S4 class to store data from differentially expression analysis. It should be compatible with different package and stores the information in a way the methods will work with all of them.
DEGSet(resList, default) DEGSet(resList, default) DEGSetFromEdgeR(object, ...) DEGSetFromDESeq2(object, ...) # S4 method for TopTags DEGSetFromEdgeR(object, default = "shrunken", extras = NULL) # S4 method for DESeqResults DEGSetFromDESeq2(object, default = "shrunken", extras = NULL)
resList | List with results as elements containing log2FoldChange, pvalues and padj as column. Rownames should be feature names. Elements should have names. |
---|---|
default | The name of the element to use by default. |
object | Different objects to be transformed to DEGSet. |
... | Optional parameters of the generic. |
extras | List of extra tables related to the same comparison. |
For now supporting only DESeq2::results()
output.
Use constructor degComps()
to create the object.
The list will contain one element for each comparison done. Each element has the following structure:
DEG table
Optional table with shrunk Fold Change when it has been done.
To access the raw table use deg(dgs, "raw")
, to access the
shrunken table use deg(dgs, "shrunken")
or just deg(dgs)
.
library(DESeq2) dds <- makeExampleDESeqDataSet(betaSD = 1) colData(dds)[["treatment"]] <- sample(colData(dds)[["condition"]], 12) design(dds) <- ~ condition + treatment dds <- DESeq(dds)#>#>#>#>#>#>#>#>#>#> log2 fold change (MAP): condition B vs A #> Wald test p-value: condition B vs A #> DataFrame with 1000 rows and 6 columns #> baseMean log2FoldChange lfcSE stat pvalue padj #> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> #> gene928 132.31 3.468 0.3472 9.489 2.324e-21 2.138e-18 #> gene952 70.21 3.625 0.3834 8.875 6.976e-19 3.209e-16 #> gene644 455.54 2.266 0.2618 8.634 5.940e-18 1.822e-15 #> gene515 372.73 2.804 0.3344 8.348 6.967e-17 1.602e-14 #> gene368 37.82 -2.763 0.3557 -7.532 4.992e-14 9.185e-12 #> ... ... ... ... ... ... ... #> gene906 1.3820 -0.35406 0.5346 -1.09121 0.2752 NA #> gene943 0.0000 NA NA NA NA NA #> gene956 0.6292 -0.10818 0.3952 -0.15208 0.8791 NA #> gene963 0.4705 0.09311 0.3518 -0.01693 0.9865 NA #> gene995 0.1578 -0.11622 0.3514 -0.41438 0.6786 NA#> # A tibble: 1,000 x 7 #> gene baseMean log2FoldChange lfcSE stat pvalue padj #> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 gene928 132 3.47 0.347 9.49 2.32e⁻²¹ 2.14e⁻¹⁸ #> 2 gene952 70.2 3.63 0.383 8.88 6.98e⁻¹⁹ 3.21e⁻¹⁶ #> 3 gene644 456 2.27 0.262 8.63 5.94e⁻¹⁸ 1.82e⁻¹⁵ #> 4 gene515 373 2.80 0.334 8.35 6.97e⁻¹⁷ 1.60e⁻¹⁴ #> 5 gene368 37.8 -2.76 0.356 -7.53 4.99e⁻¹⁴ 9.19e⁻¹² #> 6 gene409 68.1 -2.05 0.283 -7.07 1.56e⁻¹² 2.39e⁻¹⁰ #> 7 gene130 404 1.81 0.258 6.95 3.76e⁻¹² 4.94e⁻¹⁰ #> 8 gene948 898 2.15 0.300 6.89 5.61e⁻¹² 6.45e⁻¹⁰ #> 9 gene60 115 -1.97 0.280 -6.83 8.74e⁻¹² 8.93e⁻¹⁰ #> 10 gene605 133 1.80 0.285 6.13 8.97e⁻¹⁰ 8.25e⁻ ⁸ #> # ... with 990 more rows