plot_cutoff: Check number of DE genes at different cutoff combinations
In RVA: RNAseq Visualization Automation

Description Usage Arguments Details Value References Examples

This function processes summary statistics table generated by differential expression analysis like limma or DESeq2 to evaluate the number of differntially expressed genes with different FDR and fold change cutoff.

plot_cutoff(
  data = data,
  comp.names = NULL,
  FCflag = "logFC",
  FDRflag = "adj.P.Val",
  FCmin = 1.2,
  FCmax = 2,
  FCstep = 0.1,
  p.min = 0,
  p.max = 0.2,
  p.step = 0.01,
  plot.save.to = NULL,
  gen.3d.plot = TRUE,
  gen.plot = TRUE
)

`data`	Summary statistics table or a list of summary statistics tables from limma or DEseq2, where each row is a gene.
`comp.names`	A character vector that contains the comparison names which correspond to the same order as `data`.
`FCflag`	The column name of the log2FC in the summary statistics table. Default = "logFC".
`FDRflag`	The column name of the False Discovery Rate (FDR) in the summary statistics table. Default = "adj.P.Val".
`FCmin`	The minimum starting fold change cutoff to be checked, so the minimum fold change cutoff to be evaluated will be FCmin + FCstep, FCmin default = 1.
`FCmax`	The maximum fold change cutoff to be checked, default = 2.
`FCstep`	The step from the minimum to maximum fold change cutoff, one step increase at a time, default = 0.01.
`p.min`	The minimum starting FDR cutoff to be checked, so the minimum fold change cutoff to be evaluated will be p.min + p.step, p.min default = 0.
`p.max`	The maximum FDR cutoff to be checked, default = 0.2.
`p.step`	The step from the minimum to maximum fold change cutoff, one step increase at a time, default = 0.005.
`plot.save.to`	The address where to save the plot from simplified cutoff combination with FDR of 0.01, 0.05, 0.1, and 0.2.
`gen.3d.plot`	Whether generate a 3d plotly object to visualize the result, only applys to single dataframe input, default = F.
`gen.plot`	Whether generate a plot to visualize the result, default = T.

The function takes the summary statistics and returns a list which contains 3 objects: a table which describes the number of DE genes with different cutoff combinations of FDR and fold change, a ggplot object which depicts a simplified version of cutoff selection combination, and a plotly 3d visulization object which depicts a high resolution of cutoff combinations. The default range of the fold change is from 1 to 2, and p value is from 0 to 0.2, with the step of 0.01 for FC and 0.005 for FDR.

If the input data is a data list, then a multi-facet ggplot plot object which contains each of the summary statistics table will be returned; otherwise, if the input data is a data frame, then the function will return a list which contains 3 elements:

`df.sub`	A dataframe, which contains the number of genes(3rd column) with FDR (1st column), Fold Change (2nd column)
`plot3d`	A plotly object to show the 3d illustration of all possible cutoff selectiosn and the number of DE genes in the 3d surface
`gp`	A ggplot object to show the simplified cutoff combination result

Xingpeng Li & Olya Besedina, RVA - RNAseq Visualization Automation tool.

plot_cutoff(Sample_summary_statistics_table)

plot_cutoff(data = list(Sample_summary_statistics_table, Sample_summary_statistics_table1),
           comp.names = c("A", "B"))