Convert DESeq2 objects to a volcano3d object

This function is used instead of polar_coords if you have raw RNA-Seq count data. It takes 2 DESeqDataSet objects, extracts statistical results and converts the results to a 'volc3d' object, which can be directly plotted.

deseq_polar(
  object,
  objectLRT,
  contrast = NULL,
  data = NULL,
  pcutoff = 0.05,
  padj.method = "BH",
  filter_pairwise = TRUE,
  ...
)

Arguments

object	An object of class 'DESeqDataSet' with the full design formula. The function DESeq needs to have been run.
objectLRT	An object of class 'DESeqDataSet' with the reduced design formula. The function DESeq needs to have been run on this object with argument test="LRT".
contrast	Character value specifying column within the metadata stored in the DESeq2 dataset objects is the outcome variable. This column must contain a factor with 3 levels. If not set, the function will select the last term in the design formula of object as per DESeq2 convention.
data	Optional matrix containing gene expression data. If not supplied, the function will pull the expression data from within the DESeq2 object using the DESeq2 function assay(). NOTE: for consistency with gene expression datasets, genes are in rows.
pcutoff	Cut-off for p-value significance
padj.method	Can be any method available in p.adjust or "qvalue". The option "none" is a pass-through.
filter_pairwise	Logical whether adjusted p-value pairwise statistical tests are only conducted on genes which reach significant adjusted p-value cut-off on the group likelihood ratio test
...	Optional arguments passed to polar_coords

Value

Calls polar_coords to return an S4 'volc3d' object

See also

polar_coords, voom_polar, DESeq in the DESeq2 package

Examples

# \donttest{
  library(DESeq2)

  counts <- matrix(rnbinom(n=1500, mu=100, size=1/0.5), ncol=15)
  cond <- factor(rep(1:3, each=5), labels = c('A', 'B', 'C'))

  # object construction
  dds <- DESeqDataSetFromMatrix(counts, DataFrame(cond), ~ cond)
#> converting counts to integer mode

  # standard analysis
  dds <- DESeq(dds)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing

  # Likelihood ratio test
  ddsLRT <- DESeq(dds, test="LRT", reduced= ~ 1)
#> using pre-existing size factors
#> estimating dispersions
#> found already estimated dispersions, replacing these
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing

  polar <- deseq_polar(dds, ddsLRT, "cond")
  volcano3D(polar)
  radial_ggplot(polar)

# }