In andreagrioni/ToolViz: ToolViz

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Installation

devtools::install_github("andreagrioni/cautious-doodle")

library(ToolViz)
data("test_data")

Rain Cloud Plot

Visualize time-dependent protein concentration

The function is adapted from Cédric Scherer's blog here

Function arguments:

data: tibble class object as sample X features. Features can be gene names, probes ids, or gene sets.
target: column name corresponding to the feature to be visualize (e.g. Probe ID, Gene Name),
annotation: if features are probes ids, you can provide a tibble class object with probes ids and corresponding EntrezGeneSymbol.
x: the column name of the time event (e.g. Visit, Days)..
group_ids: how to separate samples in the dataset (e.g. Treatment_code, ARM code)..
spaghetti: add lines to connect each observation over time.
colspaghetti: color lines according to a feature (e.g. SEX).

The annotation must have a column named SeqId with ProbeIds as values and a column named EntrezGeneSymbol with the corresponding gene name.

plot_raincloud(
  test_data,
  #annotation=anno_v4,
  target='seq.10008.43',
  x='Sample...Visit.Description..clin.event.',
  group_ids = 'ARM',
  spaghetti="Sample...Screening.ID",
  colspaghetti='PlateId'
)

#ggsave("PATH/TO/FILE.png", dpi=300, width = 15, height=15)

Distribution Plot

Visualize Protein Concentration Changes over time as Violin Plot

Function arguments:

data: tibble class object as sample X features. Features can be gene names, probes ids, or gene sets.
target: column name corresponding to the feature to be visualize (e.g. Probe ID, Gene Name),
annotation: if features are probes ids, you can provide a tibble class object with probes ids and corresponding EntrezGeneSymbol.
x: the column name of the time event (e.g. Visit, Days)..
group_ids: how to separate samples in the dataset (e.g. Treatment_code, ARM code)..
spaghetti: add lines to connect each observation over time.
colspaghetti: color lines according to a feature (e.g. SEX).

The annotation must have a column named SeqId with ProbeIds as values and a column named EntrezGeneSymbol with the corresponding gene name.

ToolViz::plot_dist(
  test_data,
  #annotation=anno_v4,
  target='seq.10008.43',
  x='Sample...Visit.Description..clin.event.',
  #rows_by="ARM",
  cols_by='ARM',
  kind="grid",
  x_rot=45,
  scales_grid='free_y',
  spaghetti="Sample...Screening.ID",
  colspaghetti='SEX',
  stats=TRUE,
  x_lab="time")

#ggsave("PATH/TO/FILE.png", dpi=300, width = 15, height=15)

draw_heatmap

Visually represent the plate position of each sample in the dataset

Function useful to debug experiment where some of the samples were corrupted. The heatmap helps to identify if corrupted samples originated from a cluster/region of the plate.

The function looks for the column named PlatePosition and separate it into column_location and row_location.

Function arguments:

table = table obtained from somascan adat file

draw_heatmap(
  table=test_data,
  color_by="ARM",
  text_by="SEX",
  title="heatmap 96-well plate position",
  text_size=4)

#ggsave("PATH/TO/FILE.png", dpi=300, width = 15, height=15)