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

## 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)
# }