搜索
查看: 2463|回复: 0

[software] maSigPro对有时间点的芯片或者RNA-Seq做差异分析

[复制链接]

7

主题

12

帖子

226

积分

中级会员

Rank: 3Rank: 3

积分
226
QQ
发表于 2017-5-7 19:57:28 | 显示全部楼层 |阅读模式
maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments.
QQ截图20170507193532.png
和limma类似,需要传入表达矩阵和分组矩阵
代码如下
[mw_shl_code=python,true]source("https://bioconductor.org/biocLite.R")
biocLite("maSigPro")
library(maSigPro)
data(data.abiotic)
data(edesign.abiotic)
edesign.abiotic
#Defining the regression mode
design <- make.design.matrix(edesign.abiotic, degree = 2)
design$groups.vector
#Finding significant genes
fit <- p.vector(data.abiotic, design, Q = 0.05, MT.adjust = "BH", min.obs = 20)
fit$i # returns the number of significant genes
fit$alfa # gives p-value at the Q false discovery control level
fit$SELEC # is a matrix with the significant genes and their expression values
#Finding significant differences
tstep <- T.fit(fit, step.method = "backward", alfa = 0.05)
#Obtaining lists of significant genes
sigs <- get.siggenes(tstep, rsq = 0.6, vars = "groups")
write.table(file="sig.summary.txt",sigs$summary,sep = "\t",quote = F)[/mw_shl_code]

同时还可以可视化结果
[mw_shl_code=python,true]#Graphical display
#Venn Diagrams
suma2Venn(sigs$summary[, c(2:4)])
suma2Venn(sigs$summary[, c(1:4)])[/mw_shl_code]
QQ截图20170507195057.png QQ截图20170507195109.png

[mw_shl_code=python,true]#see.genes
sigs$sig.genes$ColdvsControl$g
see.genes(sigs$sig.genes$ColdvsControl, show.fit = T, dis =design$dis, cluster.method="hclust" ,cluster.data = 1, k = 9)[/mw_shl_code]
QQ截图20170507195458.png

回复

使用道具 举报

您需要登录后才可以回帖 登录 | 立即注册

本版积分规则

QQ|手机版|小黑屋|生信技能树 ( 粤ICP备15016384号  

GMT+8, 2020-3-31 08:46 , Processed in 0.027328 second(s), 33 queries .

Powered by Discuz! X3.2

© 2001-2013 Comsenz Inc.