H eatmap is one of the must-have data visualization toolkits for data scientists. In Rthere are many packages to generate heatmaps, such as heatmapheatmap. However, my favorite one is pheatmap.
I am very positive that you will agree with my choice after reading this post. The raw data is from the basketball reference. You can either download the dataset manually or scrape the data by following one of my previous posts. Ready to begin? Language: R. Package name: pheatmap. Above is the head of the data frame we are working on.
Data cleaning: filter out players who played less than 30 minutes per game, remove duplicates of players who got traded during the season and fill NA values with 0. First, pheatmap only takes the numeric matrix object as input.
So, we need to transfer the numeric part of the data frame to a matrix by removing the first 5 columns of categorical data. The scale function in R performs standard scaling to the columns of the input data, which first subtracts the column means from the columns center step and then divides the centered columns by the column standard deviations scale step. This function is to scale the data to a distribution with mean as 0 and standard deviation as 1.
Its equation can be shown as below, where x is the data, u is the column means and s is the column standard deviations. After scaling the data is ready to be fed into the function. The default behavior of the function includes the hierarchical clustering of both rows and columnsin which we can observe similar players and stats types in close positions.
The code below cancels the column clustering. Actually, the function itself can do both row and column scaling in the heatmap. It mainly serves as a visualization purpose for the comparison across rows or columns. The following code shows the row scaling heatmap. The annotation function is one of the most powerful features of pheatmap. Specifically, you can input an independent data frame with annotations to the rows or columns of the heatmap matrix. For example, I annotated each player with their position, made it a data frame object and input it to the pheatmap function.
One thing to note, the row names of the annotation data frame have to match the row names or column names of the heatmap matrix depending on your annotation target. You can see from the heatmap that there is another column of colors that indicate the position of the players. Also, we can add the column annotation as well.
I named the stats with their categories that include OffenceDefenceand others. Then, I plot the heatmap with column annotation only. This time I only turn on the column clustering. We can see from the heatmap that the offense-related stats tend to be clustered together.You can report issue about the content on this page here Want to share your content on R-bloggers? The app introduces a functionality that saves to disk a self contained copy of the htmlwidget as an html file with your data and specifications you set from the UI, so it can be embedded in webpages, blogposts and online web appendices for academic publications.
From github :. The application has an import interface as part of the application which currently supports csv, txt, tab, xls, xlsx, rd, rda. You can start the app using:.
The gadget is called from the R console and accepts input arguments. The object defined as the input to the shinyHeatmaply gadget is a data. You can start it using the following code:. You can see an example of a saved shinyHeatmaply output here. Or view the following iframe:. I am very grateful to them both. And lastly, to my adviser Yoav Benjamini for his support and advices. To leave a comment for the author, please follow the link and comment on their blog: R — R-statistics blog.
Want to share your content on R-bloggers? Never miss an update! Subscribe to R-bloggers to receive e-mails with the latest R posts. You will not see this message again.HeatmapInteractive Visualization.
Cluster AnalysisData Visualization. This articles describes how to create and customize an interactive heatmap in R using the heatmaply R package, which is based on the ggplot2 and plotly. Note that, other data transformation functions are scale for standardizationpercentize [for percentile transformation; available in the heatmaply R package]. Heatmaply has also the option to produce a static heatmap using the ggheatmap R function:.
Note that, heatmaply uses the seriation package to find an optimal ordering of rows and columns. The function heatmaply has an option named seriatewhich possible values include:. The default color palette is viridis. Other excellent color palettes are available in the packages cetcolor and RColorBrewer. By default, the colour of the text on each cell is chosen to ensure legibility, with black text shown over light cells and white text shown over dark cells. Prerequisites Data preparation Basic heatmap Split rows and columns dendrograms into k groups Change color palettes Customize dendrograms using dendextend Add annotation based on additional factors Add text annotations Add custom hover text Saving your heatmaply into a file References.
Prerequisites Install the required R package: install. Data preparation Normalize the data to make the variables values comparable. Basic heatmap heatmaply df Heatmaply has also the option to produce a static heatmap using the ggheatmap R function: ggheatmap df Note that, heatmaply uses the seriation package to find an optimal ordering of rows and columns. The result is similar to what we would get by default from hclust. Split rows and columns dendrograms into k groups The k-means algorithm is used.
Change color palettes The default color palette is viridis. Add text annotations By default, the colour of the text on each cell is chosen to ensure legibility, with black text shown over light cells and white text shown over dark cells. Saving your heatmaply into a file Create an interactive html file: dir. References Introduction to heatmaply. Recommended for you This section contains best data science and self-development resources to help you on your path.Author : Tal Galili Tal.
Galili gmail. A heatmap is a popular graphical method for visualizing high-dimensional data, in which a table of numbers are encoded as a grid of colored cells.
The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by dendrograms. Heatmaps are used in many fields for visualizing observations, correlations, missing values patterns, and more. Interactive heatmaps allow the inspection of specific value by hovering the mouse over a cell, as well as zooming into a region of the heatmap by dragging a rectangle around the relevant area.
This work is based on ggplot2 and plotly. It produces similar heatmaps as d3heatmap, with the advantage of speed plotly. This interface can provide smaller objects and faster rendering to disk in many cases and provides otherwise almost identical features. The default settings in heatmaply attempt to be both useful yet not too computationally intensive.
Here is an example based on the mtcars dataset:. The data was extracted from the Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles models. We can use the margins parameter with correlation heatmaps. We can also do a more advanced correlation heatmap where the p-value from the correlation test is mapped to point size:.
The variables in mtcars includes values that reflect different types of measurement, each with its own range and meaning of values. In such a case, it is best to transform the data so to have all the variables have comparable values. If we would assume all variables come from some normal distribution, then scaling i. In such a case, each value would reflect the distance from the mean in units of standard deviation. When variables in the data comes from possibly different and non-normal distributions, other transformations may be in order.
Another possibility is to use the normalize function to brings data to the 0 to 1 scale by subtracting the minimum and dividing by the maximum of all observations. Using the function on mtcars easily reveals columns with only two amvs or three gearcyl variables compared with variables that have a higher resolution of possible values:.
An alternative to normalize is the percentize function.
This is similar to ranking the variables, but instead of keeping the rank values, divide them by the maximal rank. This is done by using the ecdf of the variables on their own values, bringing each value to its empirical percentile.
The benefit of the percentize function is that each value has a relatively clear interpretation, it is the percent of observations with that value or below it.
Notice that for binary variables 0 and 1percentize will turn all 0 values to their proportion and all 1 values will remain 1. This means the transformation is not symmatric for 0 and 1. Hence, if scaling for clustering, it might be better to use rank for dealing with tie values if no ties are present, then percentize will perform similarly to rank.
Reviewing missing values can easily be done using the is. We can use colors other than the default viridis. The packages cetcolor and RColorBrewer provide a number of excellent options for continuous and discrete colour palettes. These are generally designed to be perceptually uniform, and often also colorblind-friendly.
For example, we may want to use other color palettes in order to get divergent colors for the correlations these will, sadly, often be less useful for colorblind people :. Optimal means to optimize the Hamiltonian path length that is restricted by the dendrogram structure.
This, in other words, means to rotate the branches so that the sum of distances between each adjacent leaf label will be minimized. This is related to a restricted version of the travelling salesman problem.
Another option is "GW" Gruvaeus and Wainer which aims for the same goal but uses a potentially faster heuristic. The option "mean" gives the output we would get by default from heatmap functions in other packages such as gplots::heatmap. The option "none" gives us the dendrograms without any rotation that is based on the data matrix. This works heavily relies on the seriation package their vignette is well worth the readand also lightly on the dendextend package see vignette.Typically, reordering of the rows and columns according to some set of values row or column means within the restrictions imposed by the dendrogram is carried out.
This heatmap provides a number of extensions to the standard R heatmap function. By default, it is TRUE, which implies dendrogram is computed and reordered based on row means.
If a dendrogramthen it is used "as-is", ie without any reordering. If a vector of integers, then dendrogram is computed and reordered based on the order of the vector. Defaults to dist.
Defaults to hclust. Defaults to 'both'. The default is "none". Can be used to add components to the plot. Boolean indicating whether breaks should be made symmetric about 0. Defaults to c 0. Defaults to "cyan". The distance of the line from the center of each color-cell is proportional to the size of the measurement.
Defaults to 'column'. Vector of values within cells where a horizontal or vertical dotted line should be drawn. The color of the line is controlled by linecol.
Horizontal lines are only plotted if trace is 'row' or 'both'. Vertical lines are only drawn if trace 'column' or 'both'. The defaults currently only use number of rows or columns, respectively. Boolean indicating whether the color key should be made symmetric about 0. Numeric scaling value for tuning the kernel width when a density plot is drawn on the color key.
See the adjust parameter for the density function for details. Defaults to 0. Returns a named list containing parameters that can be passed to axis.
See examples. If either Rowv or Colv are dendrograms they are honored and not reordered. If either is NULLno reordering will be done for the corresponding side.
Introduction to heatmaply
There is some empirical evidence from genomic plotting that this is useful.Interactivity includes a tooltip display of values when hovering over cells, as well as the ability to zoom in to specific sections of the figure from the data matrix, the side dendrograms, or annotated labels.
Thanks to the synergistic relationship between heatmaply and other R packages, the user is empowered by a refined control over the statistical and visual aspects of the heatmap layout. Supplementary data are available at Bioinformatics online.
A cluster heatmap is a popular graphical method for visualizing high dimensional data. In it, a table of numbers is scaled and encoded as a tiled matrix of colored cells. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by dendrograms and extra columns of categorical annotation.Understanding Manhattan Plots and Genome-wide Association Studies
The ongoing development of this iconic visualization, spanning over more than a century, has provided the foundation for one of the most widely used of all bioinformatics displays Wilkinson and Friendly, When using the R language for statistical computing R Core Team,there are many available packages for producing static heatmaps, such as: statsgplotsheatmap3fheatmappheatmap and others. Recently released packages also allow for more complex layouts; these include gapmapsuperheat and ComplexHeatmap Gu et al.
The next evolutionary step has been to create interactive cluster heatmaps, and several solutions are already available. However, these solutions, such as the idendro R package Sieger et al. Some solutions do exist for creating shareable interactive heatmaps.
How to Create a Beautiful Interactive Heatmap in R
The HTML file contains a publication-ready, interactive figure that allows the user to zoom in as well as see values when hovering over the cells.
This self-contained HTML file can be made available to interested readers by uploading it to the researcher's homepage or as a Supplementary Material in the journal's server. The rest of this paper offers guidelines for creating effective cluster heatmap visualization. Figure 1 demonstrates the suggestions from this section on data from project Tycho van Panhuis et al. The square root number of people infected by Measles in 50 states, from to The generation of cluster heatmaps is a subtle process Gehlenborg and Wong, ; Weinstein,requiring the user to make many decisions along the way.
The major decisions to be made deal with the data matrix and the dendrogram. The raw data often need to be transformed in order to have a meaningful and comparable scale, while an appropriate color palette should be picked. The clustering of the data requires us to decide on a distance measure between the observation, a linkage function, as well as a rotation and coloring of branches that manage to highlight interpretable clusters.
Each such decision can have consequences on the patterns and interpretations that emerge. In this section, we go through some of the arguments in the function heatmaplyaiming to make it easy for the user to tune these important statistical and visual parameters. Our toy example visualizes the effect of vaccines on measles infection. Both were created using:.
The first argument of the function x accepts a matrix of the data. In the measles data, each row corresponds with a state, each column with a year from toand each cell with the number of people infected with measles per people. In this example, the data were scaled twice—first by not giving the raw number of cases with measles, but scaling them relatively to people, thus making it possible to more easily compare between states. And second by taking the square root of the values.
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