The sina plot is a decisive visualization tool showing numerical data spread across various classes. Sina plots derive their design from the combination of strip charts and violin plots and display data distributions through vital information about density distribution while showing outliers alongside sample sizes. The article investigates sina plot features through discussions about construction principles as well as advantages and applications alongside implementation methods.
The data visualization method known as sina plot enables the visual representation of single variables that belong to multiple classes. The point distribution in this plotting method demonstrates width according to the kernel density estimation results. Each point displayed in the plot represents unique data, while its horizontal width indicates the quantity of data gathered at that specific value. A sina plot resembles its violin plot counterpart since it shows data points instead of kernel densities. The main benefit of using sina plots comes from their ability to preserve information that violin plots lack. The application of Sina plots is most beneficial when an analysis involves datasets that exhibit wide differences in data point counts between classes.
The density information of each class obtained through kernel methods governs jitter width in sina plots. Non-parametric Kernel density estimation serves as a method to evaluate the probability density function of random variables. The stats package in R provides the density function, which allows analysts to estimate the density function of every class individually. The band parameter lets users control the sharpness or smoothness of the obtained curves. The definition of the samples’ value range into bins with equal lengths occurs independently across every class. A sample in the same bin establishes neighbor relations while multiple samples in a given neighborhood get distributed on the x-axis through samples from a uniform distribution.
Several important benefits emerge when researchers make use of Sina plots over traditional approaches for visualizing data.
Sina plots serve multiple applications in the fields of data science and computational biology because of their effective display abilities. The plotting technique provides specific value through data combination when sample quantities differ between different classes. The visualization technique enables users to display different variables with their multiple measurements whenever arranged by various classes. Sina plots serve all data analysts together with data presenters, including professionals from growing ‘omics’ fields that handle expansive collected data sets. A sina plot displayed 2095 bone marrow samples from AML and ALL patients as well as healthy donors.
Several programming languages, as well as libraries, can generate Sina plots.
R users can generate sina plots through the sina plot package according to this example implementation.
# Install and load the sinaplot package
install.packages(“sinaplot” )
library(sinaplot)
# Create sample data
x <- c(rnorm(200, 4, 1), rnorm(200, 5, 2), rnorm(400, 6, 1.5))
groups <- c(rep(“Cond1” , 200), rep(“Cond2” , 200), rep(“Cond3” , 400))
# Create the sina plot
sinaplot(x, groups)
# Customize the plot
sinaplot(x, groups, col = 2:4, pch = 20, bty = “n” )
This example shows how the ‘sinaplot’ function generates a sina plot based on the data x, which is divided into groups. Users can modify plot appearance by providing arguments such as col for choosing different colors while ‘pch’ controls point symbols and ‘bty’ sets the box type.
Sina plots undergo regular comparison with bar charts and box plots, as well as strip charts and violin plots. The full set of meaningful data information can only be revealed through combined pairs of plots or through dense multiple representations of these plot types. Sina plots unite both strip charts and violin plots functions to handle the deficiencies found in typical visualization chart formats.
Users can change multiple components within Sina plots to make them more appealing to view and easier to understand. Some common customization options include:
Sina plots function as important graphical tools that display numerical data distributions throughout multiple groups. Analysis conducted through sina plots merges strip chart elements with violin plot characteristics to present true and complete data information about distribution density patterns and outlier occurrences together with whole sample size details. The sina plot serves as an essential tool because it provides a simple implementation strategy and adaptation features that benefit both data scientists and computational biologists and all additional researchers working to discover new insights from their datasets.