Plot function to visualize differential transcript usage (DTU)

plotDTU(
  object,
  contrast,
  groups,
  coefficients,
  summaryStat = "model",
  transcripts = NULL,
  genes = NULL,
  top.n = 6
)

Arguments

object: A `SummarizedExperiment` containing the models and results of the DTU analysis as obtained by the `fitDTU` and `testDTU` function from this `satuRn` package, respectively.
contrast: Specifies the specific contrast for which the visualization should be returned. This should be one of the column names of the contrast matrix that was provided to the `testDTU` function.
groups: A `list` containing two character vectors. Each character vector contains the names (sample names or cell names) of the respective groups in the target contrast.
coefficients: A `list` containing two numeric vectors. Each numeric vector specifies the model coefficient of the corresponding groups in the selected contrast.
summaryStat: Which summary statistic for the relative usage of the transcript should be displayed. `Character` or `character vector`, must be any of following summary statistics; model (default), mean or median.
transcripts: A `character` or `character vector` of transcript IDs, to specify which transcripts should be visualized. Can be used together with `genes`. If not specified, `plotDTU` will check if the `genes` slot is specified.
genes: A single `character` or `character vector` of gene IDs, to specify the genes for which the individual transcripts should be visualized. Can be used together with `transcripts`. If not specified, `plotDTU` will check if the `transcripts` slot is specified.
top.n: A `numeric` value. If neither `transcripts` nor `genes` was was specified, this argument leads to the visualization of the `n` most significant DTU transcripts in the contrast. Defaults to 6 transcripts.

Value

A ggplot object that can be directly displayed in the current R session or stored in a list.

Author

Jeroen Gilis

Examples

data(sumExp_example, package = "satuRn")
library(SummarizedExperiment)
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sumExp <- fitDTU(
    object = sumExp_example,
    formula = ~ 0 + group,
    parallel = FALSE,
    BPPARAM = BiocParallel::bpparam(),
    verbose = TRUE
)
group <- as.factor(colData(sumExp)$group)
design <- model.matrix(~ 0 + group)
colnames(design) <- levels(group)
L <- matrix(0, ncol = 2, nrow = ncol(design))
rownames(L) <- colnames(design)
colnames(L) <- c("Contrast1", "Contrast2")
L[c("VISp.L5_IT_VISp_Hsd11b1_Endou", "ALM.L5_IT_ALM_Tnc"), 1] <- c(1, -1)
L[c("VISp.L5_IT_VISp_Hsd11b1_Endou", 
    "ALM.L5_IT_ALM_Tmem163_Dmrtb1"), 2] <- c(1, -1)

sumExp <- testDTU(object = sumExp, 
    contrasts = L, 
    diagplot1 = FALSE,
    diagplot2 = FALSE,
    sort = FALSE)





group1 <- rownames(colData(sumExp))[colData(sumExp)$group == 
                                    "VISp.L5_IT_VISp_Hsd11b1_Endou"]
group2 <- rownames(colData(sumExp))[colData(sumExp)$group == 
                                    "ALM.L5_IT_ALM_Tnc"]

plots <- plotDTU(
    object = sumExp,
    contrast = "Contrast1",
    groups = list(group1, group2),
    coefficients = list(c(0, 0, 1), c(0, 1, 0)),
    summaryStat = "model",
    transcripts = c("ENSMUST00000165123", 
                    "ENSMUST00000165721", 
                    "ENSMUST00000005067"),
    genes = NULL,
    top.n = 6
)