DataFrame with 12 rows and 2 columns
condition genotype
<factor> <factor>
sample1 A I
sample2 A I
sample3 A I
sample4 A II
sample5 A II
... ... ...
sample8 B I
sample9 B I
sample10 B II
sample11 B II
sample12 B II
组A vs. 组B。
dds <- makeExampleDESeqDataSet(n = 100, m = 12)
dds$genotype <- factor(rep(rep(c('I', 'II'), each=3), 2))
## condition: A vs. B
design(dds) <- ~ condition
ddres <- DESeq(dds)
res <- results(ddres, contrast = c('condition', 'B', 'A'))
## genotype I vs. II
design(dds) <- ~ genotype
ddres <- DESeq(dds)
res <- results(ddres, contrast = c('genotype', 'I', 'II'))
## A vs. B at genotype II
dds$group <- factor(paste0(dds$genotype, dds$condition))
design(dds) <- ~ group
ddres <- DESeq(dds)
results(ddres, contrast = c('group', 'IIB', 'IIA'))
dds <- makeExampleDESeqDataSet(n = 100, m = 12)
dds$genotype <- factor(rep(rep(c('I', 'II'), each=3), 2))
design(dds) <- ~ genotype + condition + genotype:condition
ddres <- DESeq(dds)
## A vs. B at genotype I
res <- results(ddres, contrast = c('condition', 'B', 'A'))
## A vs. B at genotype II
res <- results(ddres, list(c('condition_B_vs_A', 'genotypeII.conditionB')))
## condition effect *different* across genotypes
res <- results(ddres, name = 'genotypeII.conditionB')
其中,第二例子中的A vs. B at genotype II
与第一个的区别是,考虑了交叉项的影响。如果只是为了两两比对,可以考虑使用第一个例子的处理方法。
## 查看设计
colData(dds)
## 查看对比
resultsName(dds)
2019年04月15日