Package 'EnrichDO'

Title: a Global Weighted Model for Disease Ontology Enrichment Analysis
Description: To implement disease ontology (DO) enrichment analysis, this package is designed and presents a double weighted model based on the latest annotations of the human genome with DO terms, by integrating the DO graph topology on a global scale. This package exhibits high accuracy that it can identify more specific DO terms, which alleviates the over enriched problem. The package includes various statistical models and visualization schemes for discovering the associations between genes and diseases from biological big data.
Authors: Liang Cheng [aut], Haixiu Yang [aut], Hongyu Fu [cre]
Maintainer: Hongyu Fu <[email protected]>
License: MIT + file LICENSE
Version: 1.1.1
Built: 2024-11-29 03:10:05 UTC
Source: https://github.com/bioc/EnrichDO

Help Index

EnrichDO Enrichment analyses including a variety of statistical models and visualization schemes for discovering the disease-gene relationship under biological big data.

Description

To implement disease ontology (DO) enrichment analysis, this package is designed and presents a double weighted model based on the latest annotations of the human genome with DO terms, by integrating the DO graph topology on a global scale. This package exhibits high accuracy that it can identify more specific DO terms, which alleviates the over enriched problem. The package includes various statistical models and visualization schemes for discovering the associations between genes and diseases from biological big data.

Author(s)

Liang cheng, Haixiu Yang, Hongyu Fu

Maintainer: Haixiu Yang [email protected]

convDraw

Description

using the result of writeResult for convenience drawing.

Usage

convDraw(resultDO)

Arguments

resultDO

a data frame of enrichment result

Value

DataFrame

Author(s)

Haixiu Yang

Examples

#'#Draw from wrireResult output files
#Firstly, read the wrireResult output file,using the following two lines
data <- read.delim(file.path(system.file('examples', package = 'EnrichDO'), 'result.txt'))
enrich <- convDraw(resultDO = data)
#then, Use the drawing function you need
drawGraphViz(enrich=enrich)    #Tree diagram
drawPointGraph(enrich=enrich)  #Bubble diagram
drawBarGraph(enrich=enrich)    #Bar plot

doEnrich

Description

given an array of human protein-genes with NCBI ENTREZID format, this function combines topological properties of the disease ontology structure for enrichment analysis.

Usage

doEnrich(
  interestGenes,
  test = c("hypergeomTest", "fisherTest", "binomTest", "chisqTest", "logoddTest"),
  method = c("BH", "holm", "hochberg", "hommel", "bonferroni", "BY", "fdr", "none"),
  m = 1,
  maxGsize = 5000,
  minGsize = 5,
  traditional = FALSE,
  delta = 0.01,
  penalize = TRUE,
  allDOTerms = FALSE
)

Arguments

interestGenes

a vector of gene IDs.The interest gene sets should be protein-coding genes, using the ENTREZID format from NCBI.
test

One of 'fisherTest','hypergeomTest','binomTest','chisqTest' and 'logoddTest' statistical model. Default is hypergeomTest.
method

One of 'holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY','fdr' and 'none',for P value correction.
m

Set the maximum number of ancestor layers for ontology enrichment. Default is layer 1.
maxGsize

indicates that doterms with more annotation genes than maxGsize are ignored, and the P value of these doterms is set to 1.
minGsize

indicates that doterms with less annotation genes than minGsize are ignored, and the P value of these doterms is set to 1.
traditional

a logical variable, TRUE for traditional enrichment analysis, FALSE for enrichment analysis with weights. Default is FALSE.
delta

Set the threshold of nodes, if the p value of doterm is greater than delta, the nodes are not significant, and these nodes are not weighted.Default is 0.01.
penalize

Logical value, used to alleviate the impact of different magnitudes of p-values, default value is TRUE. When set to FALSE, the degree of reduction in weight for non-significant nodes is decreased.
allDOTerms

Logical value, whether to store all doterms in EnrichResult, defaults is FALSE (only significant nodes are retained).

Value

A EnrichResult instance.

Author(s)

Haixiu Yang

Examples

##Input data case
#the inputdata_demo variable stores validated protein-coding genes associated with Alzheimer's disease.
Alzheimer <- read.delim(file.path(system.file('extdata', package='EnrichDO'), 'Alzheimer_curated.csv'), header = FALSE)
inputdata_demo <- Alzheimer[,1]
##doEnrich case
#The enrichment results were obtained by using demo.data
demo.data <- c(1636,351,102,2932,3077,348,4137,54209)
demo_result <- doEnrich(interestGenes=demo.data,maxGsize = 100, minGsize=10)

All DO term annotated genes.

Description

A dataset includes 15106 genes.

Usage

dotermgenes

Format

An character array with 15106 elements:

Detailed annotation information for 4831 DO terms.

Description

A dataset includes 4831 DO terms of hierarchical information, annotated gene information, and weight information

Usage

doterms

Format

A data frame with 4813 rows and 10 variables:

DOID

the DOterm ID on enrichment

level

the hierarchy of the DOterm in the DAG graph

gene.arr

all genes related to the DOterm

weight.arr

gene weights in each node

parent.arr

the parent node of the DOterm

parent.len

the number of parent.arr

child.arr

child nodes of the DOterm

child.len

the number of child.arr

gene.len

the number of all genes related to the DOterm

DOTerm

the standard name of the DOterm

drawBarGraph

Description

The enrichment results are shown in a bar chart

Usage

drawBarGraph(EnrichResult = NULL, enrich = NULL, n = 10, delta = 1e-15)

Arguments

EnrichResult

the EnrichResult object
enrich

a data frame of enrichment result
n

number of bars
delta

the threshold of P value

Value

bar graph

Author(s)

Haixiu Yang

Examples

demo.data <- c(1636,351,102,2932,3077,348,4137,54209)
sample1 <- doEnrich(interestGenes=demo.data,maxGsize = 100, minGsize=10)
drawBarGraph(EnrichResult=sample1, n=10, delta=0.05)

drawGraphViz

Description

the enrichment results are shown in a tree diagram

Usage

drawGraphViz(
  EnrichResult = NULL,
  enrich = NULL,
  n = 10,
  labelfontsize = 14,
  numview = TRUE,
  pview = TRUE
)

Arguments

EnrichResult

the EnrichResult object
enrich

a data frame of the enrichment result
n

the number of most significant nodes
labelfontsize

the font size of nodes
numview

Displays the number of intersections between the interest set and each doterm.
pview

Displays the P value for each dotrem.

Value

tree diagram

Author(s)

Haixiu Yang

Examples

demo.data <- c(1636,351,102,2932,3077,348,4137,54209)
sample5 <- doEnrich(interestGenes=demo.data,maxGsize = 100, minGsize=10)
drawGraphViz(EnrichResult =sample5)

#The p-value and the number of intersections are not visible
drawGraphViz(EnrichResult=sample5, numview = FALSE, pview = FALSE)

drawHeatmap

Description

The top DOID_n nodes in the enrichment results showed the top gene_n genes with the highest weight sum.

Usage

drawHeatmap(
  interestGenes,
  EnrichResult = NULL,
  DOID_n = 10,
  gene_n = 50,
  fontsize_row = 10,
  readable = TRUE,
  ...
)

Arguments

interestGenes

A collection of interest genes in vector form
EnrichResult

the EnrichResult object
DOID_n

There are DOID_n nodes with the highest significance in the enrichment results.
gene_n

Among the selected DOID_n nodes, the top gene_n genes with the highest weight sum are selected to show.
fontsize_row

Set the font size of the gene tag.
readable

Logical value that controls whether the gene tag is in symbol format
...

Other parameters in the pheatmap function also apply.

Value

heat map

Author(s)

Haixiu Yang

Examples

demo.data <- c(1636,351,102,2932,3077,348,4137,54209)
sample6 <- doEnrich(interestGenes=demo.data,maxGsize = 100, minGsize=10)
drawHeatmap(interestGenes=demo.data, EnrichResult = sample6, gene_n = 10)

drawPointGraph

Description

The enrichment results are shown in a scatter plot

Usage

drawPointGraph(EnrichResult = NULL, enrich = NULL, n = 10, delta = 1e-15)

Arguments

EnrichResult

the EnrichResult object
enrich

a data frame of enrichment result.
n

number of points.
delta

the threshold of P value.

Value

scatter graph

Author(s)

Haixiu Yang

Examples

demo.data <- c(1636,351,102,2932,3077,348,4137,54209)
sample2 <- doEnrich(interestGenes=demo.data,maxGsize = 100, minGsize=10)
drawPointGraph(EnrichResult=sample2, n=10, delta=0.05)

Class 'EnrichResult' This class represents the result of enrich analysis

Description

Class 'EnrichResult' This class represents the result of enrich analysis

Slots

enrich

a data frame of enrichment result

test

Statistical test

method

Multiple test correction methods

m

the maximum number of ancestor layers for ontology enrichment

maxGsize

The maximum number of DOTerm genes in enrichment analysis

minGsize

The minimum number of DOTerm genes in enrichment analysis

traditional

Indicates whether the traditional ORA method is used

delta

The highest p-value of significance for each node

penalize

Whether to use penalty function in enrichment analysis

interestGenes

A valid interest gene set

Author(s)

Haixiu Yang

show method

Description

show method for EnrichResult instance

Usage

## S4 method for signature 'EnrichResult'
show(object)

Arguments

object

A EnrichResult instance.

Value

print info

Author(s)

Haixiu Yang

showDoTerms

Description

show DOterms

Usage

showDoTerms(doterms = doterms)

Arguments

doterms

a data frame of DOterms.

Value

text

Author(s)

Haixiu Yang

Examples

showDoTerms(doterms)

Enrich_internal

Description

Internal calculation of enrichment analysis

Usage

TermStruct(resultDO)

Arguments

resultDO

Receives the file output by the wrireResult function, which is used to visually display the enrichment results (without running the enrichment operation again).

Value

A EnrichResult instance.

Author(s)

Haixiu Yang

writeResult

Description

Output enrichment result as text

Usage

writeResult(EnrichResult = NULL, file, Q = 1, P = 1)

Arguments

EnrichResult

the EnrichResult object
file

the address and name of the output file.
Q

Output only doterm information with p.adjust values less than or equal to Q.
P

Output only doterm information with p values less than or equal to P.

Value

text

Author(s)

Haixiu Yang

Examples

demo.data <- c(1636,351,102,2932,3077,348,4137,54209)
sample4 <- doEnrich(interestGenes=demo.data,maxGsize = 100, minGsize=10)
writeResult(EnrichResult=sample4, file=file.path(tempdir(), 'result.txt'))