Data visualization serves as a crucial element in today's research landscape and business analytics environment. Among visualization techniques, heatmaps have emerged as particularly powerful tools extensively utilized for analyzing gene expression patterns, interactions between proteins, pathway analysis in metabolism, and microbial communities found in soil samples. This article provides a comprehensive overview of heatmap fundamentals, their key benefits, interpretation guidelines, and demonstrates practical implementation using R programming. The primary aim is to equip scientists and data specialists with an effective visualization methodology that maintains scientific integrity.
What is Heatmap
A heatmap represents numerical data matrices through graduated color schemes. By converting data values into corresponding color intensities, these visual tools transform complex numerical datasets into easily digestible visual representations. This transformation helps reveal patterns, cluster relationships, and exceptional values within the data. For this reason, they frequently appear in scientific literature illustrating patterns of gene expression, networks of protein interactions, metabolic activities, and similarity measurements between samples, providing visual foundations for subsequent statistical analysis and biological investigations.
The Advantages of Heatmap
Visual clarity that enables quick identification of high-value regions, low-value areas, and unusual patterns through color gradient representation
Exceptional information density capability, allowing substantial multidimensional data presentation within limited visual space
Enhanced comparative analysis through simultaneous display of multiple variables or samples, facilitating correlation discovery and group characteristic identification
Remarkable adaptability, with heatmaps easily integrated alongside clustering approaches and correlation analyses across various scientific fields
Extensive customization options including annotations, descriptive legends, numerical displays, and specialized color palettes that communicate both data and underlying scientific reasoning
How to Read a Heatmap Plot
Accurate interpretation of heatmaps is critical for unveiling the intrinsic structure of data. The primary interpretative steps include:
(1) Examining the color gradient and associated legend, wherein each color corresponds to a specific numerical range; typically, darker hues denote higher values, while lighter hues indicate lower values.
(2) Understanding the significance of the rows and columns, which generally represent different variables or samples. For example, in gene expression heatmaps, rows may represent genes and columns may represent various samples or experimental conditions.
(3) Analyzing the dendrograms, when provided, which depict clustering relationships and similarities among data points, thereby facilitating the identification of functionally related or similarly behaving groups.
(4) Identifying outliers and distinctive regions, as areas with color deviations from the overall trend may signal unique biological phenomena or experimental anomalies that require further investigation.
(5) Integrating additional annotation information, such as sample groupings or functional classifications, to achieve a comprehensive understanding of the dataset's context and internal logic.
The heatmap plot of differentially expressed circRNAs in patients and compared with healthy donors.(Chen, C., et.al, 2022)
How to Draw a Heatmap Plot in R
So, how do we draw heatmap plot in the R language? Here, the editor brings you a examples. We will start with simple random data.
First we need to install and load the pheatmap package:
install.packages("pheatmap")
library(pheatmap)
Next, we can use the pheatmap() function to draw a heatmap plot.
In this example, we generated a random data set containing 10 samples and 20 genes.
test = matrix(rnorm(200), 20, 10)
test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:20, sep = "")
We then use the pheatmap() function to visualize the results.
pheatmap(test)
The random dataset heatmap plot result.
Additionally, we can customize the drawing by adjusting some parameters:
Using the parameter "color" to change the heatmap plot color
Showing text within cells with the parameter "display_numbers"
Fixing cell sizes with parameters "cellwidth" and "cellheight"
Adding row and color annotations with parameters "annotation_col" and "annotation_col"
annotation_col = data.frame(
CellType = factor(rep(c("CT1", "CT2"), 5)),
Time = 1:5
)
rownames(annotation_col) = paste("Test", 1:10, sep = "")
annotation_row = data.frame(
GeneClass = factor(rep(c("Path1", "Path2", "Path3"), c(10, 4, 6)))
)
rownames(annotation_row) = paste("Gene", 1:20, sep = "")
pheatmap(test,
color = colorRampPalette(c("navy", "white", "firebrick3"))(50),
display_numbers = TRUE,cellwidth = 20, cellheight = 20,
annotation_col = annotation_col, annotation_row = annotation_row)
The random dataset heatmap plot result.
In addition, we can also use ComplexHeatmap to draw heatmap plot ourselves.
Using the Heatmap() function to visualize the results.
install.packages("ComplexHeatmap")
library(ComplexHeatmap)
Heatmap(test)
The random dataset heatmap plot result.
Heatmaps have become essential visualization techniques in both scientific research contexts and commercial analytics. This article has explored their conceptual foundations, principal advantages, and interpretive frameworks. We have also outlined practical implementation steps using R programming with both pheatmap and ComplexHeatmap packages. Through effective color-based representation and flexible clustering capabilities, heatmaps facilitate rapid pattern identification within complex datasets, generating valuable insights for scientific discovery and strategic planning. Ongoing advancement in heatmap generation techniques and interpretation methodology will likely further enhance researchers' ability to extract meaningful biological and business insights from increasingly complex data structures.
Reference
- Chen, C., Yu, H., Han, F. et al. Tumor-suppressive circRHOBTB3 is excreted out of cells via exosome to sustain colorectal cancer cell fitness. Mol Cancer 21, 46 (2022). https://doi.org/10.1186/s12943-022-01511-1
