## ----pubmed1, fig.cap=" PubMed中的精神疾病。1955 - 2016年查询**精神疾病[标题/摘要]**."，echo=FALSE, fig.wide = TRUE---- library(ggplot2)数据。文件<- system。文件(paste0("extdata"， . platform $file. txt)9月,“psychiatricDisordersPubmed.csv”),包=“psygenet2r”)pmid < - read.delim (data.file头= TRUE, 9 =”、“)pmid < - pmid [pmid $ > < & pmid 2017年1950年,]pmid美元年< -因子(pmid年美元)标签< - as.integer (seq(1950、2016 = 5)p < - ggplot (pmid, aes (x =, y = count)) + geom_bar (stat =“身份”,填补=“灰色”)+实验室(title =“精神疾病在PubMed的出版物数量”,x =“年”,y = pmids“#”)+ theme_classic () + scale_x_discrete(休息=标签,标签=as.character(标签))+主题(情节)。Margin = grid::unit (x = c (5,15,5,15)， units = "mm")，坐标轴。行= element_line(大小= 0.7,颜色=“黑色”),文本= element_text(大小= 14),axis.text.x = element_text(角= 45,大小= 11,hjust = 1)) p # #——bioC eval = FALSE --------------------------------------------------------- # 如果(!requireNamespace(“BiocManager”,悄悄地= TRUE)) # install.packages (BiocManager) # BiocManager::安装(“psyGeNET2R " ) ## ---- load_library,消息= FALSE --------------------------------------------- 库(psygenet2r ) ## ---- 基因 -------------------------------------------------------------------- genesOfInterest < - c(“ADCY2”、“AKAP13”、“ANK3”、“ANKS1A”、“ATP6V1G3”、“ATXN1”、“C11orf80”、“C15orf53”、“CACNA1C”、“CACNA1D”、“CACNB3”、“CROT”、“DLG2”、“DNAJB4”、“DUSP22”、“FAM155A”、“FLJ16124”,“FSTL5”、“GATA5”、“GNA14”、“GPR81”,“HHAT”、“IFI44”,“ITIH3”、“KDM5B”,“KIF1A”、“LOC150197”,“MAD1L1”、“MAPK10”,“MCM9”、“MSI2”,“NFIX”、“神经生长因子”、“NPAS3”、“ODZ4”,“PAPOLG”、“PAX1”,“叫做PBRM1”、“PTPRE”,“PTPRT”、“RASIP1”,“RIMBP2”、“RXRG”,“SGCG”、“SH3PXD2A”,“SIPA1L2”、“SNX8”,“SPERT”、“STK39”,“SYNE1”、“THSD7A”,“TNR”、“TRANK1”,“TRIM9”、“UBE2E3”,“UBR1”、“ZMIZ1”、“ZNF274 ") ## ---- search_multiple ---------------------------------------------------------- m1 < - psygenetGene (= genesOfInterest基因,database =" ALL"， verbose =FALSE, warnings =FALSE) m1 ## ----基因-疾病，fig.height=8, fig.width=8, fig.cap="基因-疾病关联网络"，fig.wide = TRUE---- plot(m1) ## ----基因-psy, fig.cap="根据精神病学类别的关联类型barplot "， fig.wide = TRUE---- geneAttrPlot(m1, type ="证据索引")## ----黑豹，fig.cap="感兴趣基因的黑豹类分析。"，message=FALSE, warning=FALSE, fig.wide = TRUE---- pantherGraphic(genesOfInterest， "ALL") ## ----gene-disease-2, fig.cap="Gene-Disease Association Heatmap", fig.wide = TRUE---- plot( m1, type="GDA heatmap") ## ----pubmed2, fig.cap="Publications that report each gene association with bipolar disorder", fig.wide = TRUE---- plot( m1, name="bipolar disorder", type="publications") ## ----sentences1_query--------------------------------------------------------- m2 <- psygenetGeneSentences( geneList = genesOfInterest, database = "ALL" ) m2 ## ----sentences2_extraction, warnings=FALSE------------------------------------ sentences <- extractSentences( m2, disorder = "bipolar disorder" ) head(sentences$PUBMED_ID) ## ----jaccard_1, warning=FALSE------------------------------------------------- xx <- jaccardEstimation( genesOfInterest, "bipolar disorder", database = "ALL", nboot = 500 ) xx ## ----jaccard_2---------------------------------------------------------------- extract( xx ) ## ----jaccard_3, warning=FALSE------------------------------------------------- xx <- jaccardEstimation( genesOfInterest, database = "ALL", nboot = 500 ) ## ----jacc, fig.cap="Bar-plot where the Jaccard Index of each comparison between the list of genes of interest and PsyGeNET's diseases is shown.", fig.wide = TRUE---- plot( xx ) ## ----bpGenes, fig.cap="Barplot: Genes associated to each of the psychiatric disorders", fig.wide = TRUE---- geneAttrPlot( m1, type = "disease category" ) ## ----bpDis, fig.cap="Barplot: CUIs and psychiatric categories associated to each gene", fig.wide = TRUE---- geneAttrPlot( m1, type = "gene" )