| Type: | Package |
| Title: | Inferring Chromatin Interaction Modules from 3C-Based Data |
| Version: | 0.1.38 |
| Author: | Sora Yoon [aut, cre] |
| Maintainer: | Sora Yoon <sora.yoon@pennmedicine.upenn.edu> |
| Description: | Identifies chromatin interaction modules by constructing a Hi-C contact network based on statistically significant interactions, followed by network clustering. The method enables comparison of module connectivity across two Hi-C datasets and is capable of detecting cell-type-specific regulatory modules. By integrating network analysis with chromatin conformation data, this approach provides insights into the spatial organization of the genome and its functional implications in gene regulation. Author: Sora Yoon (2025) https://github.com/ysora/HiCociety. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Depends: | R (≥ 3.5.0) |
| Imports: | strawr, shape, fitdistrplus, igraph, ggraph, foreach, doParallel, biomaRt, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, org.Hs.eg.db, Rcpp, AnnotationDbi, GenomicFeatures, parallel, IRanges, S4Vectors, grDevices, graphics, stats, BiocManager, BiocGenerics, GenomicRanges, pracma, signal, HiCocietyExample |
| LinkingTo: | Rcpp |
| NeedsCompilation: | yes |
| Packaged: | 2025-05-09 21:22:42 UTC; sora |
| Repository: | CRAN |
| Date/Publication: | 2025-05-13 08:20:02 UTC |
Connectivity difference between two conditions
Description
output table of connectivity difference of modules between cell types is generated.
Usage
ConnectivityDiff(wt, ko, prefix.wt, prefix.ko, resolution = 5000)
Arguments
wt |
hic2community result from condition 1 |
ko |
hic2community result from condition 2 |
prefix.wt |
Prefix for wt to be presented in the column names |
prefix.ko |
Prefix for ko to be presented in the column names |
resolution |
Resolution of Hi-C dataset |
Details
Connectivity difference between two conditions
Value
A list of two data.frame objects, each representing the network connectivity differences of modules in condition 1 or condition 2 when compared to the counterpart cell type. Each data.frame contains the following columns: "chr", "module_start", "module_end", "connectivity", "transitivity", "centrality_node", "idx" (the row index of the module in the input module object), "connectivity_in_(counterpart_cell_type)", "connectivity_difference", and "connectivity_foldchange".
Author(s)
Sora Yoon, PhD
Examples
modulefile1 = system.file('extdata','mouse_naiveCD4T_Vahedi_short.rds',
package = 'HiCocietyExample')
modulefile2 = system.file('extdata','mouse_Th1_Vahedi_short.rds',
package = 'HiCocietyExample')
mycom1 = readRDS(modulefile1)
mycom2 = readRDS(modulefile2)
result = ConnectivityDiff(mycom1, mycom2, 'NaiveCD4T', 'Th1',
resolution = 5000)
head(print(result))
Add gene information
Description
This function adds a column with a list of genes included in each locus to the ModuleSummary data frame of the hic2community function.
Usage
add_Genes(df, speciesObj)
Arguments
df |
The ModuleSummary data frame obtained by running hic2community function |
speciesObj |
Any Txdb package name corresponding |
Details
Adding gene list to ModuleSummary data frame obtained from hic2community function.
Value
A data.frame identical to the input, with an additional "Genes" column. Each entry in this column lists the gene(s) that overlap with the corresponding genomic region. If multiple genes are present, they are concatenated with commas.
Author(s)
Sora Yoon, PhD
Examples
modulefile = system.file('extdata','mouse_naiveCD4T_Vahedi_short.rds',
package = 'HiCocietyExample')
mycom = readRDS(modulefile)
mycom$ModuleSummary = add_Genes(mycom$ModuleSummary,
'TxDb.Mmusculus.UCSC.mm10.knownGene')
Calculate Average Count within 5-pixel Padding
Description
This function calculates the average count within a 25kb padding around each (x, y) coordinate pair.
Usage
calculate_avg_count(x, y, counts, resol)
Arguments
x |
Numeric vector of x-coordinates of contact frequency data frame. |
y |
Numeric vector of y-coordinates of contact frequency data frame. |
counts |
Numeric vector of contact frequency counts. |
resol |
Integer specifying the HiC resolution. |
Value
A numeric vector of average counts.
Examples
x <- c(1, 2, 3, 4, 5)
y <- c(1, 2, 3, 4, 5)
counts <- c(10, 20, 30, 40, 50)
resol <- 10000
calculate_avg_count(x, y, counts, resol)
Check if a package is installed
Description
This function checks whether a package is installed. If the package is not installed, it informs the user that the package is missing.
Usage
check_package(package)
Arguments
package |
The name of the package to check. |
Details
This function checks whether a package is installed. If the package is not installed, it informs the user that the package is missing.
Value
A logical value: TRUE if the package is installed, FALSE otherwise.
Author(s)
Sora Yoon, PhD
Examples
check_package('dplyr')
Get contact Frequency
Description
Retrieve contact frequency from .hic file using strawR package.
Usage
getContactFrequency(fname, chr, resol)
Arguments
fname |
.hic data for any types of genome conformation capture data |
chr |
Chromosome number of network extraction |
resol |
DNA basepair Resolution. Default=10000 |
Details
Get Contact Frequncy from .hic file
Value
A data.frame containing three columns: x and y (genomic coordinate pairs), and counts (the contact frequency between them).
Author(s)
Sora Yoon, PhD
Examples
myhic=system.file('extdata', 'example.hic', package ='HiCocietyExample')
A = getContactFrequency(myhic,19,5000)
head(print(A))
Contact probability
Description
It estimates contact probability based on the distance of a pair of a loci.
Usage
getContactProbability(
tab,
farthest = 2000000,
resol = 10000,
prob,
n_cores = NULL
)
Arguments
tab |
Output from getContactFrequency function. |
farthest |
Maximum 1-D distance to search. Default=2Mb |
resol |
Hi-C resolution for test. Default = 10000 |
prob |
Significance cutoff for negative binomial distribution. Default =0.975 |
n_cores |
The number of cores used for parallel computing. If set as NULL, n_cores is automatically set to the number of cores in the computer if it is not exceed 30. If it is more than 30, it is set as 30. Default = NULL |
Details
Get Contact probablity
Value
A list containing three objects: AREA, original, and len1, representing the statistical significance of each chromatin interaction pair.
Author(s)
Sora Yoon, PhD
Examples
# This example might take a long time to run, so we wrap it in donttest{}
myhic = system.file('extdata','example.hic',package = 'HiCocietyExample')
mydf=getContactFrequency(myhic, 19, 5000);
myprob=getContactProbability(mydf,farthest=2000000, resol=5000,prob=0.975,
n_cores=2);
Estimation of elbow point from J-shaped-curve
Description
It provides a point of the highest curvature from a J-shaped-plot
Usage
getElbowPoint(numbers)
Arguments
numbers |
Numeric vector |
Details
Estimation of elbow point from J-shaped curve
Value
A list containing two elements: index, the index of a point in a sorted vector of numbers in descending order, representing the point where the tangent is closest to one, and ConnectivityCutoff, the corresponding value at that point.
Author(s)
Sora Yoon, PhD
Examples
modulefile = system.file('extdata','mouse_naiveCD4T_Vahedi_short.rds',
package = 'HiCocietyExample')
mycom = readRDS(modulefile)
connec = mycom$ModuleSummary$connectivity
getElbowPoint(connec)
Retrieve chromosome names from .hic file
Description
It retrieves all chromosome names having longer than 2.5Mbp.
Usage
get_all_chr_names(fname)
Arguments
fname |
Path to .hic file |
Details
To extract all chromosome names from .hic file
Value
A character vector containing the names of chromosomes whose genomic lengths exceed 2.5 Mbp.
Author(s)
Sora Yoon, PhD
Examples
myhic=system.file('extdata', 'example.hic', package ='HiCocietyExample')
get_all_chr_names(myhic)
All available Txdb
Description
It finds all available Txdb packages used in add_Genes function.
Usage
get_txdb()
Details
Check all available Txdb package
Value
A character vector containing the names of all available TxDb packages.
Author(s)
Sora Yoon, PhD
Examples
get_txdb()
Create module objects from the Hi-C data
Description
It generates a list of graph of significant interactions, module table and module elements.
Usage
hic2community(
fname,
chr,
resol,
nbprob,
farthest,
par.noise = 1,
network.cluster.method = "louvain",
n_cores = NULL
)
Arguments
fname |
Path to .hic file |
chr |
chromosome numbers to run. |
resol |
Resolution of Hi-C data |
nbprob |
Negative binomial probability. Higher value gives smaller number of stronger interaction. |
farthest |
The maximum searching distance between two nodes |
par.noise |
Parameter for noise removal. Default is 1, higher value gives more filtered interactions. |
network.cluster.method |
Can select between 'louvain' as default and 'label_prop' which means the label propagation method. |
n_cores |
The number of cores used for parallel computing. If set as NULL, n_cores is automatically set to the number of cores in the computer if it is not exceed 30. If it is more than 30, it is set as 30. Default = NULL |
Details
It generates a list of graph of significant interactions, module table and module elements.
Value
A list containing three elements: Graphs (an igraph object representing significant chromatin interactions for each chromosome), ModuleSummary (a data.frame containing information about chromatin interaction modules), and ModuleElements (a list of nodes forming significant chromatin interactions within each module).
Author(s)
Sora Yoon, PhD
Examples
# This example might take a long time to run, so we wrap it in donttest{}
myhic=system.file('extdata', 'example.hic', package ='HiCocietyExample')
mycom = hic2community(myhic, "19", 5000, 0.975, 2000000,
par.noise=1, 'louvain', n_cores=2)
HiC to network data format
Description
It converts Hi-C dataframe to network object.
Usage
hic2network(ftab)
Arguments
ftab |
three-column data composed of locus1, locus2 and value |
Details
Convert HiC to network data format
Value
An igraph object representing statistically significant chromatin interactions.
Author(s)
Sora Yoon, PhD
Examples
# This example might take a long time to run, so we wrap it in donttest{}
myhic=system.file('extdata', 'example.hic', package ='HiCocietyExample')
ftab=getContactFrequency(myhic,19,5000);
net = hic2network(ftab[1:100,]);
plot(net)
Visualization of module
Description
It draws a triangle heatmap and arcplot of a module
Usage
visualizeModule(
hicpath,
HC.object,
moduleNum,
resolution,
hic.norm,
heatmap.color.range = NULL,
heatmap.color = colorRampPalette(c("white", "red")),
arc.depth = 10,
arc.color = "gray80",
nbnom.param = 0.99,
txdb = "TxDb.Mmusculus.UCSC.mm10.knownGene",
gene.strand.arrow.lwd = 3,
gene.strand.lwd = 6,
col.forward.gene = "purple",
col.reverse.gene = "pink",
highlight.centrality = FALSE,
highlight.cent.col = FALSE,
highlight.node = NULL,
highlight.node.col = NULL,
show.sig.int = TRUE,
netinfo
)
Arguments
hicpath |
Path to the .hic file |
HC.object |
The object name from hic2community result |
moduleNum |
The row index of module to draw |
resolution |
Resolution of HiC data |
hic.norm |
Normalization method. If not, set 'NONE' |
heatmap.color.range |
Min and max value of contact frequency, e.g., c(0,10) |
heatmap.color |
Color for heatmap. For example, colorRampPalette(c("white","red)) |
arc.depth |
Height of arc plot |
arc.color |
Arc color |
nbnom.param |
Negative binomial probability cutoff. Higher cutoff gives less number of arcs. |
txdb |
Character. One of Txdb list obtained from get_txdb(). |
gene.strand.arrow.lwd |
Numeric. Line width of arrowhead indicating the strands of genes. Same as arr.lwd option in Arrows function in shape package. |
gene.strand.lwd |
Numeric. Line width of arrow body indicating the strands of genes. Same as lwd option in Arros function in shape package. |
col.forward.gene |
Character. Color of arrows within gene track for forward genes. |
col.reverse.gene |
Character. Color of arrows within gene track for reverse genes |
highlight.centrality |
Boolean input to set if highlight eigenvector centrality node. |
highlight.cent.col |
The color of arcs stemming from the centrality node. |
highlight.node |
The coordiante of a node of which the user will highlight the arcs stemming from this node. Default=NULL |
highlight.node.col |
The color of arcs stemming from the node which the user highlight. |
show.sig.int |
Boolean. If TRUE, it marks significant contact on the triangle heatmap. |
netinfo |
Boolean. If TRUE, it shows network information of the module as text in the plot. |
Details
Visualization of module
Value
No return value; the function generates a plot.
Author(s)
Sora Yoon, PhD
Examples
# A slow example that takes too long to run, wrapped in donttest{}
myhic = system.file('extdata','example.hic',package = 'HiCocietyExample')
HC.object = hic2community(myhic, "19", 5000, 0.975, 2000000, par.noise=1,
'louvain', n_cores=2)
mNum = 1
visualizeModule(hicpath = myhic, HC.object = HC.object, moduleNum = mNum,
resolution = 5000,
hic.norm = 'NONE', heatmap.color.range=c(0,10),
heatmap.color = colorRampPalette(c('white','red')),
arc.depth=10, arc.color = "gray80", nbnom.param=0.99,
txdb = 'TxDb.Mmusculus.UCSC.mm10.knownGene',
gene.strand.arrow.lwd = 3, gene.strand.lwd = 3,
col.forward.gene = 'purple', col.reverse.gene = 'pink',
highlight.centrality=FALSE, highlight.cent.col=FALSE,
highlight.node=NULL, highlight.node.col=NULL,
show.sig.int=FALSE, netinfo=FALSE)