Package 'BioCor' reference manual

Title:	Functional similarities
Description:	Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships...
Authors:	Lluís Revilla Sancho [aut, cre] , Pau Sancho-Bru [ths] , Juan José Salvatella Lozano [ths]
Maintainer:	Lluís Revilla Sancho <[email protected]>
License:	MIT + file LICENSE
Version:	1.31.0
Built:	2024-10-30 04:51:14 UTC
Source:	https://github.com/bioc/BioCor

BioCor: A package to calculate functional similarities

Description

Calculates a functional similarity measure between gene identifiers based on the pathways described on KEGG and REACTOME.

Important functions

pathSim(): Calculates the similarity between two pathways.
geneSim(): Calculates the similarity (based on pathSim) between two genes.
clusterSim(): Calculates the similarity between two clusters of genes by joining pathways of each gene.
clusterGeneSim(): Calculates the similarity between two clusters of genes by comparing the similarity between the genes of a cluster.
similarities(): Allows to combine the value of matrices of similarities.
conversions(): Two functions to convert similarity measures.
weighted(): Functions provided to combine similarities.

Author(s)

Maintainer: Lluís Revilla Sancho [email protected] (ORCID)

Other contributors:

Pau Sancho-Bru (ORCID) [thesis advisor]
Juan José Salvatella Lozano (ORCID) [thesis advisor]

Additive integration of similarities

Description

Function that use the previously calculated similarities into a single similarity matrix.

Usage

addSimilarities(x, bio_mat, weights = c(0.5, 0.18, 0.1, 0.22))
addSimilarities(x, bio_mat, weights = c(0.5, 0.18, 0.1, 0.22))

Arguments

`x`	A matrix with the similarity of expression
`bio_mat`	A list of matrices of the same dimension as x.
`weights`	A numeric vector of weight to multiply each similarity

Details

The total weight can't be higher than 1 to prevent values above 1 but can be below 1. It uses weighted.sum with abs = TRUE internally.

Value

A square matrix of the same dimensions as the input matrices.

Author(s)

Lluís Revilla

Examples

set.seed(100)
a <- seq2mat(LETTERS[1:5], rnorm(10))
b <- seq2mat(LETTERS[1:5], seq(from = 0.1, to = 1, by = 0.1))
sim <- list(b)
addSimilarities(a, sim, c(0.5, 0.5))
set.seed(100)
a <- seq2mat(LETTERS[1:5], rnorm(10))
b <- seq2mat(LETTERS[1:5], seq(from = 0.1, to = 1, by = 0.1))
sim <- list(b)
addSimilarities(a, sim, c(0.5, 0.5))

Insert a matrix into another

Description

Insert values from a matrix into another matrix based on the rownames and colnames replacing the values.

Usage

AintoB(A, B)
AintoB(A, B)

Arguments

`A`	A matrix to be inserted.
`B`	A matrix to insert in.

Details

If all the genes with pathway information are already calculated but you would like to use more genes when performing analysis. insert the once you have calculated on the matrix of genes.

Value

A matrix with the values of A in the matrix B.

Author(s)

Lluís Revilla

Examples

B <- matrix(
    ncol = 10, nrow = 10,
    dimnames = list(letters[1:10], letters[1:10])
)
A <- matrix(c(1:15),
    byrow = TRUE, nrow = 5,
    dimnames = list(letters[1:5], letters[1:3])
)
AintoB(A, B)

# Mixed orders
colnames(A) <- c("c", "h", "e")
rownames(A) <- c("b", "a", "f", "c", "j")
AintoB(A, B)

# Missing colums or rows
colnames(A) <- c("d", "f", "k")
AintoB(A, B)
B <- matrix(
    ncol = 10, nrow = 10,
    dimnames = list(letters[1:10], letters[1:10])
)
A <- matrix(c(1:15),
    byrow = TRUE, nrow = 5,
    dimnames = list(letters[1:5], letters[1:3])
)
AintoB(A, B)

# Mixed orders
colnames(A) <- c("c", "h", "e")
rownames(A) <- c("b", "a", "f", "c", "j")
AintoB(A, B)

# Missing colums or rows
colnames(A) <- c("d", "f", "k")
AintoB(A, B)

Similarity score between clusters of genes based on genes similarity

Description

Looks for the similarity between genes of a group and then between each group's genes.

Usage

clusterGeneSim(cluster1, cluster2, info, method = c("max", "rcmax.avg"), ...)

## S4 method for signature 'character,character,GeneSetCollection'
clusterGeneSim(cluster1, cluster2, info, method = c("max", "rcmax.avg"), ...)
clusterGeneSim(cluster1, cluster2, info, method = c("max", "rcmax.avg"), ...)

## S4 method for signature 'character,character,GeneSetCollection'
clusterGeneSim(cluster1, cluster2, info, method = c("max", "rcmax.avg"), ...)

Arguments

`cluster1`, `cluster2`	A vector with genes.
`info`	A GeneSetCollection or a list of genes and the pathways they are involved.
`method`	A vector with two or one argument to be passed to combineScores the first one is used to summarize the similarities of genes, the second one for clusters.
`...`	Other arguments passed to `combineScores`

Details

Differs with clusterSim that first each combination between genes is calculated, and with this values then the comparison between the two clusters is done. Thus applying combineScores twice, one at gene level and another one at cluster level.

Value

Returns a similarity score between the genes of the two clusters.

Methods (by class)

clusterGeneSim( cluster1 = character, cluster2 = character, info = GeneSetCollection ): Calculates the gene similarities in a GeneSetCollection and combine them using combineScoresPar()

Author(s)

Lluís Revilla

Examples

if (require("org.Hs.eg.db")) {
    # Extract the paths of all genes of org.Hs.eg.db from KEGG (last update in
    # data of June 31st 2011)
    genes.kegg <- as.list(org.Hs.egPATH)
    clusterGeneSim(c("18", "81", "10"), c("100", "10", "1"), genes.kegg)
    clusterGeneSim(
        c("18", "81", "10"), c("100", "10", "1"), genes.kegg,
        c("avg", "avg")
    )
    clusterGeneSim(
        c("18", "81", "10"), c("100", "10", "1"), genes.kegg,
        c("avg", "rcmax.avg")
    )
    (clus <- clusterGeneSim(
        c("18", "81", "10"), c("100", "10", "1"),
        genes.kegg, "avg"
    ))
    combineScores(clus, "rcmax.avg")
} else {
    warning("You need org.Hs.eg.db package for this example")
}
if (require("org.Hs.eg.db")) {
    # Extract the paths of all genes of org.Hs.eg.db from KEGG (last update in
    # data of June 31st 2011)
    genes.kegg <- as.list(org.Hs.egPATH)
    clusterGeneSim(c("18", "81", "10"), c("100", "10", "1"), genes.kegg)
    clusterGeneSim(
        c("18", "81", "10"), c("100", "10", "1"), genes.kegg,
        c("avg", "avg")
    )
    clusterGeneSim(
        c("18", "81", "10"), c("100", "10", "1"), genes.kegg,
        c("avg", "rcmax.avg")
    )
    (clus <- clusterGeneSim(
        c("18", "81", "10"), c("100", "10", "1"),
        genes.kegg, "avg"
    ))
    combineScores(clus, "rcmax.avg")
} else {
    warning("You need org.Hs.eg.db package for this example")
}

Similarity score between clusters of genes based on pathways similarity

Description

Looks for the similarity between genes in groups

Usage

clusterSim(cluster1, cluster2, info, method = "max", ...)

## S4 method for signature 'character,character,GeneSetCollection'
clusterSim(cluster1, cluster2, info, method = "max", ...)
clusterSim(cluster1, cluster2, info, method = "max", ...)

## S4 method for signature 'character,character,GeneSetCollection'
clusterSim(cluster1, cluster2, info, method = "max", ...)

Arguments

`cluster1`, `cluster2`	A vector with genes.
`info`	A GeneSetCollection or a list of genes and the pathways they are involved.
`method`	one of `c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal")`, see Details.
`...`	Other arguments passed to `combineScores`

Details

Once the pathways for each cluster are found they are combined using combineScores().

Value

clusterSim returns a similarity score of the two clusters

Methods (by class)

clusterSim( cluster1 = character, cluster2 = character, info = GeneSetCollection ): Calculates all the similarities of the GeneSetCollection and combine them using combineScoresPar()

Author(s)

Lluís Revilla

Examples

if (require("org.Hs.eg.db")) {
    # Extract the paths of all genes of org.Hs.eg.db from KEGG (last update in
    # data of June 31st 2011)
    genes.kegg <- as.list(org.Hs.egPATH)
    clusterSim(c("9", "15", "10"), c("33", "19", "20"), genes.kegg)
    clusterSim(c("9", "15", "10"), c("33", "19", "20"), genes.kegg, NULL)
    clusterSim(c("9", "15", "10"), c("33", "19", "20"), genes.kegg, "avg")
} else {
    warning("You need org.Hs.eg.db package for this example")
}
if (require("org.Hs.eg.db")) {
    # Extract the paths of all genes of org.Hs.eg.db from KEGG (last update in
    # data of June 31st 2011)
    genes.kegg <- as.list(org.Hs.egPATH)
    clusterSim(c("9", "15", "10"), c("33", "19", "20"), genes.kegg)
    clusterSim(c("9", "15", "10"), c("33", "19", "20"), genes.kegg, NULL)
    clusterSim(c("9", "15", "10"), c("33", "19", "20"), genes.kegg, "avg")
} else {
    warning("You need org.Hs.eg.db package for this example")
}

i-th combination of n elements taken from r

Description

Function similar to combn but for larger vectors. To avoid allocating a big vector with all the combinations each one can be computed with this function.

Usage

combinadic(n, r, i)
combinadic(n, r, i)

Arguments

`n`	Elements to extract the combination from
`r`	Number of elements per combination
`i`	ith combination

Value

The combination ith of the elements

Author(s)

Joshua Ulrich

References

StackOverflow answer 4494469/2886003

Examples

# Output of all combinations
combn(LETTERS[1:5], 2)
# Otuput of the second combination
combinadic(LETTERS[1:5], 2, 2)
# Output of all combinations
combn(LETTERS[1:5], 2)
# Otuput of the second combination
combinadic(LETTERS[1:5], 2, 2)

Combining values

Description

Combine several similarities into one using several methods.

Usage

combineScores(
  scores,
  method = c("max", "avg", "rcmax", "rcmax.avg", "BMA", "reciprocal"),
  round = FALSE,
  t = 0
)

combineScoresPar(scores, method, subSets = NULL, BPPARAM = NULL, ...)
combineScores(
  scores,
  method = c("max", "avg", "rcmax", "rcmax.avg", "BMA", "reciprocal"),
  round = FALSE,
  t = 0
)

combineScoresPar(scores, method, subSets = NULL, BPPARAM = NULL, ...)

Arguments

`scores`	Matrix of scores to be combined
`method`	one of `c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal")`, see Details.
`round`	Should the resulting value be rounded to the third digit?
`t`	Numeric value to filter scores below this value. Only used in the reciprocal method.
`subSets`	List of combinations as info in other functions.
`BPPARAM`	BiocParallel back-end parameters. By default (`NULL`) a `for` loop is used.
`...`	Other arguments passed to `combineScores`

Details

The input matrix can be a base matrix or a matrix from package Matrix. The methods return:

avg: The average or mean value.
max: The max value.
rcmax: The max of the column means or row means.
rcmax.avg: The sum of the max values by rows and columns divided by the number of columns and rows.
BMA: The same as rcmax.avg.
reciprocal: The double of the sum of the reciprocal maximal similarities (above a threshold) divided by the number of elements. See equation 3 of the Tao et al 2007 article.

Value

A numeric value as described in details.

Note

combineScores is a version of the function of the same name in package GOSemSim (GOSemSim::combineScores()) with optional rounding and some internal differences.

Author(s)

Lluís Revilla based on Guangchuang Yu.

References

Ying Tao, Lee Sam, Jianrong Li, Carol Friedman, Yves A. Lussier; Information theory applied to the sparse gene ontology annotation network to predict novel gene function. Bioinformatics 2007; 23 (13): i529-i538. doi: 10.1093/bioinformatics/btm195

Examples

(d <- structure(c(
    0.4, 0.6, 0.222222222222222, 0.4, 0.4, 0, 0.25, 0.5,
    0.285714285714286
),
.Dim = c(3L, 3L),
.Dimnames = list(c("a", "b", "c"), c("d", "e", "f"))
))
e <- d
sapply(c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal"),
    combineScores,
    scores = d
)
d[1, 2] <- NA
sapply(c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal"),
    combineScores,
    scores = d
)
colnames(e) <- rownames(e)
combineScoresPar(e, list(a = c("a", "b"), b = c("b", "c")),
    method = "max"
)
(d <- structure(c(
    0.4, 0.6, 0.222222222222222, 0.4, 0.4, 0, 0.25, 0.5,
    0.285714285714286
),
.Dim = c(3L, 3L),
.Dimnames = list(c("a", "b", "c"), c("d", "e", "f"))
))
e <- d
sapply(c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal"),
    combineScores,
    scores = d
)
d[1, 2] <- NA
sapply(c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal"),
    combineScores,
    scores = d
)
colnames(e) <- rownames(e)
combineScoresPar(e, list(a = c("a", "b"), b = c("b", "c")),
    method = "max"
)

Combine different sources of pathways

Description

Given several sources of pathways with the same for the same id of the genes it merge them.

Usage

combineSources(...)
combineSources(...)

Arguments

...

Lists of genes and their pathways.

Details

It assumes that the identifier of the genes are the same for both sources but if many aren't equal it issues a warning. Only unique pathways identifiers are returned.

Value

A single list with the pathways of each source on the same gene.

Examples

DB1 <- list(g1 = letters[6:8], g2 = letters[1:5], g3 = letters[4:7])
DB2 <- list(
    g1 = c("one", "two"), g2 = c("three", "four"),
    g3 = c("another", "two")
)
combineSources(DB1, DB2)
combineSources(DB1, DB1)
DB3 <- list(
    g1 = c("one", "two"), g2 = c("three", "four"),
    g4 = c("five", "six", "seven"), g5 = c("another", "two")
)
combineSources(DB1, DB3) # A warning is expected
DB1 <- list(g1 = letters[6:8], g2 = letters[1:5], g3 = letters[4:7])
DB2 <- list(
    g1 = c("one", "two"), g2 = c("three", "four"),
    g3 = c("another", "two")
)
combineSources(DB1, DB2)
combineSources(DB1, DB1)
DB3 <- list(
    g1 = c("one", "two"), g2 = c("three", "four"),
    g4 = c("five", "six", "seven"), g5 = c("another", "two")
)
combineSources(DB1, DB3) # A warning is expected

Convert the similarities formats

Description

Functions to convert the similarity coefficients between Jaccard and Dice. D2J is the opposite of J2D.

Usage

D2J(D)

J2D(J)
D2J(D)

J2D(J)

Arguments

`D`	Dice coefficient, as returned by `diceSim()`, `geneSim()`, `clusterSim()` and `clusterGeneSim()`
`J`	Jaccard coefficient

Value

A numeric value.

Author(s)

Lluís Revilla

Examples

D2J(0.5)
J2D(0.5)
D2J(J2D(0.5))
D2J(0.5)
J2D(0.5)
D2J(J2D(0.5))

Compare pathways

Description

Function to estimate how much two list of genes overlap by looking how much of the nodes are shared. Calculates the Dice similarity

Usage

diceSim(g1, g2)
diceSim(g1, g2)

Arguments

g1, g2

A character list with the names of the proteins in each pathway.

Details

It requires a vector of characters otherwise will return an NA.

Value

A score between 0 and 1 calculated as the double of the proteins shared by g1 and g2 divided by the number of genes in both groups.

Author(s)

Lluís Revilla

Examples

genes.id2 <- c("52", "11342", "80895", "57654", "548953", "11586", "45985")
genes.id1 <- c(
    "52", "11342", "80895", "57654", "58493", "1164", "1163",
    "4150", "2130", "159"
)
diceSim(genes.id1, genes.id2)
diceSim(genes.id2, genes.id2)
genes.id2 <- c("52", "11342", "80895", "57654", "548953", "11586", "45985")
genes.id1 <- c(
    "52", "11342", "80895", "57654", "58493", "1164", "1163",
    "4150", "2130", "159"
)
diceSim(genes.id1, genes.id2)
diceSim(genes.id2, genes.id2)

Finds the indices of the duplicated events of a vector

Description

Finds the indices of duplicated elements in the vector given.

Usage

duplicateIndices(vec)
duplicateIndices(vec)

Arguments

vec

Vector of identifiers presumably duplicated

Details

For each duplication it can return a list or if all the duplication events are of the same length it returns a matrix, where each column is duplicated.

Value

The format is determined by the simplify2array

Author(s)

Lluís Revilla

Examples

duplicateIndices(c("52", "52", "53", "55")) # One repeated element
duplicateIndices(c("52", "52", "53", "55", "55")) # Repeated elements
duplicateIndices(c("52", "55", "53", "55", "52")) # Mixed repeated elements
duplicateIndices(c("52", "52", "53", "55")) # One repeated element
duplicateIndices(c("52", "52", "53", "55", "55")) # Repeated elements
duplicateIndices(c("52", "55", "53", "55", "52")) # Mixed repeated elements

Similarity score genes based on pathways similarity

Description

Given two genes, calculates the Dice similarity between each pathway which is combined to obtain a similarity between the genes.

Usage

geneSim(gene1, gene2, info, method = "max", ...)

## S4 method for signature 'character,character,GeneSetCollection'
geneSim(gene1, gene2, info, method = "max", ...)
geneSim(gene1, gene2, info, method = "max", ...)

## S4 method for signature 'character,character,GeneSetCollection'
geneSim(gene1, gene2, info, method = "max", ...)

Arguments

`gene1`, `gene2`	Ids of the genes to calculate the similarity, to be found in genes.
`info`	A GeneSetCollection or a list of genes and the pathways they are involved.
`method`	one of `c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal")`, see Details.
`...`	Other arguments passed to `combineScores`

Details

Given the information about the genes and their pathways, uses the ids of the genes to find the Dice similarity score for each pathway comparison between the genes. Later this similarities are combined using combineScoresPar().

Value

The highest Dice score of all the combinations of pathways between the two ids compared if a method to combine scores is provided or NA if there isn't information for one gene. If an NA is returned this means that there isn't information available for any pathways for one of the genes. Otherwise a number between 0 and 1 (both included) is returned. Note that there isn't a negative value of similarity.

Methods (by class)

geneSim(gene1 = character, gene2 = character, info = GeneSetCollection): Calculates all the similarities of the GeneSetCollection and combine them using combineScoresPar()

Author(s)

Lluís Revilla

Examples

if (require("org.Hs.eg.db") & require("reactome.db")) {
    # Extract the paths of all genes of org.Hs.eg.db from KEGG
    # (last update in data of June 31st 2011)
    genes.kegg <- as.list(org.Hs.egPATH)
    # Extracts the paths of all genes of org.Hs.eg.db from reactome
    genes.react <- as.list(reactomeEXTID2PATHID)
    geneSim("81", "18", genes.react)
    geneSim("81", "18", genes.kegg)
    geneSim("81", "18", genes.react, NULL)
    geneSim("81", "18", genes.kegg, NULL)
} else {
    warning("You need reactome.db and org.Hs.eg.db package for this example")
}
if (require("org.Hs.eg.db") & require("reactome.db")) {
    # Extract the paths of all genes of org.Hs.eg.db from KEGG
    # (last update in data of June 31st 2011)
    genes.kegg <- as.list(org.Hs.egPATH)
    # Extracts the paths of all genes of org.Hs.eg.db from reactome
    genes.react <- as.list(reactomeEXTID2PATHID)
    geneSim("81", "18", genes.react)
    geneSim("81", "18", genes.kegg)
    geneSim("81", "18", genes.react, NULL)
    geneSim("81", "18", genes.kegg, NULL)
} else {
    warning("You need reactome.db and org.Hs.eg.db package for this example")
}

Invert a list

Description

Calculate the pathways per gene of list

Usage

inverseList(x)
inverseList(x)

Arguments

`x`	A list with genes as names and names of pathways as values of the list

Value

The number of pathways each gene has.

Author(s)

Lluís Revilla

Similarity score between clusters of genes based on genes similarity

Description

Looks for the similarity between genes of a group and then between each group's genes.

Usage

mclusterGeneSim(clusters, info, method = c("max", "rcmax.avg"), ...)

## S4 method for signature 'list,GeneSetCollection'
mclusterGeneSim(clusters, info, method = c("max", "rcmax.avg"), ...)
mclusterGeneSim(clusters, info, method = c("max", "rcmax.avg"), ...)

## S4 method for signature 'list,GeneSetCollection'
mclusterGeneSim(clusters, info, method = c("max", "rcmax.avg"), ...)

Arguments

`clusters`	A list of clusters of genes to be found in `id`.
`info`	A GeneSetCollection or a list of genes and the pathways they are involved.
`method`	A vector with two or one argument to be passed to combineScores the first one is used to summarize the similarities of genes, the second one for clusters.
`...`	Other arguments passed to `combineScores`

Value

Returns a matrix with the similarity scores for each cluster comparison.

Methods (by class)

mclusterGeneSim(clusters = list, info = GeneSetCollection): Calculates all the similarities of the GeneSetCollection and combine them using combineScoresPar()

Author(s)

Lluís Revilla

Examples

if (require("org.Hs.eg.db")) {
    genes.kegg <- as.list(org.Hs.egPATH)
    clusters <- list(
        cluster1 = c("18", "81", "10"),
        cluster2 = c("100", "594", "836"),
        cluster3 = c("18", "10", "83")
    )
    mclusterGeneSim(clusters, genes.kegg)
    mclusterGeneSim(clusters, genes.kegg, c("max", "avg"))
    mclusterGeneSim(clusters, genes.kegg, c("max", "BMA"))
} else {
    warning("You need org.Hs.eg.db package for this example")
}
if (require("org.Hs.eg.db")) {
    genes.kegg <- as.list(org.Hs.egPATH)
    clusters <- list(
        cluster1 = c("18", "81", "10"),
        cluster2 = c("100", "594", "836"),
        cluster3 = c("18", "10", "83")
    )
    mclusterGeneSim(clusters, genes.kegg)
    mclusterGeneSim(clusters, genes.kegg, c("max", "avg"))
    mclusterGeneSim(clusters, genes.kegg, c("max", "BMA"))
} else {
    warning("You need org.Hs.eg.db package for this example")
}

Similarity score between clusters of genes based on pathways similarity

Description

Looks for the similarity between genes in groups. Once the pathways for each cluster are found they are combined using codecombineScores.

Usage

mclusterSim(clusters, info, method = "max", ...)

## S4 method for signature 'list,GeneSetCollection'
mclusterSim(clusters, info, method = "max", ...)
mclusterSim(clusters, info, method = "max", ...)

## S4 method for signature 'list,GeneSetCollection'
mclusterSim(clusters, info, method = "max", ...)

Arguments

`clusters`	A list of clusters of genes to be found in `id`.
`info`	A GeneSetCollection or a list of genes and the pathways they are involved.
`method`	one of `c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal")`, see Details.
`...`	Other arguments passed to `combineScores`

Value

mclusterSim returns a matrix with the similarity scores for each cluster comparison.

Methods (by class)

mclusterSim(clusters = list, info = GeneSetCollection): Calculates all the similarities of the GeneSetCollection and combine them using combineScoresPar()

Author(s)

Lluís Revilla

Examples

if (require("org.Hs.eg.db")) {
    # Extract the paths of all genes of org.Hs.eg.db from KEGG (last update in
    # data of June 31st 2011)
    genes.kegg <- as.list(org.Hs.egPATH)

    clusters <- list(
        cluster1 = c("18", "81", "10"),
        cluster2 = c("100", "10", "1"),
        cluster3 = c("18", "10", "83")
    )
    mclusterSim(clusters, genes.kegg)
    mclusterSim(clusters, genes.kegg, "avg")
} else {
    warning("You need org.Hs.eg.db package for this example")
}
if (require("org.Hs.eg.db")) {
    # Extract the paths of all genes of org.Hs.eg.db from KEGG (last update in
    # data of June 31st 2011)
    genes.kegg <- as.list(org.Hs.egPATH)

    clusters <- list(
        cluster1 = c("18", "81", "10"),
        cluster2 = c("100", "10", "1"),
        cluster3 = c("18", "10", "83")
    )
    mclusterSim(clusters, genes.kegg)
    mclusterSim(clusters, genes.kegg, "avg")
} else {
    warning("You need org.Hs.eg.db package for this example")
}

Similarity score genes based on pathways similarity

Description

Given two genes, calculates the Dice similarity between each pathway which is combined to obtain a similarity between the genes.

Usage

mgeneSim(genes, info, method = "max", ...)

## S4 method for signature 'character,GeneSetCollection'
mgeneSim(genes, info, method = "max", ...)

## S4 method for signature 'missing,GeneSetCollection'
mgeneSim(genes, info, method = "max", ...)
mgeneSim(genes, info, method = "max", ...)

## S4 method for signature 'character,GeneSetCollection'
mgeneSim(genes, info, method = "max", ...)

## S4 method for signature 'missing,GeneSetCollection'
mgeneSim(genes, info, method = "max", ...)

Arguments

`genes`	A vector of genes.
`info`	A GeneSetCollection or a list of genes and the pathways they are involved.
`method`	one of `c("avg", "max", "rcmax", "rcmax.avg", "BMA", "reciprocal")`, see Details.
`...`	Other arguments passed to `combineScores`

Details

Value

mgeneSim returns the matrix of similarities between the genes in the vector

Methods (by class)

mgeneSim(genes = character, info = GeneSetCollection): Calculates all the similarities of the list and combine them using combineScoresPar()
mgeneSim(genes = missing, info = GeneSetCollection): Calculates all the similarities of the list and combine them using combineScoresPar()

Note

genes accept named characters and the output will use the names of the genes.

Examples

if (require("org.Hs.eg.db") & require("reactome.db")) {
    # Extract the paths of all genes of org.Hs.eg.db from KEGG
    # (last update in data of June 31st 2011)
    genes.kegg <- as.list(org.Hs.egPATH)
    # Extracts the paths of all genes of org.Hs.eg.db from reactome
    genes.react <- as.list(reactomeEXTID2PATHID)
    mgeneSim(c("81", "18", "10"), genes.react)
    mgeneSim(c("81", "18", "10"), genes.react, "avg")
    named_genes <- structure(c("81", "18", "10"),
        .Names = c("ACTN4", "ABAT", "NAT2")
    )
    mgeneSim(named_genes, genes.react, "max")
} else {
    warning("You need reactome.db and org.Hs.eg.db package for this example")
}
if (require("org.Hs.eg.db") & require("reactome.db")) {
    # Extract the paths of all genes of org.Hs.eg.db from KEGG
    # (last update in data of June 31st 2011)
    genes.kegg <- as.list(org.Hs.egPATH)
    # Extracts the paths of all genes of org.Hs.eg.db from reactome
    genes.react <- as.list(reactomeEXTID2PATHID)
    mgeneSim(c("81", "18", "10"), genes.react)
    mgeneSim(c("81", "18", "10"), genes.react, "avg")
    named_genes <- structure(c("81", "18", "10"),
        .Names = c("ACTN4", "ABAT", "NAT2")
    )
    mgeneSim(named_genes, genes.react, "max")
} else {
    warning("You need reactome.db and org.Hs.eg.db package for this example")
}

Calculates the Dice similarity between pathways

Description

Calculates the similarity between several pathways using dice similarity score. If one needs the matrix of similarities between pathways set the argument methods to NULL.

Usage

mpathSim(pathways, info, method = NULL, ...)

## S4 method for signature 'character,GeneSetCollection,ANY'
mpathSim(pathways, info, method = NULL, ...)

## S4 method for signature 'missing,GeneSetCollection,ANY'
mpathSim(pathways, info, method = NULL, ...)

## S4 method for signature 'missing,list,ANY'
mpathSim(pathways, info, method = NULL, ...)

## S4 method for signature 'missing,list,missing'
mpathSim(pathways, info, method = NULL, ...)
mpathSim(pathways, info, method = NULL, ...)

## S4 method for signature 'character,GeneSetCollection,ANY'
mpathSim(pathways, info, method = NULL, ...)

## S4 method for signature 'missing,GeneSetCollection,ANY'
mpathSim(pathways, info, method = NULL, ...)

## S4 method for signature 'missing,list,ANY'
mpathSim(pathways, info, method = NULL, ...)

## S4 method for signature 'missing,list,missing'
mpathSim(pathways, info, method = NULL, ...)

Arguments

`pathways`	Pathways to calculate the similarity for
`info`	A list of genes and the pathways they are involved or a GeneSetCollection object
`method`	To combine the scores of each pathway, one of `c("avg", "max", "rcmax", "rcmax.avg", "BMA")`, if NULL returns the matrix of similarities.
`...`	Other arguments passed to `combineScoresPar()`

Value

The similarity between those pathways or all the similarities between each comparison.

Methods (by class)

mpathSim(pathways = character, info = GeneSetCollection, method = ANY): Calculates the similarity between the provided pathways of the GeneSetCollection using combineScoresPar
mpathSim(pathways = missing, info = GeneSetCollection, method = ANY): Calculates all the similarities of the GeneSetCollection and combine them using combineScoresPar
mpathSim(pathways = missing, info = list, method = ANY): Calculates all the similarities of the list and combine them using combineScoresPar
mpathSim(pathways = missing, info = list, method = missing): Calculates all the similarities of the list

Note

pathways accept named characters, and then the output will have the names

Examples

if (require("reactome.db")) {
    genes.react <- as.list(reactomeEXTID2PATHID)
    (pathways <- sample(unique(unlist(genes.react)), 10))
    mpathSim(pathways, genes.react, NULL)
    named_paths <- structure(
        c("R-HSA-112310", "R-HSA-112316", "R-HSA-112315"),
        .Names = c(
            "Neurotransmitter Release Cycle",
            "Neuronal System",
            "Transmission across Chemical Synapses"
        )
    )
    mpathSim(named_paths, genes.react, NULL)
    many_pathways <- sample(unique(unlist(genes.react)), 152)
    mpathSim(many_pathways, genes.react, "avg")
} else {
    warning("You need reactome.db package for this example")
}
if (require("reactome.db")) {
    genes.react <- as.list(reactomeEXTID2PATHID)
    (pathways <- sample(unique(unlist(genes.react)), 10))
    mpathSim(pathways, genes.react, NULL)
    named_paths <- structure(
        c("R-HSA-112310", "R-HSA-112316", "R-HSA-112315"),
        .Names = c(
            "Neurotransmitter Release Cycle",
            "Neuronal System",
            "Transmission across Chemical Synapses"
        )
    )
    mpathSim(named_paths, genes.react, NULL)
    many_pathways <- sample(unique(unlist(genes.react)), 152)
    mpathSim(many_pathways, genes.react, "avg")
} else {
    warning("You need reactome.db package for this example")
}

Calculates the Dice similarity between pathways

Description

Calculates the similarity between pathways using dice similarity score. diceSim is used to calculate similarities between the two pathways.

Usage

pathSim(pathway1, pathway2, info)

## S4 method for signature 'character,character,GeneSetCollection'
pathSim(pathway1, pathway2, info)
pathSim(pathway1, pathway2, info)

## S4 method for signature 'character,character,GeneSetCollection'
pathSim(pathway1, pathway2, info)

Arguments

`pathway1`, `pathway2`	A single pathway to calculate the similarity
`info`	A GeneSetCollection or a list of genes and the pathways they are involved.

Value

The similarity between those pathways or all the similarities between each comparison.

Methods (by class)

pathSim(pathway1 = character, pathway2 = character, info = GeneSetCollection): Calculates all the similarities of a GeneSetCollection and combine them using combineScoresPar

Author(s)

Lluís Revilla

Examples

if (require("reactome.db")) {
    # Extracts the paths of all genes of org.Hs.eg.db from reactome
    genes.react <- as.list(reactomeEXTID2PATHID)
    (paths <- sample(unique(unlist(genes.react)), 2))
    pathSim(paths[1], paths[2], genes.react)
} else {
    warning("You need reactome.db package for this example")
}
if (require("reactome.db")) {
    # Extracts the paths of all genes of org.Hs.eg.db from reactome
    genes.react <- as.list(reactomeEXTID2PATHID)
    (paths <- sample(unique(unlist(genes.react)), 2))
    pathSim(paths[1], paths[2], genes.react)
} else {
    warning("You need reactome.db package for this example")
}

The position of the nodes is based on the similarity between them.

Description

The position of the nodes is based on the similarity between them.

Plot how similar are the data

Usage

plot_data(x, top)

plot_similarity(pd)
plot_data(x, top)

plot_similarity(pd)

Arguments

`x`	Matrix with the similarities.
`top`	a number between 0 and 1 to select the edges relating the elements of the matrix.
`pd`	The plot data from `plot_data()` function.

Value

A list with two elements:

nodes: The position and name of the nodes
edges: The information about the selected edges

A ggplot object

Examples

if (require("org.Hs.eg.db") & require("reactome.db")) {
  # Extract the paths of all genes of org.Hs.eg.db from KEGG
  # (last update in data of June 31st 2011)
  genes.kegg <- as.list(org.Hs.egPATH)
  # Extracts the paths of all genes of org.Hs.eg.db from reactome
  genes.react <- as.list(reactomeEXTID2PATHID)

  sim <- mgeneSim(c("81", "18", "10"), genes.react)
  pd <- plot_data(sim, top = 0.25)
  if (requireNamespace("ggplot2", quietly = TRUE)){
    plot_similarity(pd)
  }
}
if (require("org.Hs.eg.db") & require("reactome.db")) {
  # Extract the paths of all genes of org.Hs.eg.db from KEGG
  # (last update in data of June 31st 2011)
  genes.kegg <- as.list(org.Hs.egPATH)
  # Extracts the paths of all genes of org.Hs.eg.db from reactome
  genes.react <- as.list(reactomeEXTID2PATHID)

  sim <- mgeneSim(c("81", "18", "10"), genes.react)
  pd <- plot_data(sim, top = 0.25)
  if (requireNamespace("ggplot2", quietly = TRUE)){
    plot_similarity(pd)
  }
}

Remove duplicated rows and columns

Description

Given the indices of the duplicated entries remove the columns and rows until just one is left, it keeps the duplicated with the highest absolute mean value.

Usage

removeDup(cor_mat, dupli)
removeDup(cor_mat, dupli)

Arguments

`cor_mat`	List of matrices
`dupli`	List of indices with duplicated entries

Value

A matrix with only one of the columns and rows duplicated

Author(s)

Lluís Revilla

Examples

a <- seq2mat(c("52", "52", "53", "55"), runif(choose(4, 2)))
b <- seq2mat(c("52", "52", "53", "55"), runif(choose(4, 2)))
mat <- list("kegg" = a, "react" = b)
mat
dupli <- duplicateIndices(rownames(a))
remat <- removeDup(mat, dupli)
remat
a <- seq2mat(c("52", "52", "53", "55"), runif(choose(4, 2)))
b <- seq2mat(c("52", "52", "53", "55"), runif(choose(4, 2)))
mat <- list("kegg" = a, "react" = b)
mat
dupli <- duplicateIndices(rownames(a))
remat <- removeDup(mat, dupli)
remat

Transforms a vector to a symmetric matrix

Description

Fills a matrix of ncol = length(x) and nrow = length(x) with the values in dat and setting the diagonal to 1.

Usage

seq2mat(x, dat)
seq2mat(x, dat)

Arguments

`x`	names of columns and rows, used to define the size of the matrix
`dat`	Data to fill with the matrix with except the diagonal.

Details

dat should be at least choose(length(x), 2) of length. It assumes that the data provided comes from using the row and column id to obtain it.

Value

A square matrix with the diagonal set to 1 and dat on the upper and lower triangle with the columns ids and row ids from x.

Author(s)

Lluís Revilla

Examples

seq2mat(LETTERS[1:5], 1:10)
seq2mat(LETTERS[1:5], seq(from = 0.1, to = 1, by = 0.1))
seq2mat(LETTERS[1:5], 1:10)
seq2mat(LETTERS[1:5], seq(from = 0.1, to = 1, by = 0.1))

Apply a function to a list of similarities

Description

Function to join list of similarities by a function provided by the user.

Usage

similarities(sim, func, ...)
similarities(sim, func, ...)

Arguments

`sim`	list of similarities to be joined. All similarities must have the same dimensions. The genes are assumed to be in the same order for all the matrices.
`func`	function to perform on those similarities: `prod`, `sum`... It should accept as many arguments as similarities matrices are provided, and should use numbers.
`...`	Other arguments passed to the function `func`. Usually `na.rm` or similar.

Value

A matrix of the size of the similarities

Note

It doesn't check that the columns and rows of the matrices are in the same order or are the same.

Author(s)

Lluís Revilla

Examples

set.seed(100)
a <- seq2mat(LETTERS[1:5], rnorm(10))
b <- seq2mat(LETTERS[1:5], seq(from = 0.1, to = 1, by = 0.1))
sim <- list(b, a)
similarities(sim, weighted.prod, c(0.5, 0.5))
# Note the differences in the sign of some values
similarities(sim, weighted.sum, c(0.5, 0.5))
set.seed(100)
a <- seq2mat(LETTERS[1:5], rnorm(10))
b <- seq2mat(LETTERS[1:5], seq(from = 0.1, to = 1, by = 0.1))
sim <- list(b, a)
similarities(sim, weighted.prod, c(0.5, 0.5))
# Note the differences in the sign of some values
similarities(sim, weighted.sum, c(0.5, 0.5))

Weighted operations

Description

Calculates the weighted sum or product of x. Each values should have its weight, otherwise it will throw an error.

Usage

weighted.sum(x, w, abs = TRUE)

weighted.prod(x, w)
weighted.sum(x, w, abs = TRUE)

weighted.prod(x, w)

Arguments

`x`	an object containing the values whose weighted operations is to be computed
`w`	a numerical vector of weights the same length as `x` giving the weights to use for elements of `x`.
`abs`	If any `x` is negative you want the result negative too?

Details

This functions are thought to be used with similarities. As some similarities might be positive and others negative the argument abs is provided for weighted.sum, assuming that only one similarity will be negative (usually the one coming from expression correlation).

Value

weighted.sum returns the sum of the product of x*weights removing all NA values. See parameter abs if there are any negative values.

weighted.prod returns the product of product of x*weights removing all NA values.

Author(s)

Lluís Revilla

Examples

expr <- c(-0.2, 0.3, 0.5, 0.8, 0.1)
weighted.sum(expr, c(0.5, 0.2, 0.1, 0.1, 0.1))
weighted.sum(expr, c(0.5, 0.2, 0.1, 0.2, 0.1), FALSE)
weighted.sum(expr, c(0.4, 0.2, 0.1, 0.2, 0.1))
weighted.sum(expr, c(0.4, 0.2, 0.1, 0.2, 0.1), FALSE)
weighted.sum(expr, c(0.4, 0.2, 0, 0.2, 0.1))
weighted.sum(expr, c(0.5, 0.2, 0, 0.2, 0.1))
# Compared to weighted.prod:
weighted.prod(expr, c(0.5, 0.2, 0.1, 0.1, 0.1))
weighted.prod(expr, c(0.4, 0.2, 0.1, 0.2, 0.1))
weighted.prod(expr, c(0.4, 0.2, 0, 0.2, 0.1))
weighted.prod(expr, c(0.5, 0.2, 0, 0.2, 0.1))
expr <- c(-0.2, 0.3, 0.5, 0.8, 0.1)
weighted.sum(expr, c(0.5, 0.2, 0.1, 0.1, 0.1))
weighted.sum(expr, c(0.5, 0.2, 0.1, 0.2, 0.1), FALSE)
weighted.sum(expr, c(0.4, 0.2, 0.1, 0.2, 0.1))
weighted.sum(expr, c(0.4, 0.2, 0.1, 0.2, 0.1), FALSE)
weighted.sum(expr, c(0.4, 0.2, 0, 0.2, 0.1))
weighted.sum(expr, c(0.5, 0.2, 0, 0.2, 0.1))
# Compared to weighted.prod:
weighted.prod(expr, c(0.5, 0.2, 0.1, 0.1, 0.1))
weighted.prod(expr, c(0.4, 0.2, 0.1, 0.2, 0.1))
weighted.prod(expr, c(0.4, 0.2, 0, 0.2, 0.1))
weighted.prod(expr, c(0.5, 0.2, 0, 0.2, 0.1))

Package 'BioCor'

Help Index

BioCor: A package to calculate functional similarities

Description

Important functions

Author(s)

See Also

Additive integration of similarities

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Insert a matrix into another

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Similarity score between clusters of genes based on genes similarity

Description

Usage

Arguments

Details

Value

Methods (by class)

Author(s)

See Also

Examples

Similarity score between clusters of genes based on pathways similarity

Description

Usage

Arguments

Details

Value

Methods (by class)

Author(s)

See Also

Examples

i-th combination of n elements taken from r

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Combining values

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

See Also

Examples

Combine different sources of pathways

Description

Usage

Arguments

Details

Value

Examples

Convert the similarities formats

Description

Usage

Arguments

Value

Author(s)

Examples

Compare pathways

Description