[英]how to have full rank matrix?
我有两个集合数据,批次1的每个样本有4个样本,每个样本有2个重复,而批次2的另一个4个样本有2个样本。
我想从这些样本中删除批处理效果,然后将不同的方法一起比较。 我已经完成以下命令,但遇到错误:
design
method batch
L4_rep1 L b1
L4_rep2 L b1
L6_L8_rep1 L b1
L6_L8_rep2 L b1
Q5_Q7_rep1 Q b1
Q5_Q7_rep2 Q b1
Q3_rep1 Q b1
Q3_rep2 Q b1
co_40d_A co_40d b2
co_40d_B co_40d b2
co_60d_A co_60d b2
co_60d_B co_60d b2
EB_A EB b2
EB_B EB b2
H9_A H9 b2
H9_B H9 b2
design$method <- factor(design$method, levels = c("L", "Q", "co_40d","co_60d", "EB", "H9"))
design$batch <- factor(design$batch, levels = c("b1", "b2"))
design.matrix <- model.matrix(~0+batch+method,design)
design.matrix
batchb1 batchb2 methodQ methodco_40d methodco_60d methodEB methodH9
L4_rep1 1 0 0 0 0 0 0
L4_rep2 1 0 0 0 0 0 0
L6_L8_rep1 1 0 0 0 0 0 0
L6_L8_rep2 1 0 0 0 0 0 0
Q5_Q7_rep1 1 0 1 0 0 0 0
Q5_Q7_rep2 1 0 1 0 0 0 0
Q3_rep1 1 0 1 0 0 0 0
Q3_rep2 1 0 1 0 0 0 0
co_40d_A 0 1 0 1 0 0 0
co_40d_B 0 1 0 1 0 0 0
co_60d_A 0 1 0 0 1 0 0
co_60d_B 0 1 0 0 1 0 0
EB_A 0 1 0 0 0 1 0
EB_B 0 1 0 0 0 1 0
H9_A 0 1 0 0 0 0 1
H9_B 0 1 0 0 0 0 1
library(edgeR)
data_filter<- count table
edgeR.dgelist = DGEList(data_filter)
edgeR.dgelist_normal = calcNormFactors(edgeR.dgelist)
CommonDisp <- estimateGLMCommonDisp(edgeR.dgelist_normal, design.matrix)
Error in glmFit.default(y, design = design, dispersion = dispersion, offset = offset, : Design matrix not of full rank. The following coefficients not estimable: methodH9
我想知道我的设计矩阵是否正确? 另外,我想将Q方法与其他方法进行比较,请您帮我做一下对比吗?
无法基于此设计创建完整等级矩阵。 如果您具有变量batch1的值和方法列之一,则可以轻松了解其他虚拟变量的值。 也不可能跨批次比较方法,例如,批次2中没有方法“ L”要与批次1进行比较。
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