FlexDotPlot_Tutorial"

In FlexDotPlot: Flexible Dot Plot

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", 
  echo = TRUE,
  fig.width = 16, fig.height = 6
)

Package installation

install.packages("devtools")
devtools::install_github("Simon-Leonard/FlexDotPlot")

Package Loading

library(FlexDotPlot)

---

Run the dot_plot function

1. Example dataset {.tabset}

Command

data(PBMC3K_example_data)

Input overview (head)

head(PBMC3K_example_data)

Input overview (summary)

summary(PBMC3K_example_data)

---

2. Choose variables to plot {.tabset}

Use the size parameters

Size can be set with a column name using the size_var argument. Size scale can be adjusted with the shape.scale argument

dot_plot(data.to.plot = PBMC3K_example_data, size_var = "pct.exp", shape.scale = 8)

A column index or a vector can also be provided

# Same results
dot_plot(data.to.plot = PBMC3K_example_data, size_var = 3, shape.scale = 8) # Third column = pct.exp
dot_plot(data.to.plot = PBMC3K_example_data, size_var = PBMC3K_example_data$pct.exp, shape.scale = 8)

Comment : The variable controlling the shape size has to be numeric

class(PBMC3K_example_data$pval_symb)
dot_plot(data.to.plot = PBMC3K_example_data, size_var ="pval_symb") # Error ; size_var column has to be numeric

Additional size parameters

Scale the size by size or radius with the scale.by argument (default = "radius") Set lower and upper limits for scaling with scale.min and scale.max arguments, use NA for default values.

---

Use the color parameters

Color can be set with a column name using the col_var argument.

dot_plot(data.to.plot = PBMC3K_example_data, col_var = "pct.exp", shape.scale = 20)

A column index or a vector can also be provided

# Same results
dot_plot(data.to.plot = PBMC3K_example_data, col_var = 3, shape.scale = 20) # Third column = pct.exp
dot_plot(data.to.plot = PBMC3K_example_data, col_var = PBMC3K_example_data$pct.exp, shape.scale = 20)

Comment : A discrete variable can also be used to set the shape color

class(PBMC3K_example_data$pval_symb)
dot_plot(data.to.plot = PBMC3K_example_data, col_var ="pval_symb", shape.scale=20) 

Use custom colors

Custom colors can be used with the cols.use parameter

dot_plot(data.to.plot = PBMC3K_example_data, col_var = "pct.exp", cols.use = c("yellow","blue"), shape.scale = 20)

Use the shape parameters

Variable shape can be set with a column name using the shape_var argument.

dot_plot(data.to.plot = PBMC3K_example_data, shape_var = "pct.exp", shape.scale = 20)

A vector can also be provided

# Same results
dot_plot(data.to.plot = PBMC3K_example_data, shape_var = PBMC3K_example_data$pct.exp, shape.scale = 8)

Comment : A discrete variable can also be used to set the shape

class(PBMC3K_example_data$pval_symb)
dot_plot(data.to.plot = PBMC3K_example_data, shape_var ="pval_symb", shape.scale=20) 

You can choose the shape types with the shape_use argument and unicode characters

dot_plot(data.to.plot = PBMC3K_example_data, shape_var ="pval_symb", shape_use=c("\u2605","\u2736","\u25CF","\u2737","\u2726"),
         shape.scale=20) 

Add text on shapes

Text can be set with a column name using the text_var argument. Text size can be adjusted with the text.size argument

dot_plot(data.to.plot = PBMC3K_example_data, text_var = "pct.exp2", text.size = 3, shape.scale = 20)

A column index or a vector can also be provided

# Same results
dot_plot(data.to.plot = PBMC3K_example_data, text_var = 4, , text.size = 3, shape.scale = 20)
dot_plot(data.to.plot = PBMC3K_example_data, text_var = PBMC3K_example_data$pct.exp2, , text.size = 3, shape.scale = 20)

---

3. Combine variables

Shape, size, colors and text can be provided simultaneously and independently

dot_plot(data.to.plot = PBMC3K_example_data, 
         size_var = "pct.exp", shape.scale = 25,
         shape_var= "pct.exp",
         col_var = "avg_logFC"
         )

dot_plot(data.to.plot = PBMC3K_example_data, 
         size_var = "pct.exp", shape.scale = 8,
         shape_var= ifelse(PBMC3K_example_data$avg_logFC>0, "Up","Down"), 
         col_var = "avg_logFC", cols.use = c("yellow","blue")
         )

4. Add dendrograms to the plot

Variable used to compute x and y dendrogramms can be provided within the dend_x_var and dend_y_var respectively. dist_method and hclust_methods arguments control distance method (default = euclidean) and hclust method (default = ward.D).

x dendrogramm only :

dot_plot(data.to.plot = PBMC3K_example_data, 
         size_var = "pct.exp", shape.scale = 25,
         shape_var= "pct.exp",
         col_var = "avg_logFC",
         dend_x_var = c("pct.exp","avg_logFC"),
         dist_method="euclidean", hclust_method="ward.D"
         )

x and y dendrogramms :

dot_plot(data.to.plot = PBMC3K_example_data, 
         size_var = "pct.exp", shape.scale = 25,
         shape_var= "pct.exp",
         col_var = "avg_logFC",
         dend_x_var = c("pct.exp","avg_logFC"),
         dend_y_var = c("pct.exp","avg_logFC"),
         dist_method="euclidean", hclust_method="ward.D"
         )

---

5. Customize plot legends {.tabset}

Shape legend

Legend title can be modified with the size_legend, col_legend and shape_legend arguments.

dot_plot(data.to.plot = PBMC3K_example_data, 
         size_var = "pct.exp", shape.scale = 8, size_legend="My size legend",
         shape_var= "pval_symb", shape_legend="My shape legend",
         col_var = "avg_logFC", col_legend="My col legend"
         )

If you don't want to plot the legend, set the plot.legend argument to FALSE.

dot_plot(data.to.plot = PBMC3K_example_data, 
         size_var = "pct.exp", shape.scale = 8,
         shape_var= "pval_symb",
         col_var = "avg_logFC",
         plot.legend=F
         )

size.breaks.number argument define the number of shapes with different size to display in the legend

dot_plot(data.to.plot = PBMC3K_example_data, size_var = "pct.exp", shape.scale = 8, size.breaks.number = 3)

You can also directly specify the values to get in the legend with the size.breaks.values argument

dot_plot(data.to.plot = PBMC3K_example_data, size_var = "pct.exp", shape.scale = 8, size.breaks.values = c(0,0.25,0.5,0.75,1))

color.breaks.number argument define the number of labels for the color gradient legend.

dot_plot(data.to.plot = PBMC3K_example_data, col_var = "pct.exp", shape.scale = 20, color.breaks.number = 3)

You can also directly specify the values to get in the legend with the color.breaks.values argument

dot_plot(data.to.plot = PBMC3K_example_data, col_var = "pct.exp", shape.scale = 20, color.breaks.values = c(0,0.3,0.6,0.9))

Axis labels

Axis label positions can be controled with the x.lab.pos and y.lab.pos arguments. x.lab.pos must be one of "bottom","top","both" or "none". y.lab.pos must be one of "left","right","both"or "none".

dot_plot(data.to.plot = PBMC3K_example_data, 
         size_var = "pct.exp", shape.scale = 8,
         shape_var= "pct.exp",
         col_var = "avg_logFC",
         x.lab.pos="bottom",
         y.lab.pos="left"
         )

Plot can be transposed by setting the transpose argument to TRUE

dot_plot(data.to.plot = PBMC3K_example_data, 
         size_var = "pct.exp", shape.scale = 8,
         shape_var= "pct.exp",
         col_var = "avg_logFC",
         transpose=TRUE
         )

---

6. Plot background {.tabset}

Vertical shades

Vertical shades can be added with the vertical_coloring argument

dot_plot(data.to.plot = PBMC3K_example_data, 
         size_var = "pct.exp", shape.scale = 25,
         shape_var= "pct.exp",
         col_var = "avg_logFC",
         vertical_coloring=c(NA, "gray80")
         )

Horizontal shades

Horizontal shades can be added with the horizontal_coloring argument

dot_plot(data.to.plot = PBMC3K_example_data, 
         size_var = "pct.exp", shape.scale = 8,
         shape_var= "pct.exp",
         col_var = "avg_logFC",
         horizontal_coloring=c(NA, "gray80")
         )

7. Get the corresponding ggplot object

Set do.return to TRUE to get the corresponding ggplot object

ggplot_object <- dot_plot(data.to.plot = PBMC3K_example_data, 
                          size_var = "pct.exp", shape.scale = 8,
                          shape_var= "pct.exp",
                          col_var = "avg_logFC",
                          do.return = TRUE
                          )

Run the Shiny app

Shiny_dot_plot() 

Reproduce the article figure

Full script to generate dotplot input can be found at in the FlexDotPlot_CBMC_dataset vignette

### Import data + create the exp.type column
data(CBMC8K_example_data)

### Dotplot
dotplot = dot_plot( 
  data.to.plot=CBMC8K_example_data, 
  size_var="RNA.avg.exp.scaled",col_var="ADT.avg.exp.scaled", text_var="ADT.pct.exp.sup.cutoff",
  shape_var="canonical_marker", shape_use = c("\u25CF","\u2737"),
  x.lab.pos="bottom",y.lab.pos="left", 
  cols.use=c("lightgrey","orange","red", "darkred"),size_legend="RNA", col_legend="ADT", shape_legend="Canonical marker ?", 
  shape.scale =12, text.size=3, 
  plot.legend = TRUE, 
  size.breaks.number=4, color.breaks.number=4, shape.breaks.number=5
  , dend_x_var=c("RNA.avg.exp.scaled","ADT.avg.exp.scaled"), dend_y_var=c("RNA.avg.exp.scaled","ADT.avg.exp.scaled"), dist_method="euclidean",
  hclust_method="ward.D", do.return = TRUE)

Session Info

sessionInfo()

FlexDotPlot documentation built on April 5, 2022, 1:14 a.m.