The iSEELimmaResults class is used to provide a common interface to differential expression results produced by the limma package.
It provides methods to access common differential expression statistics (e.g., log fold-change, p-value, log2 average abundance).
Details
This class inherits all its slots directly from its parent class DataFrame.
Constructor
iSEELimmaResults(data, row.names = rownames(data)) creates an instance of a iSEELimmaResults class, with:
dataA
data.frameproduced bylimma::topTable().row.namesThe character vector of rownames for the SummarizedExperiment object in which the object is to be embedded. Must be a superset of
rownames(data).
Supported methods
embedContrastResults(x, se, name, class = "limma", ...)embedsxinseunder the identifiername. SeeembedContrastResults()for more details.pValue(x)returns the vector of raw p-values.log2FoldChange(x)returns the vector of log2-fold-change values.averageLog2(x)returns the vector of average log2-expression values.
Examples
library(limma)
#>
#> Attaching package: ‘limma’
#> The following object is masked from ‘package:DESeq2’:
#>
#> plotMA
#> The following object is masked from ‘package:BiocGenerics’:
#>
#> plotMA
library(SummarizedExperiment)
##
# From limma::lmFit() ----
##
sd <- 0.3 * sqrt(4 / rchisq(100, df = 4))
y <- matrix(rnorm(100 * 6, sd = sd), 100, 6)
rownames(y) <- paste("Gene", 1:100)
y[1:2, 4:6] <- y[1:2, 4:6] + 2
design <- cbind(Grp1 = 1, Grp2vs1 = c(0, 0, 0, 1, 1, 1))
fit <- lmFit(y, design)
fit <- eBayes(fit)
tt <- topTable(fit, coef = 2)
head(tt)
#> logFC AveExpr t P.Value adj.P.Val B
#> Gene 2 2.2640913 0.92510941 11.350832 1.402741e-05 0.001402741 3.852453
#> Gene 78 -2.2011871 -0.49464729 -4.289664 4.117023e-03 0.205851148 -2.336193
#> Gene 1 1.9110762 1.35369919 3.728917 8.166075e-03 0.272202489 -3.080341
#> Gene 61 -0.6188866 -0.03854689 -3.322904 1.381124e-02 0.345281103 -3.645980
#> Gene 74 -0.5626678 -0.14647983 -2.617643 3.632140e-02 0.695593282 -4.667721
#> Gene 22 -0.4379104 -0.22422247 -2.420919 4.803481e-02 0.695593282 -4.956539
##
# iSEELimmaResults ----
##
# Simulate the original SummarizedExperiment object
se <- SummarizedExperiment(assays = list(counts = y))
# Embed the Limma-Voom results in the SummarizedExperiment object
se <- embedContrastResults(tt, se, name = "Limma-Voom", class = "limma")
##
# Access ----
##
contrastResultsNames(se)
#> [1] "Limma-Voom"
contrastResults(se)
#> DataFrame with 100 rows and 1 column
#> Limma-Voom
#> <iSEELimmaResults>
#> Gene 1 <iSEELimmaResults>
#> Gene 2 <iSEELimmaResults>
#> Gene 3 <iSEELimmaResults>
#> Gene 4 <iSEELimmaResults>
#> Gene 5 <iSEELimmaResults>
#> ... ...
#> Gene 96 <iSEELimmaResults>
#> Gene 97 <iSEELimmaResults>
#> Gene 98 <iSEELimmaResults>
#> Gene 99 <iSEELimmaResults>
#> Gene 100 <iSEELimmaResults>
contrastResults(se, "Limma-Voom")
#> iSEELimmaResults with 100 rows and 6 columns
#> logFC AveExpr t P.Value adj.P.Val B
#> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
#> Gene 1 1.91108 1.353699 3.72892 8.16607e-03 0.27220249 -3.08034
#> Gene 2 2.26409 0.925109 11.35083 1.40274e-05 0.00140274 3.85245
#> Gene 3 NA NA NA NA NA NA
#> Gene 4 NA NA NA NA NA NA
#> Gene 5 NA NA NA NA NA NA
#> ... ... ... ... ... ... ...
#> Gene 96 NA NA NA NA NA NA
#> Gene 97 NA NA NA NA NA NA
#> Gene 98 NA NA NA NA NA NA
#> Gene 99 NA NA NA NA NA NA
#> Gene 100 NA NA NA NA NA NA
head(pValue(contrastResults(se, "Limma-Voom")))
#> Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6
#> 8.166075e-03 1.402741e-05 NA NA NA NA
head(log2FoldChange(contrastResults(se, "Limma-Voom")))
#> Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6
#> 1.911076 2.264091 NA NA NA NA
head(averageLog2(contrastResults(se, "Limma-Voom")))
#> Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6
#> 1.3536992 0.9251094 NA NA NA NA