# #——包括= FALSE --------------------------------------------------------- knitr: opts_chunk美元集(崩溃= TRUE,发表评论 = "#>" ) ## ---- 设置、消息= F = F的警告,结果= "隐藏 "---------------------------- 需要(GenomicOZone) #需要(GEOquery)要求(readxl ) ## ---- 消息= F = F的警告,results = "hide"---------------------------------- #从GSE76167补充文件中获取数据矩阵# invisible(getGEOSuppFiles("GSE76167")) # data <- read_excel(".//GSE76167_GeneFPKM_AllSamples.xlsx")文件<- system. txt。文件(“extdata”、“GSE76167_GeneFPKM_AllSamples.xlsx”、包=“GenomicOZone mustWork = TRUE) < - read_excel数据(文件)#调整输入数据data.info[1:5]数据< < -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$gene_short_name data. Genes[数据。genes == "-"] <- data.info$tracking_id[data.genes == "-"] #创建colData colData <- data.frame(Sample_name = colnames(data)， Condition = factor(rep(c("WT"， "Ni")， each = 3)， levels = c("WT"， "Ni")))GRanges pattern <- "(.[^\\:]*)\\:([0-9]+)\\-([0-9]+)" matched <- regexec(pattern, as.character(data.info$locus)) values <- regmatches(as.character(data.info$locus)， matched) data.gene.coor <- data.frame(chr = as. info$locus))字符(sapply(values, function(x){x[[2]]}))，开始= as。数值(sapply(values, function(x){x[[3]]}))，结束= as。numeric(sapply(values, function(x){x[[4]]}))) rownames(data.gene.coor) <- as.character(data.info$tracking_id) rowData. coorGRanges <- GRanges(seqnames = data.gene.)coor$chr, IRanges(start = data.gene。coor$start, end = data.gene.coor$end)， Gene.name = data.genes) names(rowData.GRanges) <- data.info$tracking_id chr. name <-size <- 4646332 names(cr .size) <- "NC_007779" seqlevels(rowData.GRanges) <- names(cr .size) seqlength(rowData.GRanges) <- chr.size大小 ## ----------------------------------------------------------------------------- # 创建一个输入对象也检查数据格式、一致性和完整性沙丘状积砂。- GOZDataSet(data = data, colData = colData, design = design, rowData. ds <- GOZDataSet(data = data, colData = colData, design = design, rowData.)#运行未完成的分区分析GOZ. rowData.GRanges)ds < - GenomicOZone (GOZ.ds ) ## ----------------------------------------------------------------------------- # 提取基因/区农庄组织对象。GRanges <- extract_genes(GOZ.ds) head(Gene.GRanges) Zone。GRanges <- extract_zones(GOZ.ds) head(Zone.GRanges) # min.effect.size = 0.36从臭氧的前5%的最小值中选择。grange <- 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")