Package 'immcp' reference manual

Title:	Poly-Pharmacology Toolkit for Traditional Chinese Medicine Research
Description:	Toolkit for Poly-pharmacology Research of Traditional Chinese Medicine. Based on the biological descriptors and drug-disease interaction networks, it can analyze the potential poly-pharmacological mechanisms of Traditional Chinese Medicine and be used for drug-repositioning in Traditional Chinese Medicine.
Authors:	Yuanlong Hu [aut, cre]
Maintainer:	Yuanlong Hu <[email protected]>
License:	GPL (>= 3)
Version:	1.0.5
Built:	2024-11-10 04:47:32 UTC
Source:	https://github.com/yuanlonghu/immcp

Class `BasicData` This class represents the basic input data.

Description

Class BasicData This class represents the basic input data.

Slots

drugnet: A directed graph
diseasenet: Disease network.
biomarker: Disease-related gene.

Author(s)

Yuanlong Hu

Class `BioDescr` This class represents the biological descriptor data.

Description

Class BioDescr This class represents the biological descriptor data.

Slots

drug_geneset: from drug to geneset.
geneset_gene: from geneset to gene for each drug.
anno: Geneset ID and description.

Author(s)

Yuanlong Hu

CreateBasicData

Description

Create BasicData Object

Usage

CreateBasicData(..., diseasenet = NULL, biomarker = NULL)
CreateBasicData(..., diseasenet = NULL, biomarker = NULL)

Arguments

`...`	Drug graph from `PrepareData`.
`diseasenet`	A graph of Disease-related gene from `PrepareData`.
`biomarker`	Character vector, the vector of Disease-related gene.

Value

A BasicData object.

Author(s)

Yuanlong Hu

Examples

data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)

CreateDisDrugNet

Description

Create Disease-Drug Network

Usage

CreateDisDrugNet(BasicData, drug, disease)
CreateDisDrugNet(BasicData, drug, disease)

Arguments

`BasicData`	BasicData object.
`drug`	Character vector, the drug.
`disease`	Character vector, the disease.

Value

A igraph object.

Author(s)

Yuanlong Hu

Examples

data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
DisDrugNet <- CreateDisDrugNet(BasicData, drug = "Drug1", disease = "disease")
data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
DisDrugNet <- CreateDisDrugNet(BasicData, drug = "Drug1", disease = "disease")

diff_network_char

Description

Calculate the difference of network characters in two network

Usage

diff_network_char(graph1, graph2, output_all = FALSE)
diff_network_char(graph1, graph2, output_all = FALSE)

Arguments

`graph1`	A igraph object.
`graph2`	A igraph object.
`output_all`	FALSE

Value

A number vector.

Author(s)

Yuanlong Hu

Datasets Demo dataset

Description

Datasets Demo dataset

enrich_f

Description

Enrich Analysis

Usage

enrich_f(
  target_character,
  geneset = c("kegg", "mkegg", "go", "wp"),
  arguments = list(minGSSize = 5, maxGSSize = 500, pvalue = 0.05, qvalue = 0.1),
  out_dataframe = TRUE,
  to_ENTREZID = TRUE
)
enrich_f(
  target_character,
  geneset = c("kegg", "mkegg", "go", "wp"),
  arguments = list(minGSSize = 5, maxGSSize = 500, pvalue = 0.05, qvalue = 0.1),
  out_dataframe = TRUE,
  to_ENTREZID = TRUE
)

Arguments

`target_character`	Charactor vector of gene.
`geneset`	Charactor vector, one of "kegg"(KEGG), "mkegg"(KEGG Module), "go"(GO-BP), and "wp"(WikiPathways); a data frame and list.
`arguments`	A list of the arguments of `clusterProfiler`, including `minGSSize`, `maxGSSize`, `pvalue`, and `qvalue`.
`out_dataframe`	Logical, whether to output data frame,defaults to `FALSE`.
`to_ENTREZID`	Logical, whether to translate to ENTREZID from SYMBOL, defaults to `TRUE`.

Value

data frame

Author(s)

Yuanlong Hu

Export an xlsx file to Cytoscape

Description

Export an xlsx file to Cytoscape.

Usage

exportCytoscape(graph, file)
exportCytoscape(graph, file)

Arguments

`graph`	igraph object.
`file`	file

Value

A workbook object

Author(s)

Yuanlong Hu

Extract Biological descriptor

Description

Extract Biological descriptor

Usage

extr_biodescr(
  BasicData,
  geneset = c("kegg", "mkegg", "go", "wp"),
  arguments = list(minGSSize = 5, maxGSSize = 500, pvalue = 0.05, qvalue = 0.1),
  ref_type = "drug",
  ref = NULL,
  to_ENTREZID = TRUE
)
extr_biodescr(
  BasicData,
  geneset = c("kegg", "mkegg", "go", "wp"),
  arguments = list(minGSSize = 5, maxGSSize = 500, pvalue = 0.05, qvalue = 0.1),
  ref_type = "drug",
  ref = NULL,
  to_ENTREZID = TRUE
)

Arguments

`BasicData`	BasicData object.
`geneset`	Charactor vector, one of "kegg"(KEGG), "mkegg"(KEGG Module), "go"(GO-BP), and "wp"(WikiPathways); a data frame and list.
`arguments`	A list of the arguments of `clusterProfiler`, including `minGSSize`, `maxGSSize`, `pvalue`, and `qvalue`.
`ref_type`	Charactor vector, one of "drug", "herb", "compound" or "target", defaults to "drug".
`ref`	Charactor vector, reference drug, herb, compound or target, defaults to `NULL`.
`to_ENTREZID`	Logical, whether to translate to ENTREZID from SYMBOL, defaults to TRUE.

Value

A BioDescr object.

Author(s)

Yuanlong Hu

Extract Biological descriptor

Description

Extract Biological descriptor

Usage

## S4 method for signature 'BasicData'
extr_biodescr(
  BasicData,
  geneset = c("kegg", "mkegg", "go", "wp"),
  arguments = list(minGSSize = 5, maxGSSize = 500, pvalue = 0.05, qvalue = 0.1),
  ref_type = "drug",
  ref = NULL,
  to_ENTREZID = TRUE
)
## S4 method for signature 'BasicData'
extr_biodescr(
  BasicData,
  geneset = c("kegg", "mkegg", "go", "wp"),
  arguments = list(minGSSize = 5, maxGSSize = 500, pvalue = 0.05, qvalue = 0.1),
  ref_type = "drug",
  ref = NULL,
  to_ENTREZID = TRUE
)

Arguments

`BasicData`	BasicData object.
`geneset`	Charactor vector, one of "kegg"(KEGG), "mkegg"(KEGG Module), "go"(GO-BP), and "wp"(WikiPathways); a data frame and list.
`arguments`	A list of the arguments of `clusterProfiler`, including `minGSSize`, `maxGSSize`, `pvalue`, and `qvalue`.
`ref_type`	Charactor vector, one of "drug", "herb", "compound" or "target", defaults to "drug".
`ref`	Charactor vector, reference drug, herb, compound or target, defaults to `NULL`.
`to_ENTREZID`	Logical, whether to translate to ENTREZID from SYMBOL, defaults to TRUE.

Value

A BioDescr object.

Examples

## Not run: 
data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
biodescr <- extr_biodescr(BasicData, geneset= "kegg")

## End(Not run)
## Not run: 
data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
biodescr <- extr_biodescr(BasicData, geneset= "kegg")

## End(Not run)

Class `HerbResult` This class represents the biological descriptor data.

Description

Class HerbResult This class represents the biological descriptor data.

Slots

Drug_Herb: Data frame, Drug-herb relationship.
Herb_Herb: Herb-herb association Rule Graph, it is a directed graph.

Author(s)

Yuanlong Hu

natural_connectivity

Description

Calculate the natural connectivity

Usage

natural_connectivity(graph)
natural_connectivity(graph)

Arguments

graph

A igraph object.

Value

A numeric vector.

Author(s)

Yuanlong Hu

network_char

Description

Calculate the network characters

Usage

network_char(graph, total_network = FALSE)
network_char(graph, total_network = FALSE)

Arguments

`graph`	The graph.
`total_network`	Calculate for total network or each nodes.

Value

A number vector or data frame.

Author(s)

Yuanlong Hu

network_node_ks

Description

Kolmogorov-Smirnov tests for node characters between networks

Usage

network_node_ks(graph1, graph2, replicate = 1000)
network_node_ks(graph1, graph2, replicate = 1000)

Arguments

`graph1`	A igraph object.
`graph2`	A igraph object.
`replicate`	Number vector, the number of conduct bootstrapping sampling replications.

Value

A data frame

Author(s)

Yuanlong Hu

Plot Biological descriptor

Description

Plot Biological descriptor

Usage

plot_BioDescr(
  BioDescr,
  type = "heatmap",
  cluster_k = 2,
  colors = c("#2E9FDF", "#E7B800")
)
plot_BioDescr(
  BioDescr,
  type = "heatmap",
  cluster_k = 2,
  colors = c("#2E9FDF", "#E7B800")
)

Arguments

`BioDescr`	BioDescr object.
`type`	one of "heatmap" and "clusterplot".
`cluster_k`	Number vector, number of cluster.
`colors`	vector of colors.

Value

Returns NULL, invisibly.

Plot Disease-Drug Network

Description

Plot Disease-Drug Network

Usage

plot_graph(
  graph,
  drug,
  disease,
  Isolated = TRUE,
  vis = "visNetwork",
  color = c(drug = "#cca4e3", herb = "#ff461f", compound = "#ffc773", target = "#70f3ff"),
  width = 1,
  size = 20,
  ...
)

## S4 method for signature 'BasicData'
plot_graph(
  graph,
  drug,
  disease,
  Isolated = TRUE,
  vis = "visNetwork",
  color = c(drug = "#cca4e3", herb = "#ff461f", compound = "#ffc773", target = "#70f3ff"),
  width = 1,
  size = 20,
  ...
)

## S4 method for signature 'igraph'
plot_graph(
  graph,
  drug,
  disease,
  Isolated = TRUE,
  vis = "visNetwork",
  color = c(drug = "#cca4e3", herb = "#ff461f", compound = "#ffc773", target = "#70f3ff"),
  width = 1,
  size = 20,
  ...
)

## S4 method for signature 'HerbResult'
plot_graph(
  graph,
  drug,
  disease,
  Isolated = TRUE,
  vis = "visNetwork",
  color = c(drug = "#cca4e3", herb = "#ff461f", compound = "#ffc773", target = "#70f3ff"),
  width = 1,
  size = 20,
  ...
)
plot_graph(
  graph,
  drug,
  disease,
  Isolated = TRUE,
  vis = "visNetwork",
  color = c(drug = "#cca4e3", herb = "#ff461f", compound = "#ffc773", target = "#70f3ff"),
  width = 1,
  size = 20,
  ...
)

## S4 method for signature 'BasicData'
plot_graph(
  graph,
  drug,
  disease,
  Isolated = TRUE,
  vis = "visNetwork",
  color = c(drug = "#cca4e3", herb = "#ff461f", compound = "#ffc773", target = "#70f3ff"),
  width = 1,
  size = 20,
  ...
)

## S4 method for signature 'igraph'
plot_graph(
  graph,
  drug,
  disease,
  Isolated = TRUE,
  vis = "visNetwork",
  color = c(drug = "#cca4e3", herb = "#ff461f", compound = "#ffc773", target = "#70f3ff"),
  width = 1,
  size = 20,
  ...
)

## S4 method for signature 'HerbResult'
plot_graph(
  graph,
  drug,
  disease,
  Isolated = TRUE,
  vis = "visNetwork",
  color = c(drug = "#cca4e3", herb = "#ff461f", compound = "#ffc773", target = "#70f3ff"),
  width = 1,
  size = 20,
  ...
)

Arguments

`graph`	graph.
`drug`	drug.
`disease`	disease.
`Isolated`	Whether to delect Isolated nodes.
`vis`	one of "igraph", "visNetwork" and "shiny".
`color`	Nodes Color
`width`	Edges width
`size`	Nodes size
`...`	Arguments

Value

Returns NULL, invisibly.

Author(s)

Yuanlong Hu

PrepareData

Description

Prepare input format.

Usage

PrepareData(..., from, to, diseaseID, format = "single", sep)
PrepareData(..., from, to, diseaseID, format = "single", sep)

Arguments

`...`	data frame, containing interaction information.
`from`	A charactor vector, containing "drug", "herb", "compound", or "target".
`to`	A character vector, containing "drug", "herb", "compound", or "target".
`diseaseID`	Charactor vector, diseaseID
`format`	one of "single" or "basket".
`sep`	Separator.

Value

A igraph object.

Author(s)

Yuanlong Hu

Examples

data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")

write_gmt

Description

parse gmt file to a data.frame

Usage

read_gmt(gmtfile, out_dataframe = TRUE)
read_gmt(gmtfile, out_dataframe = TRUE)

Arguments

`gmtfile`	A GMT file name or URL containing gene sets.
`out_dataframe`	TRUE or FALSE

Value

data.frame, list

Author(s)

Yuanlong Hu

score_network

Description

Calculating differences in disease network characteristics before and after removal of drug targets

Usage

score_network(BasicData, n = 1000)
score_network(BasicData, n = 1000)

Arguments

`BasicData`	A BasicData object.
`n`	Number vector, the number of times random permutation sampling, default to 1000.

Value

A list.

Author(s)

Yuanlong Hu

Examples

data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
res <- score_network(BasicData, n = 100)
data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
res <- score_network(BasicData, n = 100)

Mining herb-herb associations with Apriori

Description

Mine herb-herb association rules of prescription using the Apriori algorithm.

Usage

score_rule(BasicData, drug = NULL, support = 0.1, confidence = 0.8, lift = 1)
score_rule(BasicData, drug = NULL, support = 0.1, confidence = 0.8, lift = 1)

Arguments

`BasicData`	BasicData object.
`drug`	Charactor vector of drug names to analyze, default to `NULL`.
`support`	A numeric value for the minimal support of an item set, default to 0.1.
`confidence`	A numeric value for the minimal confidence of an item set, default to 0.8.
`lift`	A numeric value for the minimal lift of an item set, default to 1.

Value

A HerbResult object.

Author(s)

Yuanlong Hu

Examples

## Not run: 
data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
res <- score_rule(BasicData, support = 0.1,confidence = 0.8, lift = 1)

## End(Not run)
## Not run: 
data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
res <- score_rule(BasicData, support = 0.1,confidence = 0.8, lift = 1)

## End(Not run)

Calculating similarity between drug and disease

Description

Calculating drug-disease similarity based on biological descriptors

Usage

score_sim(BioDescr, method = "jaccard", n = 1000)
score_sim(BioDescr, method = "jaccard", n = 1000)

Arguments

`BioDescr`	BioDescr object.
`method`	method to compute similarity, default "jaccard". See `proxyC::simil`.
`n`	number.

Value

A list.

Author(s)

Yuanlong Hu

Examples

## Not run: 
data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
biodescr <- extr_biodescr(BasicData, geneset= "kegg")
res <- score_sim(biodescr, method="jaccard", n=1000)

## End(Not run)
## Not run: 
data(drugdemo)
drug_herb <- PrepareData(drugdemo$drug_herb, from = "drug", to="herb")
herb_compound <- PrepareData(drugdemo$herb_compound, from = "herb", to="compound")
compound_target <- PrepareData(drugdemo$compound_target, from = "compound", to="target")
disease <- PrepareData(drugdemo$disease, diseaseID = "disease",from = "target", to="target")
BasicData <- CreateBasicData(drug_herb, herb_compound, compound_target, diseasenet = disease)
biodescr <- extr_biodescr(BasicData, geneset= "kegg")
res <- score_sim(biodescr, method="jaccard", n=1000)

## End(Not run)

to_biodescr

Description

Convert BioDescr object to a list of adjacency matrix

Usage

to_biodescr(BioDescr)
to_biodescr(BioDescr)

Arguments

BioDescr

A BioDescr object.

Value

A list.

Author(s)

Yuanlong Hu

Examples

## Not run: 
  to_biodescr(BioDescr)

## End(Not run)
## Not run: 
  to_biodescr(BioDescr)

## End(Not run)

to_df

Description

Convert list to data.frame

Usage

to_df(list)
to_df(list)

Arguments

list

A list containing gene sets.

Value

A data frame.

Author(s)

Yuanlong Hu

Examples

## Not run: 
  to_df(list)

## End(Not run)
## Not run: 
  to_df(list)

## End(Not run)

to_list

Description

Create a new list from a data.frame of drug target and disease biomarker as input

Usage

to_list(dataframe, input = "single", sep = ", ")
to_list(dataframe, input = "single", sep = ", ")

Arguments

`dataframe`	a data frame of 2 column with term/drug and gene
`input`	one of the single or basket
`sep`	When 'input' is 'basket'.

Value

list

Author(s)

Yuanlong Hu

Examples

## Not run: 
  to_list(dataframe)

## End(Not run)
## Not run: 
  to_list(dataframe)

## End(Not run)

write_gmt

Description

prints data frame to a gmt file

Usage

write_gmt(geneset, gmt_file)
write_gmt(geneset, gmt_file)

Arguments

`geneset`	A data.frame of 2 column with term/drug and gene.
`gmt_file`	A character of gmt file name.

Value

gmt file

Author(s)

Yuanlong Hu

Package 'immcp'

Help Index

Class BasicData This class represents the basic input data.

Description

Slots

Author(s)

Class BioDescr This class represents the biological descriptor data.

Description

Slots

Author(s)

CreateBasicData

Description

Usage

Arguments

Value

Author(s)

Examples

CreateDisDrugNet

Description

Usage

Arguments

Value

Author(s)

Examples

diff_network_char

Description

Usage

Arguments

Value

Author(s)

Datasets Demo dataset

Description

enrich_f

Description

Usage

Arguments

Value

Author(s)

Export an xlsx file to Cytoscape

Description

Usage

Arguments

Value

Author(s)

Extract Biological descriptor

Description

Usage

Arguments

Value

Author(s)

Extract Biological descriptor

Description

Usage

Arguments

Value

Examples

Class HerbResult This class represents the biological descriptor data.

Description

Slots

Author(s)

natural_connectivity

Description

Usage

Arguments

Value

Author(s)

network_char

Description

Usage

Arguments

Value

Author(s)

network_node_ks

Description

Usage

Arguments

Value

Author(s)

Plot Biological descriptor

Description

Class `BasicData` This class represents the basic input data.

Class `BioDescr` This class represents the biological descriptor data.

Class `HerbResult` This class represents the biological descriptor data.