前言
在DisGeNET数据库的第一篇推文；DisGeNET数据库：人类疾病相关基因研究的百科全书中，Immugent介绍了它跨越十年的发展历程。如今，作为最权威的人类疾病基因相关数据库之一，DisGeNET数据库已经发展成一个集整合资源，网页，数据分析软件为一体的综合型数据库。
此外，在另一篇推文：disgenet2r:一个R包解决人类疾病分子功能的全部研究中，Immugent大致介绍了如何在自己的PC上构建disgenet2r包的资源，本期推文开始，生信宝库将会推出系列推文，通过代码实操的方式来演示如何将disgenet2r包运用在我们的数据分析中。
代码实操
此教程的代码需要延续上一篇推文来进行。
data1 <- gene2disease( gene = 3953, vocabulary = "ENTREZ",
database = "CURATED")
class(data1)
## [1] "DataGeNET.DGN"
## attr(,"package")
## [1] "disgenet2r"
data1
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: gene-disease
## . Database: CURATED
## . Score: 0-1
## . Term: 3953
## . Results: 89
results <- extract(data1)
head( results, 3 )
## year_initial protein_class el disease_type
## 1 1998 DTO:05007599 strong disease
## 2 1966 DTO:05007599 disease
## 3 1966 DTO:05007599 disease
## disease_class_name protein_class_name gene_dpi year_final
## 1 Signaling 0.434 2019
## 2 Pathological Conditions, Signs and Symptoms; Nutritional and Metabolic Diseases Signaling 0.434 2021
## 3 Nutritional and Metabolic Diseases; Endocrine System Diseases Signaling 0.434 2020
## score disease_class disease_semantic_type ei gene_symbol source gene_dsi disease_name geneid
## 1 0.82 Disease or Syndrome 1.000 LEPR CURATED 0.99475 LEPTIN RECEPTOR DEFICIENCY 3953
## 2 0.80 C23;C18 Disease or Syndrome 0.937 LEPR CURATED 0.99475 Obesity 3953
## 3 0.60 C18;C19 Disease or Syndrome 0.975 LEPR CURATED 0.99475 Diabetes Mellitus, Non-Insulin-Dependent 3953
## gene_pli uniprotid diseaseid
## 1 0.84 P48357 C3554225
## 2 0.84 P48357 C0028754
## 3 0.84 P48357 C0011860
plot( data1,
class = "Network",
prop = 20)
image.png
plot( data1, class = "DiseaseClass", prop = 3)
image.png
data1 <- gene2evidence( gene = "LEPR",
vocabulary = "HGNC",
disease ="C3554225",
database = "ALL",
score =c(0.3,1))
data1
results <- extract(data1)
探索多个基因的疾病相关性。
myListOfGenes <- c( "KCNE1", "KCNE2", "KCNH1", "KCNH2", "KCNG1")
data2 <- gene2disease(
gene = myListOfGenes,
score =c(0.2, 1),
verbose = TRUE)
plot( data2,
class = "Network",
prop = 10)
image.png
plot( data2, class ="Heatmap", limit = 100, nchars = 50 )
image.png
plot( data2, class="DiseaseClass", nchars=60)
image.png
以疾病为研究对象的使用方式。
data3 <- disease2gene( disease = "C0036341",
database = "CURATED",
score = c( 0.4,1 ) )
data4 <- disease2gene( disease = "181500", vocabulary = "OMIM",
database = "CURATED",
score = c(0.4,1 ) )
plot( data3, class="ProteinClass")
image.png
探索与疾病相关的证据。。。
data3 <- disease2evidence( disease = "C0036341",
type = "GDA",
database = "CURATED",
score = c( 0.4,1 ) )
data3 <- disease2evidence( disease = "C0036341",
gene = c("DRD2", "DRD3"),
type = "GDA",
database = "CURATED",
score = c( 0.4,1 ) )
results <- extract(data3)
image.png
同时搜索多种疾病。。。
diseasesOfInterest <- c("C0036341", "C0002395", "C0030567","C0005586")
data5 <- disease2gene(
disease = diseasesOfInterest,
database = "CURATED",
score =c(0.4,1),
verbose = TRUE )
plot( data5,
class="ProteinClass" )
image.png
说在最后
从上述流程我们可以看出disgenet2r包，高度集和了DisGeNET数据库的资源，而且和多种分析流程进行无缝衔接，这样使用起来就很方便。并且更加难能可贵的是，disgenet2r包输出的图都很高达上，可以直接用于我们发表的文章中。当然，disgenet2r包的功能远不止如此，Immmugent将会在下期介绍如何将突变位点和疾病相关联，敬请期待！
好啦，本期分享到这里就结束了，我们下期再会~~
