rownames(m) <- colnames(m) <- c("a", "b", "c", "d", "e",
                                "f", "g", "h", "i", "j")
cluster <- clustering(m, 0.3, "markov")

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

clustering <- clustering(scores_macrophage_topGO_example_small,
                        threshold = 0.5)

Plot a dendrogram

Description

Plot a dendrogram of a matrix of (distance) scores.

Usage

distanceDendro(distance_scores, cluster_method = "average")
distanceDendro(distance_scores, cluster_method = "average")

Arguments

`distance_scores`	A `Matrix::Matrix()` containing (distance) scores between 0 and 1.
`cluster_method`	character, indicating the clustering method for the `stats::hclust()` function. See the `stats::hclust()` function for the available options. Defaults to 'average'.

Value

A ggdendro::ggdendrogram() plot object.

Examples


## Mock example showing how the data should look like

distance_scores <- Matrix::Matrix(0.5, 20, 20)
distance_scores[c(11:15), c(2:6)] <- 0.2
dendro <- distanceDendro(distance_scores, cluster_method = "single")

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
dendro <- distanceDendro(scores_macrophage_topGO_example_small,
                        cluster_method = "average")
## Mock example showing how the data should look like

distance_scores <- Matrix::Matrix(0.5, 20, 20)
distance_scores[c(11:15), c(2:6)] <- 0.2
dendro <- distanceDendro(distance_scores, cluster_method = "single")

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
dendro <- distanceDendro(scores_macrophage_topGO_example_small,
                        cluster_method = "average")

Plot a heatmap

Description

Plot a heatmap of a matrix of (distance) scores of the input genesets

Usage

distanceHeatmap(distance_scores, chars_limit = 50)
distanceHeatmap(distance_scores, chars_limit = 50)

Arguments

`distance_scores`	A `Matrix::Matrix()` of (distance) scores for each pairwise combination of genesets.
`chars_limit`	Numeric value, Indicates how many characters of the row and column names of `distance_scores` should be plotted. Defaults to 50 and prevents crowded axes due to long names.

Value

A ComplexHeatmap::Heatmap() plot object.

Examples

## Mock example showing how the data should look like

distance_scores <- Matrix::Matrix(0.5, 20, 20)
distance_scores[c(11:15), c(2:6)] <- 0.2
rownames(distance_scores) <- colnames(distance_scores) <- as.character(c(1:20))
p <- distanceHeatmap(distance_scores)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
p <- distanceHeatmap(scores_macrophage_topGO_example_small)
## Mock example showing how the data should look like

distance_scores <- Matrix::Matrix(0.5, 20, 20)
distance_scores[c(11:15), c(2:6)] <- 0.2
rownames(distance_scores) <- colnames(distance_scores) <- as.character(c(1:20))
p <- distanceHeatmap(distance_scores)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
p <- distanceHeatmap(scores_macrophage_topGO_example_small)

Visualize the results of an enrichment analysis as word cloud

Description

Visualize the results of an enrichment analysis as a word cloud. The word cloud highlights the most frequent terms associated with the description of the genesets in the enrichment analysis.

Usage

enrichmentWordcloud(genesets_df)
enrichmentWordcloud(genesets_df)

Arguments

genesets_df

A data.frame object of an enrichment analysis results. This object should follow the input requirements of GeDi(), check out the vignette for further details. Besides the specified required columns, the object should ideally include a column with a short geneset description which is used for the word cloud. If no such column is available, the row names of the data.frame are used for the word cloud.

Value

A wordcloud2::wordcloud2() plot object

Examples

## Mock example showing how the data should look like

## If no "Term" or "Description" column is available,
## the rownames of the data frame will be used.
geneset_df <- data.frame(
              Genesets = c("GO:0002503", "GO:0045087", "GO:0019886"),
              Genes = c("B2M, HLA-DMA, HLA-DMB",
                        "ACOD1, ADAM8, AIM2",
                        "B2M, CD74, CTSS")
)
rownames(geneset_df) <- geneset_df$Genesets

wordcloud <- enrichmentWordcloud(geneset_df)

## With available "Term" column.
geneset_df <- data.frame(
              Genesets = c("GO:0002503", "GO:0045087", "GO:0019886"),
              Genes = c("B2M, HLA-DMA, HLA-DMB",
                        "ACOD1, ADAM8, AIM2",
                        "B2M, CD74, CTSS"),
              Term = c(
              "peptide antigen assembly with MHC class II protein complex",
              "innate immune response",
              "antigen processing and presentation of exogenous
               peptide antigen via MHC class II")
)

wordcloud <- enrichmentWordcloud(geneset_df)


## Example using the data available in the package

data(macrophage_topGO_example,
     package = "GeDi",
     envir = environment())
wordcloud <- enrichmentWordcloud(macrophage_topGO_example)
## Mock example showing how the data should look like

## If no "Term" or "Description" column is available,
## the rownames of the data frame will be used.
geneset_df <- data.frame(
              Genesets = c("GO:0002503", "GO:0045087", "GO:0019886"),
              Genes = c("B2M, HLA-DMA, HLA-DMB",
                        "ACOD1, ADAM8, AIM2",
                        "B2M, CD74, CTSS")
)
rownames(geneset_df) <- geneset_df$Genesets

wordcloud <- enrichmentWordcloud(geneset_df)

## With available "Term" column.
geneset_df <- data.frame(
              Genesets = c("GO:0002503", "GO:0045087", "GO:0019886"),
              Genes = c("B2M, HLA-DMA, HLA-DMB",
                        "ACOD1, ADAM8, AIM2",
                        "B2M, CD74, CTSS"),
              Term = c(
              "peptide antigen assembly with MHC class II protein complex",
              "innate immune response",
              "antigen processing and presentation of exogenous
               peptide antigen via MHC class II")
)

wordcloud <- enrichmentWordcloud(geneset_df)


## Example using the data available in the package

data(macrophage_topGO_example,
     package = "GeDi",
     envir = environment())
wordcloud <- enrichmentWordcloud(macrophage_topGO_example)

Find cluster from initial seeds

Description

Merge the initially determined seeds to clusters.

Usage

fuzzyClustering(seeds, threshold)
fuzzyClustering(seeds, threshold)

Arguments

`seeds`	A `list` of seeds, e.g. determined by `GeDi::seedFinding()` function
`threshold`	numerical, A threshold for merging seeds

Value

A list of clusters

References

See https://david.ncifcrf.gov/helps/functional_classification.html#clustering for details on the original implementation

Examples

## Mock example showing how the data should look like

seeds <- list(c(1:5), c(6:10))
cluster <- fuzzyClustering(seeds, 0.5)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

seeds <- seedFinding(scores_macrophage_topGO_example_small,
                     simThreshold = 0.3,
                     memThreshold = 0.5)
cluster <- fuzzyClustering(seeds, threshold = 0.5)
## Mock example showing how the data should look like

seeds <- list(c(1:5), c(6:10))
cluster <- fuzzyClustering(seeds, 0.5)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

seeds <- seedFinding(scores_macrophage_topGO_example_small,
                     simThreshold = 0.3,
                     memThreshold = 0.5)
cluster <- fuzzyClustering(seeds, threshold = 0.5)

GeDi main function

Description

GeDi main function

Usage

GeDi(
  genesets = NULL,
  ppi_df = NULL,
  distance_scores = NULL,
  col_name_genesets = "Genesets",
  col_name_genes = "Genes"
)
GeDi(
  genesets = NULL,
  ppi_df = NULL,
  distance_scores = NULL,
  col_name_genesets = "Genesets",
  col_name_genes = "Genes"
)

Arguments

`genesets`	a `data.frame`, The input data used for GeDi. This should be a `data.frame` of at least two columns.One column should be called "Genesets" and contain some sort of identifiers for the individual genesets. In this application, we use the term "Genesets" to refer to collections of individual genes, which share common biological characteristics or functions. Such genesets can for example be obtained from databases such as the Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, or the Molecular Signatures Database (MSigDB). The identifiers used in these databases can be directly used as geneset identifiers in GeDi. The second column should be called "Genes" and contain a list of genes belonging to the individual genesets in the "Genesets" column. In order to leverage all of the functionality available in GeDi, the column has to contain gene names and no other commonly used identifiers. The column names are case sensitive.
`ppi_df`	a `data.frame`, Protein-protein interaction (PPI) network data frame. The object is expected to have three columns, `Gene1` and `Gene2` which specify the gene names of the interacting proteins in no particular order (symmetric interaction) and a column `combined_score` which is a numerical value of the strength of the interaction.
`distance_scores`	A `Matrix::Matrix()` of (distance) scores
`col_name_genesets`	character, the name of the column in which the geneset ids are listed. Defaults to "Genesets".
`col_name_genes`	character, the name of the column in which the genes are listed. Defaults to "Genes".

Value

A Shiny app object is returned

Examples

if (interactive()) {
  GeDi()
}
# Alternatively, you can also start the application with your data directly
# loaded.

data("macrophage_topGO_example", package = "GeDi")
if (interactive()) {
  GeDi(genesets = macrophage_topGO_example)
}
if (interactive()) {
  GeDi()
}
# Alternatively, you can also start the application with your data directly
# loaded.

data("macrophage_topGO_example", package = "GeDi")
if (interactive()) {
  GeDi(genesets = macrophage_topGO_example)
}

Construct an adjacency matrix

Description

Construct an adjacency matrix from the (distance) scores and a given threshold.

Usage

getAdjacencyMatrix(distanceMatrix, cutOff)
getAdjacencyMatrix(distanceMatrix, cutOff)

Arguments

`distanceMatrix`	A `Matrix::Matrix()` containing (distance) scores between 0 and 1.
`cutOff`	Numeric value, indicating for which pair of entries in the `distanceMatrix` a 1 should be inserted in the adjacency matrix. A 1 is inserted when for each entry in the matrix # that is smaller or equal to the `cutOff` value.

Value

A Matrix::Matrix() of adjacency status

Examples

m <- Matrix::Matrix(stats::runif(1000, 0, 1), 100, 100)
geneset_names <- as.character(stats::runif(100, min = 0, max = 1))
rownames(m) <- colnames(m) <- geneset_names
threshold <- 0.3
adj <- getAdjacencyMatrix(m, threshold)
m <- Matrix::Matrix(stats::runif(1000, 0, 1), 100, 100)
geneset_names <- as.character(stats::runif(100, min = 0, max = 1))
rownames(m) <- colnames(m) <- geneset_names
threshold <- 0.3
adj <- getAdjacencyMatrix(m, threshold)

Get the annotation of a STRINGdb object

Description

Get the annotation of a STRINGdb object, i.e. the aliases of the protein information

Usage

getAnnotation(stringdb)
getAnnotation(stringdb)

Arguments

stringdb

the STRINGdb object

Value

A data.frame mapping STRINGdb ids to gene names

Examples

string_db <- getStringDB(9606)
string_db
anno_df <- getAnnotation(string_db)
string_db <- getStringDB(9606)
string_db
anno_df <- getAnnotation(string_db)

Construct a bipartite graph

Description

Construct a bipartite graph from cluster information, mapping the cluster to its members

Usage

getBipartiteGraph(cluster, gs_names, genes)
getBipartiteGraph(cluster, gs_names, genes)

Arguments

`cluster`	`list`, a `list` of clusters, cluster members are indicated by numeric values.
`gs_names`	vector, a vector of (geneset) identifiers/names to map the numeric member value in `cluster` to.
`genes`	`list`, a `list` of vectors of genenames which belong to the genesets in `gs_names`.

Value

An igraph object to be further manipulated or processed/plotted (e.g. via igraph::plot.igraph() or visNetwork::visIgraph())

Examples

cluster <- list(c(1:5), c(6:9))
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h", "i")
genes <- list(
  c("PDHB", "VARS2"), c("IARS2", "PDHA1"),
  c("AAAS", "ABCE1"), c("ABI1", "AAR2"), c("AATF", "AMFR"),
  c("BMS1", "DAP3"), c("AURKAIP1", "CHCHD1"), c("IARS2"),
  c("AHI1", "ALMS1")
)

g <- getBipartiteGraph(cluster, gs_names, genes)
cluster <- list(c(1:5), c(6:9))
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h", "i")
genes <- list(
  c("PDHB", "VARS2"), c("IARS2", "PDHA1"),
  c("AAAS", "ABCE1"), c("ABI1", "AAR2"), c("AATF", "AMFR"),
  c("BMS1", "DAP3"), c("AURKAIP1", "CHCHD1"), c("IARS2"),
  c("AHI1", "ALMS1")
)

g <- getBipartiteGraph(cluster, gs_names, genes)

Construct an adjacency matrix

Description

Construct an adjacency matrix from a list of cluster.

Usage

getClusterAdjacencyMatrix(cluster, gs_names)
getClusterAdjacencyMatrix(cluster, gs_names)

Arguments

`cluster`	A `list` of clusters, where each cluster member is indicated by a numeric value
`gs_names`	A vector of geneset names

Value

A Matrix::Matrix() of adjacency status

Examples

cluster <- list(c(1:5), c(6:9))
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h", "i")
adj <- getClusterAdjacencyMatrix(cluster, gs_names)
cluster <- list(c(1:5), c(6:9))
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h", "i")
adj <- getClusterAdjacencyMatrix(cluster, gs_names)

Split string of genes

Description

Split a long string of space separated genes into a list of individual genes.

Usage

getGenes(genesets, gene_name = NULL)
getGenes(genesets, gene_name = NULL)

Arguments

`genesets`	a `data.frame`, A `data.frame` with at least two columns. One should be called `Geneset`, containing the names/identifiers of the genesets in the data. The second column should be called `Genes` and contains one string of the genes contained in each geneset.
`gene_name`	a character, Alternative name for the column containing the genes in `genesets`. If not given, the column is expected to be called `Genes`.

Value

A list containing for each geneset in the Geneset column a list of the included genes.

Examples

## Mock example showing how the data should look like
df <- data.frame(
  Geneset = c(
    "Cell Cycle",
    "Biological Process",
    "Mitosis"
  ),
  Genes = c(
    c("PDHB,VARS2,IARS2"),
    c("LARS,LARS2"),
    c("IARS,SUV3")
  )
)
genes <- getGenes(df)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- getGenes(macrophage_topGO_example_small)
## Mock example showing how the data should look like
df <- data.frame(
  Geneset = c(
    "Cell Cycle",
    "Biological Process",
    "Mitosis"
  ),
  Genes = c(
    c("PDHB,VARS2,IARS2"),
    c("LARS,LARS2"),
    c("IARS,SUV3")
  )
)
genes <- getGenes(df)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- getGenes(macrophage_topGO_example_small)

Build up the node title

Description

Build up the title for the graph nodes to display the available information of each geneset.

Usage

getGraphTitle(geneset_df = NULL, node_ids, gs_ids, gs_names = NULL)
getGraphTitle(geneset_df = NULL, node_ids, gs_ids, gs_names = NULL)

Arguments

`geneset_df`	A `data.frame` of genesets with a column `Genesets` containing geneset identifiers and a column `Genes` containing the genes belonging to each geneset
`node_ids`	vector, a vector of ids of the nodes in the graph for which the node title should be build.
`gs_ids`	vector, a vector of geneset identifiers, e.g. the `Genesets` column of `geneset_df`.
`gs_names`	vector, a vector of geneset descriptions/names, e.g. the `Term` / `Description` column of `geneset_df`.

Value

A list of titles for a graph with nodes given by node_ids.

Examples

genes <- list(
  c("PDHB", "VARS2"), c("IARS2", "PDHA1"),
  c("AAAS", "ABCE1"), c("ABI1", "AAR2"), c("AATF", "AMFR"),
  c("BMS1", "DAP3"), c("AURKAIP1", "CHCHD1"), c("IARS2"),
  c("AHI1", "ALMS1")
)
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h", "i")
geneset_df <- data.frame(
  Genesets = gs_names,
  value = rep(1, 9)
)
geneset_df$Genes <- genes
graph <- getGraphTitle(
  geneset_df = geneset_df,
  node_ids = c(1:9),
  gs_ids = c(1:9),
  gs_names = gs_names
)

genes <- list(
  c("PDHB", "VARS2"), c("IARS2", "PDHA1"),
  c("AAAS", "ABCE1"), c("ABI1", "AAR2"), c("AATF", "AMFR"),
  c("BMS1", "DAP3"), c("AURKAIP1", "CHCHD1"), c("IARS2"),
  c("AHI1", "ALMS1")
)
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h", "i")
geneset_df <- data.frame(
  Genesets = gs_names,
  value = rep(1, 9)
)
geneset_df$Genes <- genes
graph <- getGraphTitle(
  geneset_df = geneset_df,
  node_ids = c(1:9),
  gs_ids = c(1:9),
  gs_names = gs_names
)

Get NCBI ID

Description

Get the NCBI ID of a species

Usage

getId(species, version = "12.0", cache = FALSE)
getId(species, version = "12.0", cache = FALSE)

Arguments

`species`	character, the species of your input data
`version`	character, the version of STRING you want to use, defaults to the current version of STRING
`cache`	Logical value, defining whether to use the BiocFileCache for retrieval of the files underlying the STRINGdb object. Defaults to `TRUE`.

Value

A character of the NCBI ID of species

Examples

species <- "Homo sapiens"
id <- getId(species = species)

species <- "Mus musculus"
id <- getId(species = species)
species <- "Homo sapiens"
id <- getId(species = species)

species <- "Mus musculus"
id <- getId(species = species)

Calculate interaction score for two genesets

Description

The function calculates an interaction score between two sets of genes based on a protein-protein interaction network.

Usage

getInteractionScore(a, b, ppi, maxInteract)
getInteractionScore(a, b, ppi, maxInteract)

Arguments

`a`, `b`	character vector, set of gene identifiers.
`ppi`	a `data.frame`, Protein-protein interaction (PPI) network data frame. The object is expected to have three columns, `Gene1` and `Gene2` which specify the gene names of the interacting proteins in no particular order (symmetric interaction) and a column `combined_score` which is a numerical value of the strength of the interaction.
`maxInteract`	numeric, Maximum interaction value in the PPI.

Value

Interaction score between the two gene sets.

References

See https://doi.org/10.1186/s12864-019-5738-6 for details on the original implementation.

Examples

## Mock example showing how the data should look like
a <- c("PDHB", "VARS2", "IARS2")
b <- c("IARS2", "PDHA1")

ppi <- data.frame(
  Gene1 = c("PDHB", "VARS2", "IARS2"),
  Gene2 = c("IARS2", "PDHA1", "CD3"),
  combined_score = c(0.5, 0.2, 0.1)
)
maxInteract <- max(ppi$combined_score)

interaction <- getInteractionScore(a, b, ppi, maxInteract)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
data(ppi_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
maxInteract <- max(ppi_macrophage_topGO_example_small$combined_score)

interaction <- getInteractionScore(genes[1], genes[2], ppi, maxInteract)
## Mock example showing how the data should look like
a <- c("PDHB", "VARS2", "IARS2")
b <- c("IARS2", "PDHA1")

ppi <- data.frame(
  Gene1 = c("PDHB", "VARS2", "IARS2"),
  Gene2 = c("IARS2", "PDHA1", "CD3"),
  combined_score = c(0.5, 0.2, 0.1)
)
maxInteract <- max(ppi$combined_score)

interaction <- getInteractionScore(a, b, ppi, maxInteract)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
data(ppi_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
maxInteract <- max(ppi_macrophage_topGO_example_small$combined_score)

interaction <- getInteractionScore(genes[1], genes[2], ppi, maxInteract)

Get Matrix of Jaccard distances

Description

Calculate the Jaccard distance of all combinations of genesets in a given data set of genesets.

Usage

getJaccardMatrix(
  genesets,
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)
getJaccardMatrix(
  genesets,
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

`genesets`	a `list`, A `list` of genesets where each genesets is represented by `list` of genes.
`progress`	a `shiny::Progress()` object, Optional progress bar object to track the progress of the function (e.g. in a Shiny app).
`BPPARAM`	A BiocParallel `bpparam` object specifying how parallelization should be handled. Defaults to `BiocParallel::SerialParam()`

Value

A Matrix::Matrix() with Jaccard distance rounded to 2 decimal places.

Examples

## Mock example showing how the data should look like
genesets <- list(list("PDHB", "VARS2"), list("IARS2", "PDHA1"))
m <- getJaccardMatrix(genesets)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
jaccard <-getJaccardMatrix(genes)
## Mock example showing how the data should look like
genesets <- list(list("PDHB", "VARS2"), list("IARS2", "PDHA1"))
m <- getJaccardMatrix(genesets)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
jaccard <-getJaccardMatrix(genes)

Get Matrix of Kappa distances

Description

Calculate the Kappa distance of all combinations of genesets in a given data set of genesets. The Kappa distance is normalized to the (0, 1) interval.

Usage

getKappaMatrix(
  genesets,
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)
getKappaMatrix(
  genesets,
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

`genesets`	a `list`, A `list` of genesets where each genesets is represented by `list` of genes.
`progress`	a `shiny::Progress()` object, Optional progress bar object to track the progress of the function (e.g. in a Shiny app).
`BPPARAM`	A BiocParallel `bpparam` object specifying how parallelization should be handled. Defaults to `BiocParallel::SerialParam()`

Value

A Matrix::Matrix() with Kappa distance rounded to 2 decimal places.

Examples

#' ## Mock example showing how the data should look like
genesets <- list(list("PDHB", "VARS2"), list("IARS2", "PDHA1"))
m <- getKappaMatrix(genesets)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
kappa <-getKappaMatrix(genes)
#' ## Mock example showing how the data should look like
genesets <- list(list("PDHB", "VARS2"), list("IARS2", "PDHA1"))
m <- getKappaMatrix(genesets)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
kappa <-getKappaMatrix(genes)

Get Matrix of Meet-Min distances

Description

Calculate the Meet-Min distance of all combinations of genesets in a given data set of genesets.

Usage

getMeetMinMatrix(
  genesets,
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)
getMeetMinMatrix(
  genesets,
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

`genesets`	a `list`, A `list` of genesets where each genesets is represented by `list` of genes.
`progress`	a `shiny::Progress()` object, Optional progress bar object to track the progress of the function (e.g. in a Shiny app).
`BPPARAM`	A BiocParallel `bpparam` object specifying how parallelization should be handled. Defaults to `BiocParallel::SerialParam()`

Value

A Matrix::Matrix() with Meet-Min distance rounded to 2 decimal places.

Examples

## Mock example showing how the data should look like
genesets <- list(list("PDHB", "VARS2"), list("IARS2", "PDHA1"))
m <- getMeetMinMatrix(genesets)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
mm <- getMeetMinMatrix(genes)
## Mock example showing how the data should look like
genesets <- list(list("PDHB", "VARS2"), list("IARS2", "PDHA1"))
m <- getMeetMinMatrix(genesets)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
mm <- getMeetMinMatrix(genes)

Calculate the pMM distance

Description

Calculate the pMM distance of all combinations of genesets in a given data set of genesets.

Usage

getpMMMatrix(
  genesets,
  ppi,
  alpha = 1,
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)
getpMMMatrix(
  genesets,
  ppi,
  alpha = 1,
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

`genesets`	a `list`, A `list` of genesets where each genesets is represented by `list` of genes.
`ppi`	a `data.frame`, Protein-protein interaction (PPI) network data frame. The object is expected to have three columns, `Gene1` and `Gene2` which specify the gene names of the interacting proteins in no particular order (symmetric interaction) and a column `combined_score` which is a numerical value of the strength of the interaction.
`alpha`	numeric, Scaling factor for controlling the influence of the interaction score. Defaults to 1.
`progress`	a `shiny::Progress()` object, Optional progress bar object to track the progress of the function (e.g. in a Shiny app).
`BPPARAM`	A BiocParallel `bpparam` object specifying how parallelization should be handled. Defaults to `BiocParallel::SerialParam()`

Value

A Matrix::Matrix() with pMM distance rounded to 2 decimal places.

References

See https://doi.org/10.1186/s12864-019-5738-6 for details on the original implementation.

Examples

## Mock example showing how the data should look like
genesets <- list(c("PDHB", "VARS2"), c("IARS2", "PDHA1"))

ppi <- data.frame(
  Gene1 = c("PDHB", "VARS2"),
  Gene2 = c("IARS2", "PDHA1"),
  combined_score = c(0.5, 0.2)
)

pMM <- getpMMMatrix(genesets, ppi)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
data(ppi_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

pMM <- getpMMMatrix(genes, ppi)
## Mock example showing how the data should look like
genesets <- list(c("PDHB", "VARS2"), c("IARS2", "PDHA1"))

ppi <- data.frame(
  Gene1 = c("PDHB", "VARS2"),
  Gene2 = c("IARS2", "PDHA1"),
  combined_score = c(0.5, 0.2)
)

pMM <- getpMMMatrix(genesets, ppi)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
data(ppi_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

pMM <- getpMMMatrix(genes, ppi)

Download Protein-Protein Interaction (PPI)

Description

Download the Protein-Protein Interaction (PPI) information of a STRINGdb object

Usage

getPPI(genes, string_db, anno_df)
getPPI(genes, string_db, anno_df)

Arguments

`genes`	a `list`, A `list` of genes to download the respective protein- protein interaction information
`string_db`	A STRINGdb object, the species of the object should match the species of `genes`.
`anno_df`	An annotation `data.frame` mapping STRINGdb ids to gene names, e.g. downloaded with `GeDi::getAnnotation()`

Value

A data.frame of Protein-Protein interactions

Examples

## Mock example showing how the data should look like

genes <- c(c("CFTR", "RALA"), c("CACNG3", "ITGA3"), c("DVL2"))
string_db <- getStringDB(9606, cache_location = FALSE)
# string_db
anno_df <- getAnnotation(string_db)
ppi <- getPPI(genes, string_db, anno_df)

## Example using the data available in the package
## Not run: 
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
string_db <- getStringDB(9606)
string_db
anno_df <- getAnnotation(string_db)
genes <- GeDi::getGenes(macrophage_topGO_example_small)
ppi <- getPPI(genes, string_db, anno_df)

## End(Not run)
## Mock example showing how the data should look like

genes <- c(c("CFTR", "RALA"), c("CACNG3", "ITGA3"), c("DVL2"))
string_db <- getStringDB(9606, cache_location = FALSE)
# string_db
anno_df <- getAnnotation(string_db)
ppi <- getPPI(genes, string_db, anno_df)

## Example using the data available in the package
## Not run: 
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
string_db <- getStringDB(9606)
string_db
anno_df <- getAnnotation(string_db)
genes <- GeDi::getGenes(macrophage_topGO_example_small)
ppi <- getPPI(genes, string_db, anno_df)

## End(Not run)

Get Matrix of Sorensen-Dice distances

Description

Calculate the Sorensen-Dice distance of all combinations of genesets in a given data set of genesets.

Usage

getSorensenDiceMatrix(
  genesets,
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)
getSorensenDiceMatrix(
  genesets,
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

`genesets`	a `list`, A `list` of genesets where each genesets is represented by `list` of genes.
`progress`	a `shiny::Progress()` object, Optional progress bar object to track the progress of the function (e.g. in a Shiny app).
`BPPARAM`	A BiocParallel `bpparam` object specifying how parallelization should be handled. Defaults to `BiocParallel::SerialParam()`

Value

A Matrix::Matrix() with Sorensen-Dice distance rounded to 2 decimal places.

Examples

## Mock example showing how the data should look like
genesets <- list(list("PDHB", "VARS2"), list("IARS2", "PDHA1"))
m <- getSorensenDiceMatrix(genesets)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
sd_matrix <- getSorensenDiceMatrix(genes)
## Mock example showing how the data should look like
genesets <- list(list("PDHB", "VARS2"), list("IARS2", "PDHA1"))
m <- getSorensenDiceMatrix(genesets)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
sd_matrix <- getSorensenDiceMatrix(genes)

Get the STRING db entry of a species

Description

Get the respective STRINGdb object of your species of interest

Usage

getStringDB(
  species,
  version = "12.0",
  score_threshold = 0,
  cache_location = FALSE
)
getStringDB(
  species,
  version = "12.0",
  score_threshold = 0,
  cache_location = FALSE
)

Arguments

`species`	numeric, the NCBI ID of the species of interest
`version`	character, The STRINGdb version to use, defaults to the current version
`score_threshold`	numeric, A score threshold to cut the retrieved interactions, defaults to 0 (all interactions)
`cache_location`	Logical value, defining whether to use the BiocFileCache for retrieval of the files underlying the STRINGdb object. Defaults to `TRUE`.

Value

a STRINGdb object of species

Examples

species <- getId(species = "Homo sapiens")
string_db <- getStringDB(as.numeric(species))
species <- getId(species = "Homo sapiens")
string_db <- getStringDB(as.numeric(species))

Calculate similarity of GO terms

Description

Calculate the pairwise similarity of GO terms

Usage

goSimilarity(
  geneset_ids,
  method = "Wang",
  ontology = "BP",
  species = "org.Hs.eg.db",
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)
goSimilarity(
  geneset_ids,
  method = "Wang",
  ontology = "BP",
  species = "org.Hs.eg.db",
  progress = NULL,
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

`geneset_ids`	`list`, a `list` of GO identifiers to score
`method`	character, the method to calculate the GO similarity. See GOSemSim::goSim measure parameter for possibilities.
`ontology`	character, the ontology to use. See GOSemSim::goSim `ont` parameter for possibilities.
`species`	character, the species of your data. Indicated as org.XX.eg.db package from Bioconductor.
`progress`	`shiny::Progress()` object, optional. To track the progress of the function (e.g. in a Shiny app)
`BPPARAM`	A BiocParallel `bpparam` object specifying how parallelization should be handled. Defaults to `BiocParallel::SerialParam()`

Value

A Matrix::Matrix() with the pairwise GO similarity of each geneset pair.

Examples



## Mock example showing how the data should look like
go_ids <- c("GO:0002503", "GO:0045087", "GO:0019886",
            "GO:0002250", "GO:0001916", "GO:0019885")

similarity <- goSimilarity(go_ids)

## Example using the data available in the package
data(macrophage_topGO_example_small, package = "GeDi")
go_ids <- macrophage_topGO_example_small$Genesets
## Not run: 
similarity <- goSimilarity(go_ids)

## End(Not run)
## Mock example showing how the data should look like
go_ids <- c("GO:0002503", "GO:0045087", "GO:0019886",
            "GO:0002250", "GO:0001916", "GO:0019885")

similarity <- goSimilarity(go_ids)

## Example using the data available in the package
data(macrophage_topGO_example_small, package = "GeDi")
go_ids <- macrophage_topGO_example_small$Genesets
## Not run: 
similarity <- goSimilarity(go_ids)

## End(Not run)

Create a histogram plot for gene set sizes

Description

Create a histogram plot to plot geneset names / identifiers against their size.

Usage

gsHistogram(
  genesets,
  gs_names,
  gs_description = NULL,
  start = 0,
  end = 0,
  binwidth = 5,
  color = "#0092AC"
)
gsHistogram(
  genesets,
  gs_names,
  gs_description = NULL,
  start = 0,
  end = 0,
  binwidth = 5,
  color = "#0092AC"
)

Arguments

`genesets`	a `list`, A `list` of genesets where each genesets is represented by `list` of genes.
`gs_names`	character vector, Name / identifier of the genesets in `genesets`
`gs_description`	Optional, a character vector containing a short description for each geneset
`start`	numeric, Optional, describes the minimum gene set size to include. Defaults to 0.
`end`	numeric, Optional, describes the maximum gene set size to include. Defaults to 0.
`binwidth`	numeric, Width of histogram bins. Defaults to 5.
`color`	character, Fill color for histogram bars. Defaults to #0092AC.

Value

A ggplot2::ggplot() plot object.

Examples

## Mock example showing how the data should look like
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h")
genesets <- list(
  c("PDHB", "VARS2", "IARS2", "PDHA1"),
  c("AAAS", "ABCE1"), c("ABI1", "AAR2", "AATF"), c("AMFR"),
  c("BMS1", "DAP3"), c("AURKAIP1", "CHCHD1"), c("IARS2"),
  c("AHI1", "ALMS1")
)

p <- gsHistogram(genesets, gs_names, binwidth = 1)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
p <- gsHistogram(genes, macrophage_topGO_example_small$Genesets)
## Mock example showing how the data should look like
gs_names <- c("a", "b", "c", "d", "e", "f", "g", "h")
genesets <- list(
  c("PDHB", "VARS2", "IARS2", "PDHA1"),
  c("AAAS", "ABCE1"), c("ABI1", "AAR2", "AATF"), c("AMFR"),
  c("BMS1", "DAP3"), c("AURKAIP1", "CHCHD1"), c("IARS2"),
  c("AHI1", "ALMS1")
)

p <- gsHistogram(genesets, gs_names, binwidth = 1)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
p <- gsHistogram(genes, macrophage_topGO_example_small$Genesets)

Calculate clusters based on kNN clustering

Description

This function performs k-Nearest Neighbors (kNN) clustering on a set of scores.

Usage

kNN_clustering(scores, k)
kNN_clustering(scores, k)

Arguments

`scores`	A `Matrix::Matrix()` of (distance) scores
`k`	numerical, the number of neighbors

Value

A list of clusters

Examples

## Mock example showing how the data should look like
scores <- Matrix::Matrix(stats::runif(100, min = 0, max = 1), 10, 10)
rownames(scores) <- colnames(scores) <- c("a", "b", "c", "d", "e",
                                "f", "g", "h", "i", "j")
cluster <- kNN_clustering(scores, k = 3)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

kNN <- kNN_clustering(scores_macrophage_topGO_example_small,
                        k = 5)
## Mock example showing how the data should look like
scores <- Matrix::Matrix(stats::runif(100, min = 0, max = 1), 10, 10)
rownames(scores) <- colnames(scores) <- c("a", "b", "c", "d", "e",
                                "f", "g", "h", "i", "j")
cluster <- kNN_clustering(scores, k = 3)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

kNN <- kNN_clustering(scores_macrophage_topGO_example_small,
                        k = 5)

A sample input RData file

Description

A sample input RData file generated from the macrophage dataset.

Format

A data.frame object

Details

This sample input contains data from the macrophage package found on Bioconductor. The exact steps used to generated this file can be found in the package vignette. The used database for the enrichment was the KEGG database.

References

Alasoo, K., Rodrigues, J., Mukhopadhyay, S. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat Genet 50, 424–431 (2018). https://doi.org/10.1038/s41588-018-0046-7

A sample input RData file

Description

A sample input RData file generated from the macrophage dataset.

Format

A data.frame object

Details

References

A sample input RData file

Description

A sample input RData file generated from the macrophage dataset.

Format

A data.frame object

Details

This sample input contains data from the macrophage package found on Bioconductor. The exact steps used to generated this file can be found in the package vignette.

References

A small sample input RData file

Description

A small sample input RData file generated from the macrophage dataset.

Format

A data.frame object

Details

This sample input contains data from the macrophage package found on Bioconductor. It is a small version of the macrophage_topGO_example and only contains the first 50 rows of this example. It can be used for fast testing of the application.

References

Calculate local pMM distance

Description

Calculate the local pMM distance of two genesets.

Usage

pMMlocal(a, b, ppi, maxInteract, alpha = 1)
pMMlocal(a, b, ppi, maxInteract, alpha = 1)

Arguments

`a`, `b`	character vector, set of gene identifiers.
`ppi`	a `data.frame`, Protein-protein interaction (PPI) network data frame. The object is expected to have three columns, `Gene1` and `Gene2` which specify the gene names of the interacting proteins in no particular order (symmetric interaction) and a column `combined_score` which is a numerical value of the strength of the interaction.
`maxInteract`	numeric, Maximum interaction value in the PPI.
`alpha`	numeric, Scaling factor for controlling the influence of the interaction score. Defaults to 1.

Value

The pMMlocal score between the two gene sets.

References

See https://doi.org/10.1186/s12864-019-5738-6 for details on the original implementation.

Examples

## Mock example showing how the data should look like
a <- c("PDHB", "VARS2")
b <- c("IARS2", "PDHA1")

ppi <- data.frame(
  Gene1 = c("PDHB", "VARS2"),
  Gene2 = c("IARS2", "PDHA1"),
  combined_score = c(0.5, 0.2)
)
maxInteract <- max(ppi$combined_score)

pMM_score <- pMMlocal(a, b, ppi, maxInteract)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
data(ppi_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
maxInteract <- max(ppi_macrophage_topGO_example_small$combined_score)

pMMlocal <- pMMlocal(genes[1], genes[2], ppi, maxInteract)
## Mock example showing how the data should look like
a <- c("PDHB", "VARS2")
b <- c("IARS2", "PDHA1")

ppi <- data.frame(
  Gene1 = c("PDHB", "VARS2"),
  Gene2 = c("IARS2", "PDHA1"),
  combined_score = c(0.5, 0.2)
)
maxInteract <- max(ppi$combined_score)

pMM_score <- pMMlocal(a, b, ppi, maxInteract)

## Example using the data available in the package
data(macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
genes <- GeDi::getGenes(macrophage_topGO_example_small)
data(ppi_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())
maxInteract <- max(ppi_macrophage_topGO_example_small$combined_score)

pMMlocal <- pMMlocal(genes[1], genes[2], ppi, maxInteract)

PPI

Description

A file containing a Protein-Protein Interaction (PPI) data.frame for the macrophage_topGO_example_small.

Format

A data.frame object

Details

This sample input contains a PPI for the macrophage_topGO_example_small. The PPI has been downloaded using the functions to download a PPI matrix. Please check out the vignette for further information.

References

A sample input text file

Description

A sample input text file taken from the GScluster package

Format

Text file

Details

This sample input text file contains data from the GScluster package. It is identical to the sample_geneset.txt file found on the Github page of the package.

References

Yoon, S., Kim, J., Kim, SK. et al. GScluster: network-weighted gene-set clustering analysis. BMC Genomics 20, 352 (2019). https://doi.org/10.1186/s12864-019-5738-6

A broken input text file

Description

A broken input text file to test the application

Format

Text file

Details

This sample input text file is broken and used for testing the application.

An empty input text file

Description

An empty input text file to test the application

Format

Text file

Details

This sample input text file is empty and used for testing the application.

A small sample input text file

Description

A sample input text file taken from the GScluster package, which is reduced to a smaller number of entries for faster testing of the application.

Format

Text file

Details

This sample input text file contains data from the GScluster package. It was taken from the sample_geneset.txt file found on the Github page of the package and then reduced to a smaller amount of entries for faster testing of the application.

References

Yoon, S., Kim, J., Kim, SK. et al. GScluster: network-weighted gene-set clustering analysis. BMC Genomics 20, 352 (2019). https://doi.org/10.1186/s12864-019-5738-6

Scaling (distance) scores

Description

A method to scale a matrix of distance scores with the GO term similarity of the associated genesets.

Usage

scaleGO(
  scores,
  geneset_ids,
  method = "Wang",
  ontology = "BP",
  species = "org.Hs.eg.db",
  BPPARAM = BiocParallel::SerialParam()
)
scaleGO(
  scores,
  geneset_ids,
  method = "Wang",
  ontology = "BP",
  species = "org.Hs.eg.db",
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

`scores`	a `Matrix::Matrix()`, a matrix of (distance) scores for the identifiers in `geneset_ids`.
`geneset_ids`	`list`, a `list` of GO identifiers to score
`method`	character, the method to calculate the GO similarity. See GOSemSim::goSim measure parameter for possibilities.
`ontology`	character, the ontology to use. See GOSemSim::goSim ont parameter for possibilities.
`species`	character, the species of your data. Indicated as org.XX.eg.db package from Bioconductor.
`BPPARAM`	A BiocParallelParam object specifying how parallelization should be handled

Value

A Matrix::Matrix() of scaled values.

Examples

## Mock example showing how the data should look like
go_ids <- c("GO:0002503", "GO:0045087", "GO:0019886",
            "GO:0002250", "GO:0001916", "GO:0019885")
set.seed(42)
scores <- Matrix::Matrix(stats::runif(36, min = 0, max = 1), 6, 6)
similarity <- scaleGO(scores,
                      go_ids)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small, package = "GeDi")
data(macrophage_topGO_example_small, package = "GeDi")
go_ids <- macrophage_topGO_example_small$Genesets
## Not run: 
scores_scaled <- scaleGO(scores_macrophage_topGO_example_small,
                         go_ids)

## End(Not run)
## Mock example showing how the data should look like
go_ids <- c("GO:0002503", "GO:0045087", "GO:0019886",
            "GO:0002250", "GO:0001916", "GO:0019885")
set.seed(42)
scores <- Matrix::Matrix(stats::runif(36, min = 0, max = 1), 6, 6)
similarity <- scaleGO(scores,
                      go_ids)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small, package = "GeDi")
data(macrophage_topGO_example_small, package = "GeDi")
go_ids <- macrophage_topGO_example_small$Genesets
## Not run: 
scores_scaled <- scaleGO(scores_macrophage_topGO_example_small,
                         go_ids)

## End(Not run)

Sample scores

Description

A file containing sample distance scores for the macrophage_topGO_example_small.

Format

A sparse matrix (dgCMatrix)

Details

This sample input contains scores for the macrophage_topGO_example_small. Distance scores have been calculated using the getJaccardMatrix() method.

References

Find clustering seeds

Description

Determine initial seeds for the clustering from the distance score matrix.

Usage

seedFinding(distances, simThreshold, memThreshold)
seedFinding(distances, simThreshold, memThreshold)

Arguments

`distances`	A `Matrix::Matrix()` of (distance) scores
`simThreshold`	numerical, A threshold to determine which genesets are considered close (i.e. have a distance <= simThreshold) in the `distances` matrix.
`memThreshold`	numerical, A threshold used to ensure that enough members of a potential seed set are close/similar to each other. Only if this condition is met, the set is considered a seed.

Value

A list of seeds which can be used for clustering

References

See https://david.ncifcrf.gov/helps/functional_classification.html#clustering for details on the original implementation

Examples

## Mock example showing how the data should look like

m <- Matrix::Matrix(stats::runif(100, min = 0, max = 1), 10, 10)
seeds <- seedFinding(distances = m, simThreshold = 0.3, memThreshold = 0.5)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

seeds <- seedFinding(scores_macrophage_topGO_example_small,
                     simThreshold = 0.3,
                     memThreshold = 0.5)
## Mock example showing how the data should look like

m <- Matrix::Matrix(stats::runif(100, min = 0, max = 1), 10, 10)
seeds <- seedFinding(distances = m, simThreshold = 0.3, memThreshold = 0.5)

## Example using the data available in the package
data(scores_macrophage_topGO_example_small,
     package = "GeDi",
     envir = environment())

seeds <- seedFinding(scores_macrophage_topGO_example_small,
                     simThreshold = 0.3,
                     memThreshold = 0.5)

Package 'GeDi'

Help Index

Check genesets format

Description

Usage

Arguments

Value

Check PPI format

Description

Usage

Arguments

Value

Check distance scores format

Description

Usage

Arguments

Value

Filter Genesets from the input data

Description

Usage

Arguments

Value

Make an educated guess on the separator character

Description

Usage

Arguments

Value

References

Map each geneset to the cluster it belongs

Description

Usage

Arguments

Value

Title

Description

Usage

Arguments

Value

Determine the number of cores to use for a function

Description

Usage

Arguments

Value

Generate a data.frame of graph metrics

Description

Usage

Arguments

Value

Make an educated guess on the separator character

Description

Usage

Arguments

Value

References

Build a cluster graph

Description

Usage

Arguments

Value

Examples

Construct a graph

Description

Usage

Arguments

Value

Examples

Prepare data for gsHistogram().

Description

Usage

Arguments

Value

Examples

Calculate the Jaccard distance

Description

Usage

Arguments

Value

Examples

Calculate the Kappa distance

Description

Generate a `data.frame` of graph metrics

Prepare data for `gsHistogram()`.