volcanoPlot | R Documentation |
The function ComplexHeatmap
creates a volcano plot. On the y-axis the
-log10(p-values) are displayed, while on the x-axis the fold
changes/differences are displayed.
The output of the function differs depending on the
type
parameter. For type == "ttest"
, the fold changes are
plotted; for type == "proDA"
, the differences are plotted.
volcanoPlot(df, type = c("ttest", "proDA"))
df |
|
type |
|
Internal use in shinyQC
.
plotly
## create se
a <- matrix(seq_len(100), nrow = 10, ncol = 10,
dimnames = list(seq_len(10), paste("sample", seq_len(10))))
a[c(1, 5, 8), seq_len(5)] <- NA
set.seed(1)
a <- a + rnorm(100)
a_i <- imputeAssay(a, method = "MinDet")
cD <- data.frame(sample = colnames(a),
type = c(rep("1", 5), rep("2", 5)))
rD <- data.frame(spectra = rownames(a))
se <- SummarizedExperiment::SummarizedExperiment(assay = a,
rowData = rD, colData = cD)
se_i <- SummarizedExperiment::SummarizedExperiment(assay = a_i,
rowData = rD, colData = cD)
## create model and contrast matrix
modelMatrix_expr <- stats::formula("~ 0 + type")
contrast_expr <- "type1-type2"
modelMatrix <- model.matrix(modelMatrix_expr, data = colData(se))
contrastMatrix <- limma::makeContrasts(contrasts = contrast_expr,
levels = modelMatrix)
## ttest
fit <- limma::lmFit(a_i, design = modelMatrix)
fit <- limma::contrasts.fit(fit, contrastMatrix)
fit <- limma::eBayes(fit, trend = TRUE)
df_ttest <- limma::topTable(fit, n = Inf, adjust = "fdr", p = 0.05)
df_ttest <- cbind(name = rownames(df_ttest), df_ttest)
## plot
volcanoPlot(df_ttest, type = "ttest")
## proDA
fit <- proDA::proDA(a, design = modelMatrix)
df_proDA <- proDA::test_diff(fit = fit, contrast = contrast_expr,
sort_by = "adj_pval")
## plot
volcanoPlot(df_proDA, type = "proDA")
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