| plot_ma_de | R Documentation | 
Because I can never remember, the following from wikipedia: "An MA plot is an application of a Bland-Altman plot for visual representation of two channel DNA microarray gene expression data which has been transformed onto the M (log ratios) and A (mean average) scale."
plot_ma_de(
  table,
  expr_col = "logCPM",
  fc_col = "logFC",
  p_col = "qvalue",
  pval = 0.05,
  alpha = 0.4,
  logfc = 1,
  label_numbers = TRUE,
  size = 2,
  shapes = TRUE,
  invert = FALSE,
  label = NULL,
  label_column = "hgncsymbol",
  ...
)
| table | Df of linear-modelling, normalized counts by sample-type, | 
| expr_col | Column showing the average expression across genes. | 
| fc_col | Column showing the logFC for each gene. | 
| p_col | Column containing the relevant p values. | 
| pval | Name of the pvalue column to use for cutoffs. | 
| alpha | How transparent to make the dots. | 
| logfc | Fold change cutoff. | 
| label_numbers | Show how many genes were 'significant', 'up', and 'down'? | 
| size | How big are the dots? | 
| shapes | Provide different shapes for up/down/etc? | 
| invert | Invert the ma plot? | 
| label | Label the top/bottom n logFC values? | 
| label_column | gene annotation column from which to extract labels. | 
| ... | More options for you | 
ggplot2 MA scatter plot. This is defined as the rowmeans of the normalized counts by type across all sample types on the x axis, and the log fold change between conditions on the y-axis. Dots are colored depending on if they are 'significant.' This will make a fun clicky googleVis graph if requested.
[limma_pairwise()] [deseq_pairwise()] [edger_pairwise()] [basic_pairwise()]
 ## Not run: 
  plot_ma(voomed_data, table)
  ## Currently this assumes that a variant of toptable was used which
  ## gives adjusted p-values.  This is not always the case and I should
  ## check for that, but I have not yet.
 
## End(Not run)
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