#the first way
install.packages("ggtree")
#一般情况下rstudio默认的源里面是找不到ggtree
#因此可以通过BiocManager下载
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ggtree")
#具体参数如下
Layer Description
geom_balance highlights the two direct descendant clades of an internal node
geom_cladelabel annotate a clade with bar and text label
geom_facet plot associated data in specific panel (facet) and align the plot with the tree
geom_hilight highlight selected clade with rectanglar or round shape
geom_inset add insets (subplots) to tree nodes
geom_label2 modified version of geom_label, with subsetting supported
geom_nodepoint annotate internal nodes with symbolic points
geom_point2 modified version of geom_point, with subsetting supported
geom_range bar layer to present uncertainty of evolutionary inference
geom_rootpoint annotate root node with symbolic point
geom_rootedge add root edge to a tree
geom_segment2 modified version of geom_segment, with subsetting supported
geom_strip annotate associated taxa with bar and (optional) text label
geom_taxalink Linking related taxa
geom_text2 modified version of geom_text, with subsetting supported
geom_tiplab layer of tip labels
geom_tippoint annotate external nodes with symbolic points
geom_tree tree structure layer, with multiple layout supported
geom_treescale tree branch scale legend
正经人谁猜啊，我直接打开UniProt直接搜索NtNRT1.2蛋白，还真搜到了。
提交名称NRT1.2，由烟草（Common tobacco）NtNRT1.2-s基因表达，定位于膜，参与硝酸盐的跨膜运输，铵态氮的系吸收。也有证据表明其可能参与磷酸根离子和寡肽的运输。
进一步，我们又发现一篇刊于《frontiers in Plant Science》2018; 9: 210的文献，看了看这篇文献的作者，我一瞬间明白了我们可爱的刘老师为啥让我们做NtNRT1.2的GUS染色(Liu et al., 2018)。
通过不同的氮处理下，间隔一段时间测定烟草根系和叶片中的NtNRT1.2的表达，可以发现NtNRT1.2对于氮具有一定的反应，特别是硝态氮，随着硝态氮的输入，NtNRT1.2在一段时间后显著表达，且这种表达会受到氮缺乏的抑制（图4），因此不难猜测NtNRT1.2应当参与了植物根系硝酸盐反应
对植物根系一天内不同时间段内的NtNRT1/2的基因表达的测定表明NtNTR1.2在6:00与8:00达到表达高峰（图5），考虑到植物在夜间需要进行活跃的碳水化合物同化，此过程需要大量的氮素提供，因此这暗示我们NtNRT1.2应当与氮的吸收和运输相关
更进一步，通过在拟南芥nrt1.1-1突变体内转入NtNRT1.1与NtNRT1.2 的基因来验证其基因功能。半定量RT-PCR证明了NtNRT1.1/1.2在拟南芥中的表达，通过拟南芥叶片和根系鲜重的测定（图6），证明了NtNRT1.1/1.2显著提高突变体生长状况，间接证明了NtNRT1.2参与植物根系硝酸盐的吸收。
参考文献
Liu, L. H., Fan, T. F., Shi, D. X., Li, C. J., He, M. J., Chen, Y. Y., . . . Sun, Y. C. (2018). Coding-Sequence Identification and Transcriptional Profiling of Nine AMTs and Four NRTs From Tobacco Revealed Their Differential Regulation by Developmental Stages, Nitrogen Nutrition, and Photoperiod. Front Plant Sci, 9, 210. doi:10.3389/fpls.2018.00210
RXSA = Cross section area
TCA = Cortex area
TSA = Stele area
RatioCtoS = Cortex:Stele ratio
RatioCtoXS = Cortex:Cross Section ratio
RatioStoXS = Stele:Cross Section ratio
AA = Aerenchyma area
percCisA = Percentage of cortex that is aerenchyma
nonAA = Total cortex area - AA
MXVA = Metaxylem vessel area
percSisMX = Percentage of stele that is metaxylem
percXSisMX = Percentage of cross section that is metaxylem
MX_mean = Mean metaxylem size
MX_median = Median metaxylem size
MX_min = Minimum metaxylem size
MX_max = Maximum metaxylem size
MX_num = Number of metaxylem vessels
CF_num = Number of cell files
CCA = Cortex cell area
percCisCC = Percentage of cortex that is cortical cells
percXSisCC = Percentage of cross section that is cortical cells
CC_mean = Mean cortical cell size
CC_median = Median cortical cell size
CC_num = Number of cortical cells
Z0andEpi_mean = Mean of all cells in zone zero and the epidermis
Z0_mean = Mean of all cells in zone zero (edge)
Z1_mean = Mean of all cells in zone one (middle)
Z2_mean = Mean of all cells in zone two (center)
Z0andEpi_median = Median of all cells in zone zero and the epidermis
Z0_median = Median of all cells in zone zero (edge)
Z1_median = Median of all cells in zone one (middle)
Z2_median = Median of all cells in zone two (center)
Z0andEpi_num = Number of all cells in zone zero and the epidermis
Z0_num = Number of all cells in zone zero (edge)
Z1_num = Number of all cells in zone one (middle)
Z2_num = Number of all cells in zone two (center)
LA_mean = Mean lacunae size
LA_median = Median lacunae size
LA_min = Minimum lacunae size
LA_max = Maximum lacunae size
LA_num = Number of lacunae
Files start counting from 0 at the epidermis
Ratings indicate image quality, 0 the worst, 10 the best
Digital Imaging of Root Traits（DIRT）是一个使用DIRT成像协议计算70多种表型特征的计算机平台，其主要思路是通过计算机处理根系图片给出表型特征，这种方法作为2014年10月的Plant Physiology的封面，其软件平台2015年11月上线PlantMethods。
具体的测定表型特征可见官方入门手册
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