# #——包括= FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - knitr:: opts_chunk设置美元(崩溃= TRUE,评论= " # >”)# # - - - - -设置,消息= F = F的警告,结果=“隐藏”- - - - - - - - - - - - - - - - - - - - - - - - - - - - -要求(GenomicOZone) #需要(GEOquery)要求(readxl) # #——消息= F = = F的警告,结果“隐藏”- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #从GSE76167补充文件获取数据矩阵#看不见(getGEOSuppFiles (“GSE76167”)) #数据< - read_excel (“。/ / GSE76167_GeneFPKM_AllSamples.xlsx”)文件< -系统。文件(“extdata”、“GSE76167_GeneFPKM_AllSamples。xlsx”,包= " GenomicOZone”, mustWork = TRUE)数据< - read_excel输入数据(文件)#调整data.info < -数据[1:5]数据< -数据(,- c(1:5)) < -数据(,substr (colnames(数据)、1,4)= = " FPKM "]数据< - data.matrix(数据(c (1、5、6、3、4、2)]) colnames(数据)< - c(粘贴(代表(WT, 3),“_”, c(1、2、3), 9 = " "),粘贴(代表(“倪”,3),“_”,c (1、2、3), 9 = " ")) rownames(数据)< - data.info tracking_id #美元获得基因数据。基因< - data.info美元gene_short_name data.genes[数据。基因= =“-”)< - data.info tracking_id美元(data.genes = =“-”) #创建colData colData < - data.frame (Sample_name = colnames(数据),条件=因素(代表(c (“WT”、“北爱”),每个= 3),水平= c (“WT”、“北爱”)))#创建设计设计< - ~ #构成了rowData。创造条件< -农庄模式”(。[^ \ \:]*)\ \:([0 - 9]+)\ \([0 - 9]+)”匹配< - regexec(模式,as.character (data.info轨迹美元))值< - regmatches (as.character (data.info轨迹美元),匹配)data.gene。作< - data.frame(空空的=。字符(酸式焦磷酸钠(值,函数(x) {x[[2]]})),开始=。数字(酸式焦磷酸钠(值,函数(x) {x[[3]]})),结束=。数字(酸式焦磷酸钠(值,函数(x) {x [[4]]}))) rownames (data.gene.coor) < - as.character构成了rowData。(data.info tracking_id美元)农庄< -农庄(seqnames = data.gene。作杆使用美元,IRanges (= data.gene开始。作开始,美元结束= data.gene.coor结束美元),Gene.name = data.genes)名称(rowData.GRanges) < - data.info美元tracking_id空空的。大小< - 4646332名(chr.size) < -“NC_007779 seqlevels (rowData.GRanges) < -名称(chr.size) seqlengths (rowData.GRanges) < -空空的。大小# # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #还创建一个输入对象检查数据格式、一致性和完整性沙丘状积砂。ds < - GOZDataSet (data =数据colData = colData设计=设计、rowData。农庄= rowData.GRanges) #运行优秀区分析沙丘状积砂。ds < - GenomicOZone (GOZ.ds) # # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #提取基因/区农庄组织对象。农庄< - extract_genes (GOZ.ds)头(Gene.GRanges)区。农庄< - extract_zones (GOZ.ds)头(Zone.GRanges) # min.effect。size = 0.36 is chosen from the # minumum of top 5% effect size values OZone.GRanges <- extract_outstanding_zones( GOZ.ds, alpha = 0.05, min.effect.size = 0.36) head(OZone.GRanges) Zone.exp.mat <- extract_zone_expression(GOZ.ds) head(Zone.exp.mat) ## ---- out.width = "100%", out.height = "100%", fig.align="center"------------- # Genome-wide overview plot_genome(GOZ.ds, plot.file = "E_coli_genome.pdf", plot.width = 15, plot.height = 4, alpha = 0.05, min.effect.size = 0.36) knitr::include_graphics("E_coli_genome.pdf") ## ---- out.width = "100%", out.height = "100%", fig.align="center"------------- # Within-chromosome heatmap plot_chromosomes(GOZ.ds, plot.file = "E_coli_chromosome.pdf", plot.width = 20, plot.height = 4, alpha = 0.05, min.effect.size = 0.36) knitr::include_graphics("E_coli_chromosome.pdf") ## ---- out.width = "50%", out.height = "100%", fig.align="center"-------------- # Within-zone expression plot_zones(GOZ.ds, plot.file = "E_coli_zone.pdf", plot.all.zones = FALSE, alpha = 0.05, min.effect.size = 0.36) knitr::include_graphics("E_coli_zone.pdf")