MA-plot from base means and log fold changes

MA-plot from base means and log fold changes, in the ggplot2 framework, with additional support to annotate genes if provided.

plot_ma(
  res_obj,
  FDR = 0.05,
  point_alpha = 0.2,
  sig_color = "red",
  annotation_obj = NULL,
  draw_y0 = TRUE,
  hlines = NULL,
  title = NULL,
  xlab = "mean of normalized counts - log10 scale",
  ylim = NULL,
  add_rug = TRUE,
  intgenes = NULL,
  intgenes_color = "steelblue",
  labels_intgenes = TRUE,
  labels_repel = TRUE
)

Arguments

res_obj

A DESeq2::DESeqResults() object

FDR

Numeric value, the significance level for thresholding adjusted p-values

point_alpha

Alpha transparency value for the points (0 = transparent, 1 = opaque)

sig_color

Color to use to mark differentially expressed genes. Defaults to red

annotation_obj

A data.frame object, with row.names as gene identifiers (e.g. ENSEMBL ids) and a column, gene_name, containing e.g. HGNC-based gene symbols. Optional

draw_y0

Logical, whether to draw the horizontal line at y=0. Defaults to TRUE.

hlines

The y coordinate (in absolute value) where to draw horizontal lines, optional

title

A title for the plot, optional

xlab

X axis label, defaults to "mean of normalized counts - log10 scale"

ylim

Vector of two numeric values, Y axis limits to restrict the view

add_rug

Logical, whether to add rug plots in the margins

intgenes

Vector of genes of interest. Gene symbols if a symbol column is provided in res_obj, or else the identifiers specified in the row names

intgenes_color

The color to use to mark the genes on the main plot.

labels_intgenes

Logical, whether to add the gene identifiers/names close to the marked plots

labels_repel

Logical, whether to use geom_text_repel for placing the labels on the features to mark

Value

An object created by ggplot

Details

The genes of interest are to be provided as gene symbols if a symbol column is provided in res_obj, or else by using the identifiers specified in the row names

Examples

library("airway")
data("airway", package = "airway")
airway
#> class: RangedSummarizedExperiment 
#> dim: 63677 8 
#> metadata(1): ''
#> assays(1): counts
#> rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
#>   ENSG00000273493
#> rowData names(10): gene_id gene_name ... seq_coord_system symbol
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(9): SampleName cell ... Sample BioSample
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
# subsetting for quicker run, ignore the next two commands if regularly using the function
gene_subset <- c(
  "ENSG00000103196", # CRISPLD2
  "ENSG00000120129", # DUSP1
  "ENSG00000163884", # KLF15
  "ENSG00000179094", # PER1
  rownames(dds_airway)[rep(c(rep(FALSE, 99), TRUE), length.out = nrow(dds_airway))]
) # 1% of ids
dds_airway <- dds_airway[gene_subset, ]

dds_airway <- DESeq2::DESeq(dds_airway)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
res_airway <- DESeq2::results(dds_airway)

plot_ma(res_airway, FDR = 0.05, hlines = 1)


plot_ma(res_airway,
  FDR = 0.1,
  intgenes = c(
    "ENSG00000103196", # CRISPLD2
    "ENSG00000120129", # DUSP1
    "ENSG00000163884", # KLF15
    "ENSG00000179094"  # PER1
  ) 
)