https://github.com/somalogic/somaplotr
A highly specialized suite of standardized plotting routines based on the "Grammar of Graphics" framework of mapping variables to aesthetics used in 'ggplot2'. Graphics types are biased towards visualizing SomaScan (proteomic) data.
https://github.com/somalogic/somaplotr
dataviz ggplot2 proteomics proteomics-data-analysis r r-package somascan
Last synced: 4 months ago
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A highly specialized suite of standardized plotting routines based on the "Grammar of Graphics" framework of mapping variables to aesthetics used in 'ggplot2'. Graphics types are biased towards visualizing SomaScan (proteomic) data.
- Host: GitHub
- URL: https://github.com/somalogic/somaplotr
- Owner: SomaLogic
- License: other
- Created: 2023-09-14T00:26:19.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-16T20:10:19.000Z (12 months ago)
- Last Synced: 2025-06-16T21:24:48.295Z (12 months ago)
- Topics: dataviz, ggplot2, proteomics, proteomics-data-analysis, r, r-package, somascan
- Language: R
- Homepage: https://somalogic.github.io/SomaPlotr/
- Size: 58.9 MB
- Stars: 3
- Watchers: 4
- Forks: 4
- Open Issues: 6
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md
- Support: .github/SUPPORT.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r setup, echo = FALSE, message = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
fig.align = "left"
)
dcf <- read.dcf(file = file.path(".", "DESCRIPTION"))
if ( nrow(dcf) < 1L)
stop("DESCRIPTION file of package is corrupt.", call. = FALSE)
desc <- as.list(dcf[1L, ])
ver <- paste0("https://img.shields.io/badge/Version-", desc$Version,
"-success.svg?style=flat&logo=github")
```
```{r load-pkgs, echo = FALSE, message = FALSE}
library(SomaPlotr)
library(ggplot2)
library(patchwork)
library(scales)
# Simulate numeric vectors
withr::with_seed(123, {
x <- rnorm(200, mean = 3)
y <- x + rnorm(length(x), sd = 0.2) # Add random Gaussian noise, centered at zero
})
# Simulate fold change & p-value dataset
withr::with_seed(101, {
fc1 <- sort(runif(500, -2.5, 0)) # Z-scores as fold changes
fc2 <- sort(runif(500, 0, 2.5)) # Z-scores as fold changes
p1 <- pnorm(fc1)
p2 <- pnorm(fc2, lower.tail = FALSE)
p <- jitter(c(p1, p2), amount = 0.1)
p[p < 0] <- runif(sum(p < 0), 1e-05, 1e-02) # Use floor p < 0 after jittering
fc_df <- data.frame(fc = c(fc1, fc2), p = p)
})
# Simulate a clinical data frame
timepoint <- c("Treatment", "Relapse", "Remission")
withr::with_seed(101, {
clin_df <- data.frame(
TimePoint = sample(timepoint, 80, replace = TRUE),
seq.1234.56 = stats::rnorm(80, mean = 12, sd = 5)
)
})
# Use example datasets above to create plots
cdf <- plotCDF(x)
ccc <- plotConcord(x = x, y = y, main = "Concordance of 2 Continuous Variables")
volc <- plotVolcano(fc_df, FC = fc, p.value = p, cutoff = 0.1)
bx <- boxplotGrouped(clin_df, y = "seq.1234.56", group.var = "TimePoint",
beeswarm = TRUE, main = "Box Plot of Sample Timepoint")
```
# SomaPlotr 
[](https://cran.r-project.org/package=SomaPlotr)
[](https://cran.r-project.org/package=SomaPlotr)
[](https://github.com/SomaLogic/SomaPlotr/actions)
[](https://app.codecov.io/gh/SomaLogic/SomaPlotr?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://choosealicense.com/licenses/mit/)
## Overview
The `SomaPlotr` R package contains various standardized plotting
functions designed to visualize SomaScan data in the R statistical environment.
`SomaPlotr` is based on the "Grammar of Graphics"
([Wilkinson, 2005](https://link.springer.com/book/10.1007/0-387-28695-0)), the
same layered framework of mapping variables to aesthetics that provides a
structure for [ggplot2](https://cran.r-project.org/package=ggplot2).
As such, `SomaPlotr` largely utilizes ggplot-style syntax, and can easily and
quickly create fully labeled, colored figures of SomaScan data.
`SomaPlotr` is designed to generate figures that are well-suited for the
exploration and visualization of SomaScan data. `SomaPlotr` is therefore
biased towards cumulative distribution function (CDF) plots, probability
density function (PDF) plots, volcano and concordance plots, grouped boxplots,
and more.
```{r plot-grid, echo = FALSE, fig.width = 13, fig.height = 11}
# Display plots in a grid w/ patchwork
(cdf + ccc) / (volc + bx )
```
Keep reading (in the [Examples](#examples) section below) to see how these graphics can
be generated using functions in `SomaPlotr`.
For a full table of the visualizations that can be created with
`SomaPlotr`, see the [Usage](#usage) section below. For additional examples and
details, see the package vignette (`vignette("SomaPlotr")`).
----------------
## Installation
`SomaPlotr` can be installed from [GitHub](https://github.com/SomaLogic/SomaPlotr)
using the [remotes](https://CRAN.R-project.org/package=remotes) package as
follows:
```{r install-github, eval = FALSE}
remotes::install_github("SomaLogic/SomaPlotr")
```
To load `SomaPlotr`, simply make a call to `library()`:
```{r library, eval = FALSE}
library(SomaPlotr)
```
`SomaPlotr` provides tools to create figures commonly used for the visualization
of SomaScan data. While the package does not include a comprehensive list of all
possible graphics, those that it _does_ contain are popular for exploring and
analyzing patterns in SomaScan.
## Primary Functionality
The table below provides a high-level summary of the graphics that can be
created with `SomaPlotr`:
| Plot type | Description | Function(s) |
| :------------ | :-------- | :-------------------- |
| CDF | Empirical cumulative distribution function | `plotCDF()`, `plotCDFlist()`, `plotCDFbyGroup()` |
| PDF | Probability density function | `plotPDF()`, `plotPDFlist()`, `plotCDFbyGroup()` |
| Volcano | Statistical significance vs. magnitude of fold change | `plotVolcano()`, `plotVolcanoHTML()` |
| Concordance | Correlation coefficients of values in numeric vectors `x` and `y` | `plotConcord()` |
| Histogram | Frequency distribution of numeric data | `plotDoubleHist()` |
| Boxplot | Locality, spread and skewness of numeric data via quartiles | `boxplotBeeswarm()`, `boxplotGrouped()`, `boxplotSubarray()` |
| Longitudinal | Graphical representation of numeric data over time, by subject (also called a "trace plot") |`plotLongitudinal()` |
The plotting functions in `SomaPlotr` require a SomaScan dataset as input,
and generate a complete, labeled figure that can be further customized and
modified, if desired.
Using `SomaPlotr`, the previously shown boxplot comparing RFU values across
groups can be generated via `boxplotGrouped()`:
```{r boxplot-grouped}
# Simulate an example dataset
timepoint <- c("Treatment", "Relapse", "Remission")
withr::with_seed(101, {
clin_df <- data.frame(
TimePoint = sample(timepoint, 80, replace = TRUE),
seq.1234.56 = stats::rnorm(80, mean = 12, sd = 5)
)
})
# Create grouped boxplot w/ customized title
boxplotGrouped(
clin_df,
y = "seq.1234.56",
group.var = "TimePoint",
beeswarm = TRUE,
main = "Box Plot of Sample Timepoint"
)
```
Similarly, we can replicate the CDF plot in the introductory figure above
via `plotCDF()`:
```{r cdf, out.width = "60%"}
x <- withr::with_seed(101, rnorm(100, mean = 4))
cdf <- plotCDF(x)
cdf
```
A quantile/percentile line
(the red line in the figure below)
can be added to an existing CDF plot via `addCDFquantiles()`:
```{r cdf-quantiles, out.width = "60%"}
cdf + addCDFquantiles(x, col = "red")
```
For a more in-depth overview of the package and a full list of example plots,
please see the package vignette:
`vignette("SomaPlotr")`.
----------------
## Color Palettes
`SomaPlotr` provides color palettes that correspond to SomaLogic Operating
Co., Inc. company color schemes:
```{r palette-soma, out.width = "60%", fig.height = 4}
scales::show_col(palette_soma(n = 8))
```
These palettes can be incorporated into figures in a variety of ways. The
`soma_colors` and `soma_colors2` objects provide individual
hex colors for the SomaLogic color scheme. These objects can be used
to transform the [ggplot2](https://ggplot2.tidyverse.org/) default
color palette.
```{r soma-colors2, out.width = "60%"}
data.frame(x = seq_along(soma_colors2), y = seq_along(soma_colors2)) |>
ggplot(aes(x = x, y = y)) +
geom_bar(stat = "identity", fill = soma_colors2) +
scale_x_discrete(labels = names(soma_colors2)) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank())
```
## Themes
The `theme_soma()` theme and `scale_color_soma()` functions provide a uniform
plotting and color scheme. By using the `theme_soma()` theme, polished,
publication ready figures can be generated with consistent font sizes,
backgrounds, legend positions, and more.
Below, the left plot (`p1`) was created with the `mtcars` data set, using all
`ggplot2` defaults. The right plot (`p2`) uses the same data set, but with the
`theme_soma()` theme and `scale_color_soma()` color scale applied.
```{r theme-soma}
p1 <- ggplot(mtcars, aes(x = hp, y = mpg, color = factor(cyl))) +
geom_point(alpha = 0.5, size = 4)
p2 <- p1 +
theme_soma() +
scale_color_soma()
```
```{r theme-comparison, echo=FALSE, fig.width=11, fig.height=5}
p1 + p2
```
More detailed examples illustrating how to apply these themes can be found in
`vignette("themes-and-palettes")`.
For a full list of available color scales and themes, see
`?SomaPlotr::theme_soma`.
------------
## MIT LICENSE
* See:
- [LICENSE](https://github.com/SomaLogic/SomaPlotr/blob/main/LICENSE.md)
* The MIT license:
- [https://choosealicense.com/licenses/mit/](https://choosealicense.com/licenses/mit/)
- [https://www.tldrlegal.com/license/mit-license/](https://www.tldrlegal.com/license/mit-license)
* Further:
- "SomaPlotr" and "SomaLogic" are trademarks owned by
Standard BioTools, Inc. No license is hereby granted to
these trademarks other than for purposes of identifying the origin or
source of this Software.