{r setup, echo=FALSE}库(LearnBioconductor) stopifnot(BiocInstaller::biocVersion() == "3.1")``` ```{r style, echo = FALSE, results = 'asis'}" " #可视化马丁·摩根
[三种方法](//www.andersvercelli.com/help/course-materials/2014/summerx/Visualization-slides.pdf) - 'base' graphics - _lattice_ - ' r CRANpkg("ggplot2") ' -一个简洁的_ggplot2_示例:[幻灯片19+](//www.andersvercelli.com/help/course-materials/2014/CSAMA2014/2_Tuesday/lectures/Visualization_in_Statistical_Genomics-Carey.pdf)来自Vince Carey的演讲。交互式可视化-“r Biocpkg(“rtracklayer”)”，用于可视化基因组范围和管理UCSC基因组浏览器会话。- ' r Biocpkg('epivizr') '用于交互式显示- ' r CRANpkg("shiny") ' [web](http://shiny.rstudio.com)用于交互式应用程序report - ' r Biocpkg("ReportingTools") ' for easy report templates And…- ' r CRANpkg(“RColorBrewer”)和[网站](http://colorbrewer2.org/):帮助选择合理的配色方案。——(意外艺术)(http://accidental-art.tumblr.com/archive)——(棒图)(https://github.com/EconometricsBySimulation/R-Graphics/blob/master/Stick-Figures/draw.stick.R) # # # # #实验室Gviz / ggbio工作通过[装饰图案部分2](//www.andersvercelli.com/packages/release/bioc/vignettes/Gviz/inst/doc/Gviz.pdf)的r Biocpkg (Gviz)的包,然后是[RNASeq lab]中的“绘制基因组空间的折叠变化”(b02.1.1 1_rnaseqlab .html)。仔细阅读“r Biocpkg('ggbio')”软件包的[小插图](//www.andersvercelli.com/packages/release/bioc/vignettes/ggbio/inst/doc/ggbio.pdf)。运行以下_DESeq2_工作流到达顶层表; coerce the result to a `data.frame`. ```{r ggplot-setup, eval=FALSE} library(DESeq2) library(airway) data(airway) se = airway dds <- DESeqDataSet(se, design = ~ cell + dex) dds$dex <- relevel(dds$dex, "untrt") dds <- DESeq(dds) res <- results(dds) resdf <- as.data.frame(res) ``` A 'volcano plot' shows the relationship between P-value and log fold change. Here's a basic volcano plot using base graphics; create a volcano plot using _ggplot2_ and / or _lattice_. ```{r volcano, eval=FALSE} plot(-log10(padj) ~ log2FoldChange, resdf) ```