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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