Graphing with pmartR

Kelly Stratton, Lisa Bramer

2024-10-16

Source: vignettes/Graphing_with_pmartR.Rmd

Overview

Graphs in pmartR are made with ggplot2 (Wickham 2016), unless the interactive = TRUE argument in specified, in which case plots are generated using plotly (Sievert 2020).

This vignette provides an overview of plot capabilities included in pmartR, describes the various arguments available in the plot functions, and the use of “custom sample names” for less cluttered graphs.

Plot functions are included for

  • omicsData objects

  • Filter objects

  • Correlation heatmap

  • Dimension reduction

  • Missing data

  • Statistical results

Custom Sample Names

Sometimes the sample names in an experiment are sufficiently long that they make a plot unreadable. To assist the user in this situation, pmartR includes the ability to specify shorter sample names for use in plots only (e.g. the column names for each sample in e_data and the corresponding column in f_data remain unchanged).

Here we plot the metabData object from pmartRdata using the default sample names:

mymetab <- edata_transform(omicsData = metab_object, data_scale = "log2")
plot(mymetab, color_by = "Phenotype", order_by = "Phenotype")

If we would like shorter samples names so they don’t take so much space on the graph, we can utilize the custom_sampnames function to shorten the existing sample names based on one of the following:

  • the first nn characters in the sample name

  • a substring of the sample name from a specific location in the overall character string to another (e.g. from character 1 to character 12, or from character 7 to character 20)

  • based on a delimeter

  • using pattern matching (regex)

Examples of using each of these approaches can be found in the documentation ?custom_sampnames. Here we will use the delimeter approach. Note that there must be a unique custom sample name for each sample.

head(mymetab$f_data$SampleID)
## [1] "Sample_1_Phenotype2_B" "Sample_2_Phenotype3_B" "Sample_3_Phenotype2_A"
## [4] "Sample_4_Phenotype1_B" "Sample_5_Phenotype2_B" "Sample_6_Phenotype2_B"
# specify new names using delim and components arguments
mymetab_shorter_names <- custom_sampnames(omicsData = mymetab, delim = "_", components = c(1, 2))
plot(mymetab, use_VizSampNames = TRUE, color_by = "Phenotype", order_by = "Phenotype")

If we wish to obtain the plot using the original sample names, simply omit the use_VizSampNames argument to the plot function or set it to FALSE.

plot(mymetab, color_by = "Phenotype", order_by = "Phenotype")

plot(mymetab, use_VizSampNames = FALSE, color_by = "Phenotype", order_by = "Phenotype")

Common Arguments to pmartR Plot Functions

When appropriate for a given plot type, the following options are available to the user to further customize their visualizations:

  • order_by - A character string specifying a main effect by which to order the boxplots. This main effect must be found in the column names of f_data in the omicsData object. If is “Group”, the boxplots will be ordered by the group variable from the group_designation function. If NULL (default), the boxplots will be displayed in the order they appear in the data.

  • color_by - A character string specifying a main effect by which to color the boxplots. This main effect must be found in the column names of f_data in the omicsData object. If is “Group”, the boxplots will be colored by the group variable from the group_designation function. If NULL (default), the boxplots will have one default color.

  • facet_by - A character string specifying a main effect with which to create a facet plot. This main effect must be found in the column names of f_data in the omicsData object. Default value is NULL.

  • facet_cols - An optional integer specifying the number of columns to show in the facet plot.

  • interactive - Logical. If TRUE produces an interactive plot.

  • x_lab - A character string specifying the x-axis label.

  • y_lab - A character string specifying the y-axis label. The default is NULL in which case the y-axis label will be the metric selected for the argument.

  • x_lab_size - An integer value indicating the font size for the x-axis. The default is 11.

  • y_lab_size - An integer value indicating the font size for the y-axis. The default is 11.

  • x_lab_angle - An integer value indicating the angle of x-axis labels. The default is 0.

  • title_lab - A character string specifying the plot title.

  • title_lab_size - An integer value indicating the font size of the plot title. The default is 14.

  • legend_lab - A character string specifying the legend title.

  • legend_position - A character string specifying the position of the legend. Can be one of “right”, “left”, “top”, “bottom”, or “none”. The default is “none”.

  • ylimit - A numeric vector of length 2 specifying y-axis lower and upper limits.

  • bw_theme - Logical. If TRUE uses the ggplot2 black and white theme.

  • palette - A character string indicating the name of the RColorBrewer palette to use. For a list of available options see the details section in .

  • use_VizSampNames - Logical. Indicates whether to use custom sample names. The default is FALSE.

Additional Plot Layers

Since pmartR utilizes ggplot2 for (non-interactive) graphs, additional plot layers can be added to pmartR plots. Note that adding plot layers may override some of the built-in pmartR plot functionality. In the example below, we see that adding theme_dark() changes the background color but also changes the orientation of the x-axis labels.

plot(mymetab, order_by = "Phenotype", color_by = "Phenotype") +
  theme_dark()

Plot Methods - Alphabetical

A number of S3 object classes are defined specifically for use in the pmartR package, and many of these have accompanying plot methods. The following list shows which objects have associated plot methods. For details, see the help pages using e.g. ?plot.corRes.

  • plot.corRes
  • plot.customFilt
  • plot.cvFilt
  • plot.dataRes
  • plot.dimRes
  • plot.imdanovaFilt
  • plot.isobaricnormRes
  • plot.isobaricpepData
  • plot.lipidData
  • plot.metabData
  • plot.moleculeFilt
  • plot.naRes
  • plot.nmrData
  • plot.nmrnormRes
  • plot.normRes
  • plot.pepData
  • plot.proData
  • plot.proteomicsFilt
  • plot.rmdFilt
  • plot.SPANSRes
  • plot.statRes

Plot Methods - Grouped by Type of S3 Data Object

Data Objects

  • plot.isobaricpepData
  • plot.lipidData
  • plot.metabData
  • plot.nmrData
  • plot.pepData
  • plot.proData

Filters

  • plot.customFilt
  • plot.cvFilt
  • plot.imdanovaFilt
  • plot.moleculeFilt
  • plot.proteomicsFilt
  • plot.rmdFilt

EDA

  • plot.corRes
  • plot.dataRes
  • plot.dimRes
  • plot.naRes

Normalization

  • plot.SPANSRes
  • plot.isobaricnormRes
  • plot.nmrnormRes
  • plot.normRes

Statistics

  • plot.statRes

References

Sievert, Carson. 2020. Interactive Web-Based Data Visualization with r, Plotly, and Shiny. Chapman; Hall/CRC. https://plotly-r.com.
Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.