Momocs：用R進行表型變化分析

Momocs是一個

使用R進行表型變化

的軟體包，旨在從形狀中提取定量變數。下面就來看看Momocs是如何操作的：

操作步驟

首先，把要處理的圖片準備好

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將影象顏色降為8位

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然後調整threshold

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拖動使得紅色與要測量的形狀符合

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形狀提取好了

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加入Erode

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加Dilate

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加入Gaussian Blur

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設定sigma值

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轉換好了

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#儲存為JPG格式

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#再儲存為text image格式

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install.packages("devtools")library(devtools)devtools::install_github("MomX/Momocs")library(Momocs)#獲取檔案列表lf <- list.files(full.names=TRUE, pattern = "jpg")#讀取jpg檔案coo <- import_jpg(lf)

install.packages("devtools")

library(devtools)devtools::install_github("MomX/Momocs")library(Momocs)#獲取檔案列表lf <- list.files(full.names=TRUE, pattern = "jpg")#讀取jpg檔案coo <- import_jpg(lf)

#編輯標籤

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#匯入標籤

label<-read.table("label.txt",head=T)

#把影象與標籤合併

cooo<-Out(coo, fac = label, ldk = list())

#只畫第一張圖

coo[1] %>% paper %>% draw_outline

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#畫所有的圖

coo %>% paper %>% draw_curve

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#改為透明背景

coo %>% paper_chess %>% draw_outline -> x

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#載入不同背景

apropos("paper")

#加顏色

paper(cooo) %>%draw_outlines(factor(rep(1:2, 1)), bor=col_qual) %>%draw_centroid(~type, pch=c(1, 3))

paper(cooo) %>%

draw_outlines(factor(rep(1:2, 1)), bor=col_qual) %>%

draw_centroid(~type, pch=c(1, 3))

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#畫出單個形狀

shp <- coo[1]coo_plot(shp)

shp <- coo[1]

coo_plot(shp)

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#填充顏色

coo_plot(shp, col="grey80", border=NA, centroid=FALSE, main="Meow")

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#描點

coo_plot(coo_sample(shp, 32), points=TRUE, pch=20, main="64-pts Meow")

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#平滑

shp %>% coo_smooth(5) %>% coo_sample(64) %>% coo_scale() %>% coo_plot()

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#座標軸轉換

cooo %>%coo_center %>% coo_scale %>%coo_alignxax() %>% coo_slidedirection("up") %T>%print() %>% stack()

cooo %>%

coo_center %>% coo_scale %>%

coo_alignxax() %>% coo_slidedirection("up") %T>%

print() %>% stack()

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#新增顏色

cooo %>% paper_grid %>%draw_outline(~type, bor=col_qual) %>%draw_axes %>% draw_centroid %>% draw_firstpoint

cooo %>% paper_grid %>%

draw_outline(~type, bor=col_qual) %>%

draw_axes %>% draw_centroid %>% draw_firstpoint

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#填充顏色

data(cooo)cooopanel(cooo, fac="type", names=TRUE)

data(cooo)

cooo

panel(cooo, fac="type", names=TRUE)

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#橢圓分析

coo_oscillo(cooo[1], "efourier")

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#托勒密分析

Ptolemy(cooo[1])

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#直方圖

bot.f <- efourier(cooo, nb.h=10)bot.fhist(bot.f, drop=0)

bot.f <- efourier(cooo, nb.h=10)

bot.f

hist(bot.f, drop=0)

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#箱線圖

boxplot(bot.f, drop=1)

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#主成分分析

bot.p <- PCA(bot.f)class(bot.p) # a PCA object, let's plot itplot(bot.p)

bot.p <- PCA(bot.f)

class(bot.p) # a PCA object, let's plot it

plot(bot.p)

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#填充顏色

panel(cooo, fac="type", names="type")

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#主成分分析

bot.p <- PCA(bot.f)plot(bot.p)plot(bot.p, 1, chull=TRUE, pos.shp = "full_axes", abbreviate.labelsgroups = TRUE, points=FALSE, labelspoints = TRUE)

bot.p <- PCA(bot.f)

plot(bot.p)

plot(bot.p, 1, chull=TRUE, pos.shp = "full_axes", abbreviate.labelsgroups = TRUE, points=FALSE, labelspoints = TRUE)

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#主成分分析比重

scree(bot.p)scree_plot(bot.p)

scree(bot.p)

scree_plot(bot.p)

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boxplot(bot.p, 1)

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PCcontrib(bot.p)