Package 'pageRank' reference manual

Package 'pageRank'

Title:	Temporal and Multiplex PageRank for Gene Regulatory Network Analysis
Description:	Implemented temporal PageRank analysis as defined by Rozenshtein and Gionis. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis.
Authors:	Hongxu Ding [aut, cre, ctb, cph]
Maintainer:	Hongxu Ding <[email protected]>
License:	GPL-2
Version:	1.17.0
Built:	2024-10-30 09:24:13 UTC
Source:	https://github.com/bioc/pageRank

Title:

Temporal and Multiplex PageRank for Gene Regulatory Network Analysis

Description:

Implemented temporal PageRank analysis as defined by Rozenshtein and Gionis. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis.

Authors:

Hongxu Ding [aut, cre, ctb, cph]

Maintainer:

Hongxu Ding <[email protected]>

License:

GPL-2

Version:

1.17.0

Built:

2024-10-30 09:24:13 UTC

Source:

https://github.com/bioc/pageRank

Help Index

Build Network from Accessibility Peaks.

Description

Build network from accessibility, e.g. ATAC-Seq peaks.

Usage

accessibility_network(table, promoter, pfm, genome, p.cutoff = 5e-05, w = 7)
accessibility_network(table, promoter, pfm, genome, p.cutoff = 5e-05, w = 7)

Arguments

`table`	(data.frame) Peaks, with "Chr", "Start" and "End" in column name, and peak ID in row names.
`promoter`	(GRanges) Promoter regions.
`pfm`	(PFMatrixList) Positon Frequency Matrices (PFMs) of regulators.
`genome`	(BSgenome or character) Genome build in which regulator motifs will be searched.
`p.cutoff`	(numeric) P-value cutoff for motifs searching within peaks for TF identificaton.
`w`	(numeric) Window size for motifs searching within peaks for TF identificaton.

Value

(data.frame) Network, with "reg" and "target" in column name.

Author(s)

DING, HONGXU ([email protected])

Examples


table <- data.frame(Chr=c("chr1", "chr1"), Start=c(713689, 856337),
                    End=c(714685, 862152), row.names=c("A", "B"),
                    stringsAsFactors=FALSE)
regulators=c("FOXF2", "MZF1")
#peaks and regulators to be analyzed

library(GenomicRanges)
library(GenomicFeatures)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
library(annotate)
promoter <- promoters(genes(TxDb.Hsapiens.UCSC.hg19.knownGene))
names(promoter) <- getSYMBOL(names(promoter), data="org.Hs.eg")
promoter <- promoter[!is.na(names(promoter))]
#get promoter regions

library(JASPAR2018)
library(TFBSTools)
library(motifmatchr)
pfm <- getMatrixSet(JASPAR2018, list(species="Homo sapiens"))
pfm <- pfm[unlist(lapply(pfm, function(x) name(x))) %in% regulators]
#get regulator position frequency matrix (PFM) list

library(BSgenome.Hsapiens.UCSC.hg19)
accessibility_network(table, promoter, pfm, "BSgenome.Hsapiens.UCSC.hg19")
#generate network

table <- data.frame(Chr=c("chr1", "chr1"), Start=c(713689, 856337),
                    End=c(714685, 862152), row.names=c("A", "B"),
                    stringsAsFactors=FALSE)
regulators=c("FOXF2", "MZF1")
#peaks and regulators to be analyzed

library(GenomicRanges)
library(GenomicFeatures)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
library(annotate)
promoter <- promoters(genes(TxDb.Hsapiens.UCSC.hg19.knownGene))
names(promoter) <- getSYMBOL(names(promoter), data="org.Hs.eg")
promoter <- promoter[!is.na(names(promoter))]
#get promoter regions

library(JASPAR2018)
library(TFBSTools)
library(motifmatchr)
pfm <- getMatrixSet(JASPAR2018, list(species="Homo sapiens"))
pfm <- pfm[unlist(lapply(pfm, function(x) name(x))) %in% regulators]
#get regulator position frequency matrix (PFM) list

library(BSgenome.Hsapiens.UCSC.hg19)
accessibility_network(table, promoter, pfm, "BSgenome.Hsapiens.UCSC.hg19")
#generate network

Re-calculate PageRank

Description

Re-calculate PageRank with updated damping factor, personalized vector and edge weights.

Usage

adjust_graph(graph, damping = 0.85, personalized = NULL, weights = NULL)
adjust_graph(graph, damping = 0.85, personalized = NULL, weights = NULL)

Arguments

`graph`	(igraph) The graph to be adjusted.
`damping`	(numeric) Damping factor.
`personalized`	(numeric) Personalized vector.
`weights`	(numeric) Weight vector.

Value

(igraph) Network with updated "pagerank" as vertex attribute.

Author(s)

DING, HONGXU ([email protected])

Examples

library(igraph)
set.seed(1)
graph <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph)$name <- 1:100
igraph::V(graph)$pagerank <- igraph::page_rank(graph, damping=0.85)$vector
adjust_graph(graph, damping=0.1)

library(igraph)
set.seed(1)
graph <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph)$name <- 1:100
igraph::V(graph)$pagerank <- igraph::page_rank(graph, damping=0.85)$vector
adjust_graph(graph, damping=0.1)

Re-format ARACNe Network.

Description

Re-format ARACNe network in regulon object to data.frame with regulator, target and direction columns.

Usage

aracne_network(regulon)
aracne_network(regulon)

Arguments

regulon

(regulon) ARACNe network.

Value

(data.frame) Network, with "reg", "target" and "direction" in column name. For direction, 1/0 denotes positive/negative regulation.

Author(s)

DING, HONGXU ([email protected])

Examples

library(bcellViper)
data(bcellViper)
aracne_network(regulon[1:10])

library(bcellViper)
data(bcellViper)
aracne_network(regulon[1:10])

Make Bubbleplot

Description

Make bubbleplot.

Usage

bubble_plot(
  s_mat,
  c_mat,
  n_mat,
  col = colorRampPalette(c("Blue", "Grey", "Red"))(100),
  breaks = seq(-2, 2, length.out = 100),
  main = NULL
)
bubble_plot(
  s_mat,
  c_mat,
  n_mat,
  col = colorRampPalette(c("Blue", "Grey", "Red"))(100),
  breaks = seq(-2, 2, length.out = 100),
  main = NULL
)

Arguments

`s_mat`	(matrix) Matrix denotes the size of bubbles.
`c_mat`	(matrix) Matrix denotes the color of bubbles.
`n_mat`	(matrix) Matrix denotes the name of bubbles.
`col`	(character) Colors.
`breaks`	(numeric) Breakpoints of colors.
`main`	(character) Title.

Value

(NULL) A bubbloplot.

Author(s)

DING, HONGXU ([email protected])

Examples

s_mat <- c_mat <- n_mat <- matrix(1:12, 3, 4, dimnames=list(1:3, 1:4))
bubble_plot(s_mat, c_mat, n_mat, breaks=seq(1, 12, length.out=100), main="")

s_mat <- c_mat <- n_mat <- matrix(1:12, 3, 4, dimnames=list(1:3, 1:4))
bubble_plot(s_mat, c_mat, n_mat, breaks=seq(1, 12, length.out=100), main="")

Clean Graph

Description

Remove graph nodes by residing subgraph sizes, vertex names and PageRank values.

Usage

clean_graph(graph, size = NULL, vertices = NULL, pagerank = NULL)
clean_graph(graph, size = NULL, vertices = NULL, pagerank = NULL)

Arguments

`graph`	(igraph) The graph to be cleaned.
`size`	(numeric) Subgraph size cutoff.
`vertices`	(character) Vertices to be kept.
`pagerank`	(numeric) PageRank cutoff.

Value

(igraph) Network updated "pagerank" as vertex attribute.

Author(s)

DING, HONGXU ([email protected])

Examples

library(igraph)
set.seed(1)
graph <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph)$name <- 1:100
igraph::V(graph)$pagerank <- igraph::page_rank(graph)$vector
clean_graph(graph, size=5)
library(igraph)
set.seed(1)
graph <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph)$name <- 1:100
igraph::V(graph)$pagerank <- igraph::page_rank(graph)$vector
clean_graph(graph, size=5)

Build Network from Conformation Peaks.

Description

Build network from conformation, e.g. HiChIP records.

Usage

conformation_network(
  table,
  promoter,
  pfm,
  genome,
  range = 500,
  p.cutoff = 5e-05,
  w = 7
)
conformation_network(
  table,
  promoter,
  pfm,
  genome,
  range = 500,
  p.cutoff = 5e-05,
  w = 7
)

Arguments

`table`	(data.frame) Records, with "Chr1", "Position1", "Strand1", "Chr2", "Position2" and "Strand2" in column name, and record ID in row names.
`promoter`	(GRanges) Promoter regions.
`pfm`	(PFMatrixList) Positon Frequency Matrices (PFMs) of regulators.
`genome`	(BSgenome or character) Genome build in which regulator motifs will be searched.
`range`	(numeric) Search radius from "Position1" and "Position2" for promoters.
`p.cutoff`	(numeric) P-value cutoff for motifs searching within peaks for TF identificaton.
`w`	(numeric) Window size for motifs searching within peaks for TF identificaton.

Value

(data.frame) Network, with "reg" and "target" in column name.

Author(s)

DING, HONGXU ([email protected])

Examples

table <- data.frame(Chr1=c("chr1", "chr1"),
                    Position1=c(569265, 713603),
                    Strand1=c("+", "+"),
                    Chr2=c("chr4", "chr1"),
                    Position2=c(206628, 715110),
                    Strand2=c("+", "-"),
                    row.names=c("A", "B"), stringsAsFactors=FALSE)
regulators=c("FOXF2", "MZF1")
#peaks and regulators to be analyzed

library(GenomicRanges)
library(GenomicFeatures)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
library(annotate)
promoter <- promoters(genes(TxDb.Hsapiens.UCSC.hg19.knownGene))
names(promoter) <- getSYMBOL(names(promoter), data="org.Hs.eg")
promoter <- promoter[!is.na(names(promoter))]
#get promoter regions

library(JASPAR2018)
library(TFBSTools)
library(motifmatchr)
pfm <- getMatrixSet(JASPAR2018, list(species="Homo sapiens"))
pfm <- pfm[unlist(lapply(pfm, function(x) name(x))) %in% regulators]
#get regulator position frequency matrix (PFM) list

library(BSgenome.Hsapiens.UCSC.hg19)
conformation_network(table, promoter, pfm, "BSgenome.Hsapiens.UCSC.hg19")

table <- data.frame(Chr1=c("chr1", "chr1"),
                    Position1=c(569265, 713603),
                    Strand1=c("+", "+"),
                    Chr2=c("chr4", "chr1"),
                    Position2=c(206628, 715110),
                    Strand2=c("+", "-"),
                    row.names=c("A", "B"), stringsAsFactors=FALSE)
regulators=c("FOXF2", "MZF1")
#peaks and regulators to be analyzed

library(GenomicRanges)
library(GenomicFeatures)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
library(annotate)
promoter <- promoters(genes(TxDb.Hsapiens.UCSC.hg19.knownGene))
names(promoter) <- getSYMBOL(names(promoter), data="org.Hs.eg")
promoter <- promoter[!is.na(names(promoter))]
#get promoter regions

library(JASPAR2018)
library(TFBSTools)
library(motifmatchr)
pfm <- getMatrixSet(JASPAR2018, list(species="Homo sapiens"))
pfm <- pfm[unlist(lapply(pfm, function(x) name(x))) %in% regulators]
#get regulator position frequency matrix (PFM) list

library(BSgenome.Hsapiens.UCSC.hg19)
conformation_network(table, promoter, pfm, "BSgenome.Hsapiens.UCSC.hg19")

Calculate Temporal PageRank from Two Graphs

Description

Calculate temporal PageRank by changing edges between graph1 and graph2. This is a simplified version of temporal PageRank described by Rozenshtein and Gionis, by only analyzing temporally adjacent graph pairs.

Usage

diff_graph(graph1, graph2)
diff_graph(graph1, graph2)

Arguments

`graph1`	(igraph) The 1st graph.
`graph2`	(igraph) The 2nd graph.

Value

(igraph) Network graph1-graph2 with "moi (mode of interaction)" and "pagerank" as edge and vertex attributes.

Author(s)

DING, HONGXU ([email protected])

References

Rozenshtein, Polina, and Aristides Gionis. "Temporal pagerank." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2016.

Examples

library(igraph)
set.seed(1)
graph1 <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph1)$name <- 1:100
set.seed(2)
graph2 <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph2)$name <- 1:100
diff_graph(graph1, graph2)

library(igraph)
set.seed(1)
graph1 <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph1)$name <- 1:100
set.seed(2)
graph2 <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph2)$name <- 1:100
diff_graph(graph1, graph2)

Generate Color Gradient

Description

Generate color gradient for, e.g. gene expression.

Usage

get_color_gradient(
  x,
  col = colorRampPalette(c("Blue", "Red"))(100),
  breaks = seq(-2, 2, length.out = 100)
)
get_color_gradient(
  x,
  col = colorRampPalette(c("Blue", "Red"))(100),
  breaks = seq(-2, 2, length.out = 100)
)

Arguments

`x`	(numeric) Vector based on which color gradient is generated.
`col`	(character) Color vector.
`breaks`	(numeric) A set of breakpoints for the colors. Must be the same length of col.

Value

(character) Colors.

Author(s)

DING, HONGXU ([email protected])

Examples

get_color_gradient(-2:2)

get_color_gradient(-2:2)

Calculate Multiplex PageRank

Description

Calculate multiplex PageRank following defination by Halu et al.

Usage

multiplex_page_rank(graph, ..., beta = 1, gamma = 1, damping = 0.85)
multiplex_page_rank(graph, ..., beta = 1, gamma = 1, damping = 0.85)

Arguments

`graph`	(igraph) The base graph with pagerank and name as vertex attributes.
`...`	(igraph) Supporter graphs with pagerank and name as vertex attributes.
`beta`	(numeric) Parameters for adjusting supporter graph PageRank values. For the same nodes, PageRank values from different supporter graphs will first be multiplicated. The products will then be exponentiate by beta and gamma, as outgoing edge weights and personalizations of the base graph. Four special multiplex PageRank forms are defined by varying (beta, gamma), including additive (0, 1), multiplicative (1, 0), combined (1, 1) and neutral (0, 0).
`gamma`	(numeric) Parameters for adjusting supporter graph PageRank values. For the same nodes, PageRank values from different supporter graphs will first be multiplicated. The products will then be exponentiate by beta and gamma, as outgoing edge weights and personalizations of the base graph. Four special multiplex PageRank forms are defined by varying (beta, gamma), including additive (0, 1), multiplicative (1, 0), combined (1, 1) and neutral (0, 0).
`damping`	(numeric) Damping factor.

Value

(numeric) Multiplex PageRank values.

Author(s)

DING, HONGXU ([email protected])

References

Halu, Arda, et al. "Multiplex pagerank." PloS one 8.10 (2013).

Examples

library(igraph)
set.seed(1)
graph1 <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph1)$name <- 1:100
igraph::V(graph1)$pagerank <- igraph::page_rank(graph1)$vector
set.seed(2)
graph2 <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph2)$name <- 1:100
igraph::V(graph2)$pagerank <- igraph::page_rank(graph2)$vector
multiplex_page_rank(graph1, graph2)

library(igraph)
set.seed(1)
graph1 <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph1)$name <- 1:100
igraph::V(graph1)$pagerank <- igraph::page_rank(graph1)$vector
set.seed(2)
graph2 <- igraph::erdos.renyi.game(100, 0.01, directed = TRUE)
igraph::V(graph2)$name <- 1:100
igraph::V(graph2)$pagerank <- igraph::page_rank(graph2)$vector
multiplex_page_rank(graph1, graph2)

Build Probability-Based Network

Description

Build probability-based regulator-target interaction network.

Usage

P_graph(
  expmat,
  net,
  sep = 5,
  method = c("difference", "mi"),
  null = NULL,
  threshold = 0.001
)
P_graph(
  expmat,
  net,
  sep = 5,
  method = c("difference", "mi"),
  null = NULL,
  threshold = 0.001
)

Arguments

`expmat`	(matrix) Gene expression matrix.
`net`	(data.frame) Network, with "reg" and "target" in column name.
`sep`	(numeric) Number of bins for calculating marginal/joint probability.
`method`	(character) Method for calculating probability-based distance, either PXY-PXPY ("difference") or mutual information ("mi").
`null`	(ecdf) Null distribution of probability-based distance. Either from random interactions by P_null function, or all interactions in net.
`threshold`	(numeric) P-value threshold for filtering interactions in net.

Value

(igraph) Network graph with "pvalue" and "direction", and "pagerank" as edge/vertex attributes.

Author(s)

DING, HONGXU ([email protected])

Examples

library(bcellViper)
data(bcellViper)
dset <- exprs(dset)
net <- do.call(rbind, lapply(1:10, function(i, regulon){
  data.frame(reg=rep(names(regulon)[i], 10),
             target=names(regulon[[i]][[1]])[1:10],
             direction=rep(1, 10),
             stringsAsFactors = FALSE)}, regulon=regulon))
P_graph(dset, net, method="difference", null=NULL, threshold=0.05)

library(bcellViper)
data(bcellViper)
dset <- exprs(dset)
net <- do.call(rbind, lapply(1:10, function(i, regulon){
  data.frame(reg=rep(names(regulon)[i], 10),
             target=names(regulon[[i]][[1]])[1:10],
             direction=rep(1, 10),
             stringsAsFactors = FALSE)}, regulon=regulon))
P_graph(dset, net, method="difference", null=NULL, threshold=0.05)

Build Null Distribution of Probability-Based Distance

Description

Build null model for evaluating the significance of interactions by generating random regulator-target pairs.

Usage

P_null(expmat, net, n = 10000, sep = 5, method = c("difference", "mi"))
P_null(expmat, net, n = 10000, sep = 5, method = c("difference", "mi"))

Arguments

`expmat`	(matrix) Gene expression matrix.
`net`	(data.frame) Network, with "reg" and "target" in column name.
`n`	(numeric) Number of random pairs.
`sep`	(numeric) Number of bins for calculating marginal/joint probability.
`method`	(character) Method for calculating probability-based distance, either PXY-PXPY ("difference") or mutual information ("mi").

Value

(ecdf) ECDF of null distribution.

Author(s)

DING, HONGXU ([email protected])

Examples

library(bcellViper)
data(bcellViper)
dset <- exprs(dset)
net <- do.call(rbind, lapply(1:10, function(i, regulon){
  data.frame(reg=rep(names(regulon)[i], 10),
             target=names(regulon[[i]][[1]])[1:10],
             direction=rep(1, 10),
             stringsAsFactors = FALSE)}, regulon=regulon))
P_null(dset, net, n=100, method="difference")

library(bcellViper)
data(bcellViper)
dset <- exprs(dset)
net <- do.call(rbind, lapply(1:10, function(i, regulon){
  data.frame(reg=rep(names(regulon)[i], 10),
             target=names(regulon[[i]][[1]])[1:10],
             direction=rep(1, 10),
             stringsAsFactors = FALSE)}, regulon=regulon))
P_null(dset, net, n=100, method="difference")

Generate Timewise Average Gene Expression

Description

Generate timewise average gene expression.

Usage

time_expmat(time, expmat)
time_expmat(time, expmat)

Arguments

`time`	(character) Time-annotation of samples.
`expmat`	(matrix) Gene expression matrix.

Value

(matrix) Time-wise average gene expression.

Author(s)

DING, HONGXU ([email protected])

Examples

expmat <- matrix(rnorm(90), 10, 9, dimnames=list(LETTERS[1:10], 1:9))
time <- c(rep("T1", 3), rep("T2", 3), rep("T3", 3))
time_expmat(time, expmat)

expmat <- matrix(rnorm(90), 10, 9, dimnames=list(LETTERS[1:10], 1:9))
time <- c(rep("T1", 3), rep("T2", 3), rep("T3", 3))
time_expmat(time, expmat)