| edgeR_tidiers {biobroom} | R Documentation |
Tidy, augment and glance methods for turning edgeR objects into tidy data frames, where each row represents one observation and each column represents one column.
## S3 method for class 'DGEExact' tidy(x, ...) ## S3 method for class 'DGEList' tidy(x, addSamples = FALSE, ...) ## S3 method for class 'DGEList' augment(x, data = NULL, ...) ## S3 method for class 'DGEExact' glance(x, alpha = 0.05, p.adjust.method = "fdr", ...)
x |
DGEExact, DGEList object |
... |
extra arguments (not used) |
addSamples |
Merge information from samples. Default is FALSE. |
data |
merge data to augment. This is particularly useful when merging gene names or other per-gene information. Default is NULL. |
alpha |
Confidence level to test for significance |
p.adjust.method |
Method for adjusting p-values to determine significance; can be any in p.adjust.methods |
tidy defaults to tidying the counts in
the dataset:
gene |
gene ID |
sample |
sample ID |
count |
number of reads in this gene in this sample |
If addSamples = TRUE, it also merges this with the sample information present
in x$samples.
augment returns per-gene information (DGEList only)
glance returns one row with the columns (DGEExact only)
significant |
number of significant genes using desired adjustment method and confidence level |
comparison |
The pair of groups compared by edgeR, delimited by / |
if (require("edgeR")) {
library(Biobase)
data(hammer)
hammer.counts <- exprs(hammer)[, 1:4]
hammer.treatment <- phenoData(hammer)$protocol[1:4]
y <- DGEList(counts=hammer.counts,group=hammer.treatment)
y <- calcNormFactors(y)
y <- estimateCommonDisp(y)
y <- estimateTagwiseDisp(y)
et <- exactTest(y)
head(tidy(et))
head(glance(et))
}