Package 'EpiViz'

Create a Circos plot with up to 3 tracks using points, lines or bars

Description

Create a Circos plot with up to 3 tracks using points, lines or bars

Usage

circos_plot(
  track_number,
  track1_data,
  track2_data,
  track3_data,
  track1_type,
  track2_type,
  track3_type,
  label_column,
  section_column,
  order = TRUE,
  order_column,
  estimate_column,
  pvalue_column,
  pvalue_adjustment,
  lower_ci,
  upper_ci,
  lines_column,
  lines_type = "o",
  bar_column,
  histogram_column,
  histogram_binsize = 0.01,
  histogram_densityplot = FALSE,
  legend = FALSE,
  pvalue_legend = FALSE,
  track1_label = NA,
  track2_label = NA,
  track3_label = NA,
  pvalue_label = NA,
  circle_size = 25,
  track1_height = 0.2,
  track2_height = 0.2,
  track3_height = 0.2,
  equal_axis = FALSE,
  origin = 0,
  colours = c("#00378f", "#ffc067", "#894300")
)

Arguments

track_number	The number of tracks you wnat your circos plot to have. Maximum number of tracks is 3.
track1_data	The data frame of your first track.
track2_data	The data frame of your second track.
track3_data	The data frame of your third track.
track1_type	The type of plot for the first track. One of "scatter", "lines", "bar" and "histogram".
track2_type	The type of plot for the second track. One of "scatter", "lines", "bar" and "histogram".
track3_type	The type of plot for the third track. One of "scatter", "lines", "bar" and "histogram".
label_column	The column in your data frames that you will use as the labels for the circos plot. This will likely be the column with your exposure/outcome name in. Labels go on the outside of the plot.
section_column	The column in your data frames that you will use to group/section your data/plot by.
order	Do you want MR Viz to organise your columns alphabetically. Default is TRUE
order_column	If order = FALSE what column do you want to order your sections by
estimate_column	The column in your data frames with the estimates in.
pvalue_column	The column in your data frames with the p-values in.
pvalue_adjustment	The threshold you want to set your p-value 'significance' threshold too. This can be in the format of a single value or a function e.g. 0.05/22. The p-value thrshold is only used for scatter plots.
lower_ci	The column in your data frames with the lower confidence intervals in.
upper_ci	The column in your data frames with the upper confidence intervals in.
lines_column	The column in your data frames that you want to plot as lines.
lines_type	The type of line plot you want. "l" = straight lines; "o" = straight lines with points; "s" = boxed lines; "h" = vertical lines. Default is "o".
bar_column	The column in your data frames that you want to plot as bars.
histogram_column	The column in your data frames that you want to plot as histograms.
histogram_binsize	The binsize of the histogram. Default is 0.01.
histogram_densityplot	Do you want your histogram to be a density plot or not. Default is FALSE.
legend	Do you want a legend = TRUE or FALSE. FALSE by default
pvalue_legend	Do you want a p-value legend icon (defalut = FALSE)
track1_label	What do you want the label for the first track to be e.g. "Body Mass Index"
track2_label	What do you want the label for the first track to be e.g. "Corondary Heart Disease"
track3_label	What do you want the label for the first track to be e.g. "Breast Cancer"
pvalue_label	What do you want the label for the p-value to read e.g. "p-value <= 0.05"
circle_size	The size of the circos plot. Smaller numbers make larger circos plots. Default is 25. You will need to adjust this number a few times to get the perfect size for your specific circos plot requirements. If you have long labels then try ≥ 25. If your labels are short try 1-10.
track1_height	Size of the track as a percent of the whole circle. Default is 20 percent (0.20)
track2_height	Size of the track as a percent of the whole circle. Default is 20 percent (0.20)
track3_height	Size of the track as a percent of the whole circle. Default is 20 percent (0.20). It is sometimes worth increasing the size of track 3 to 30 percent
equal_axis	Do you want your tracks to share the same axis (Defalut = FALSE), if TRUE it will use the minimmum and maximum from the upper and lower confidence intervals to calculate the axis for each track. This ONLY applies to 'points' all other plot types are independent of each track.
origin	Where do you want your X axis line drawn, e.g. 0 for continuous outcomes and 1 for binary outcomes when using beta and odds ratios respectively. Default is 0
colours	list of colours for each track. Use "".

Examples

circos_plot(track_number = 3,
track1_data = EpiViz_data1,
track2_data = EpiViz_data2,
track3_data = EpiViz_data3,
track1_type = "points",
track2_type = "lines",
track3_type = "bar",
label_column = 1,
section_column = 9,
estimate_column = 2,
pvalue_column = 3,
pvalue_adjustment = 1,
lower_ci = 4,
upper_ci = 5,
lines_column = 2,
lines_type = "o",
bar_column = 2,
legend = FALSE,
pvalue_legend = FALSE,
circle_size = 25)

---

Example data: Mendelian randomization results from an analysis of BMI measured in European men and women and 123 metabolites

Description

A simulated dataset of linear regression results for 123 outcomes.

Usage

EpiViz_data1

Format

A simulated data frame with 123 rows and 8 variables imitating a traditional epidemiology results file from a linear regression:

label

The name of the outcomes

beta

Effect estimates

pvalue

Pvalues of the effec estimates and standard errors

lower_confidence_interval

Lower confidence interval bound

upper_confidence_interval

Upper confidence interval bound

exposure

The exposure used in the Mendelian randomization alaysis

label_units

The name of the outcomes with units

class

The groups which the outcomes can be put into

subclass

The subgroups which the outcomes can be put into

...

---

Example data: Mendelian randomization results from an analysis of BMI measured in European men and 123 metabolites

Description

A simulated dataset of linear regression results for 123 outcomes.

Usage

EpiViz_data2

Format

A simulated data frame with 123 rows and 8 variables imitating a traditional epidemiology results file from a linear regression:

label

The name of the outcomes

beta

Effect estimates

pvalue

Pvalues of the effec estimates and standard errors

lower_confidence_interval

Lower confidence interval bound

upper_confidence_interval

Upper confidence interval bound

exposure

The exposure used in the Mendelian randomization alaysis

label_units

The name of the outcomes with units

class

The groups which the outcomes can be put into

subclass

The subgroups which the outcomes can be put into

...

---

Example data: Mendelian randomization results from an analysis of BMI measured in European women and 123 metabolites

Description

A simulated dataset of linear regression results for 123 outcomes.

Usage

EpiViz_data3

Format

A simulated data frame with 123 rows and 8 variables imitating a traditional epidemiology results file from a linear regression:

label

The name of the outcomes

beta

Effect estimates

pvalue

Pvalues of the effec estimates and standard errors

lower_confidence_interval

Lower confidence interval bound

upper_confidence_interval

Upper confidence interval bound

exposure

The exposure used in the Mendelian randomization alaysis

label_units

The name of the outcomes with units

class

The groups which the outcomes can be put into

subclass

The subgroups which the outcomes can be put into

...

---