limma中的ebayes使用表达式矩阵答案

【问题标题】：ebayes in limma using expression matrixlimma中的ebayes使用表达式矩阵
【发布时间】：2016-11-08 06:55:06
【问题描述】：

我以前从未使用过数组数据，我有一些基本问题我的设置。我已经对具有两个重复的时间序列中的数据进行了标准化。

我的样本：

colnames(hela.bc)
 [1] "I.0h"   "I.1h"   "I.2h"   "I.3h"   "I.6h"   "I.9h"   "I.12h"
"I.15h"  "I.18h"  "I.21h"  "I.24h"  "II.0h"
[13] "II.1h"  "II.2h"  "II.3h"  "II.6h"  "II.9h"  "II.12h" "II.15h"
"II.18h" "II.21h" "II.24h"

我想将“I.0h”和“II.0h”与其他所有时间进行比较点。（I=复制 1，II=复制 2）

我设置了以下，但我想知道它是否正确：

design <- c(2,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1)

fit <-lmFit(hela.bc, design)

fit2 <- eBayes(fit)

topTableF(fit2, adjust="BH")*

我的数据：

dput(head(hela.bc))
structure(c(0.422727272727273, 0.0145454545454545, 0.115909090909091,
0.303636363636364, -0.0622727272727273, 0.415, 0.222727272727273,
0.0545454545454545, 0.0859090909090909, 0.163636363636364,
-0.0122727272727273,
0.205, 0.232727272727273, 0.154545454545455, 0.195909090909091,
0.193636363636364, 0.0577272727272727, 0.235, 0.0527272727272727,
0.144545454545455, 0.0959090909090909, -0.126363636363636,
0.0377272727272727,
-0.035, 0.0627272727272727, 0.104545454545455, 0.0759090909090909,
-0.116363636363636, -0.0422727272727273, -0.035, 0.0827272727272727,
0.154545454545455, 0.125909090909091, 0.00363636363636364,
0.0377272727272727,
-0.045, 0.102727272727273, 0.124545454545455, 0.115909090909091,
0.0836363636363636, -0.0322727272727273, 0.015, 0.0627272727272727,
0.114545454545455, 0.0259090909090909, -0.0563636363636364,
-0.202272727272727,
-0.065, 0.102727272727273, 0.164545454545455, 0.155909090909091,
0.0336363636363636, 0.0777272727272727, 0.085, 0.322727272727273,
0.214545454545455, 0.165909090909091, 0.213636363636364,
-0.00227272727272728,
0.155, 0.272727272727273, 0.134545454545455, 0.105909090909091,
0.163636363636364, -0.0322727272727273, 0.225, 0.177272727272727,
0.115454545454545, 0.114090909090909, 0.186363636363636,
-0.127727272727273,
0.245, 0.177272727272727, 0.0954545454545455, 0.0940909090909091,
0.0963636363636364, -0.0977272727272727, 0.045, 0.0772727272727273,
0.0454545454545455, 0.0640909090909091, -0.0536363636363636,
-0.117727272727273, 0.125, 0.157272727272727, 0.175454545454545,
0.154090909090909, 0.0563636363636364, 0.0122727272727273, 0.095,
0.267272727272727, 0.0954545454545455, 0.0640909090909091,
0.126363636363636,
0.00227272727272728, 0.125, 0.217272727272727, 0.175454545454545,
0.124090909090909, 0.0263636363636364, 0.0622727272727273, 0.115,
0.237272727272727, 0.0954545454545455, 0.144090909090909,
0.146363636363636,
0.00227272727272728, 0.145, 0.127272727272727, 0.195454545454545,
0.124090909090909, 0.106363636363636, -0.0277272727272727, 0.165,
0.137272727272727, 0.0854545454545455, 0.0940909090909091,
0.0563636363636364,
-0.00772727272727272, 0.075, 0.167272727272727, 0.155454545454545,
0.154090909090909, 0.0763636363636364, 0.0722727272727273, 0.035,
0.197272727272727, 0.145454545454545, 0.134090909090909,
0.0363636363636364,
0.0522727272727273, -0.015), .Dim = c(6L, 22L), .Dimnames = list(
    c("hsa-let-7a", "hsa-let-7b", "hsa-let-7c", "hsa-let-7d",
    "hsa-let-7e", "hsa-let-7f"), c("I.0h", "I.1h", "I.2h", "I.3h",
    "I.6h", "I.9h", "I.12h", "I.15h", "I.18h", "I.21h", "I.24h",
    "II.0h", "II.1h", "II.2h", "II.3h", "II.6h", "II.9h", "II.12h",
    "II.15h", "II.18h", "II.21h", "II.24h")))

【问题讨论】：

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已经做了，没反应。

标签： r bioinformatics bioconductor

【解决方案1】：

我认为您的设计矩阵不正确。您只是将一个向量传递给design 参数，该参数应该是matrix。我认为以下是您想要的：

fac <- factor(c(2,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1))
fit <- lmFit(hela.bc, design = model.matrix(~fac))
fit2 <- eBayes(fit)
topTableF(fit2, adjust = "BH")
#            X.Intercept.      fac2 AveExpr     F  P.Value adj.P.Val
# hsa-let-7b       0.1315 -6.65e-02  0.1255 69.34 5.48e-11  1.74e-10
# hsa-let-7c       0.1150  1.18e-16  0.1150 68.98 5.79e-11  1.74e-10
# hsa-let-7a       0.1640  1.36e-01  0.1764 52.37 1.03e-09  2.06e-09
# hsa-let-7f       0.0825  2.47e-01  0.1050 22.55 2.40e-06  3.60e-06
# hsa-let-7d       0.0615  1.83e-01  0.0782 12.07 2.10e-04  2.52e-04
# hsa-let-7e      -0.0080 -8.70e-02 -0.0159  2.12 1.41e-01  1.41e-01

【讨论】：