K-means clustering is a method for identifying groups of similar observations, i.e. cells or samples. K-means clustering aims to group observations into a pre-determined number of clusters (k) so that each observation belongs to the cluster with the nearest mean. An important aspect of K-means clustering is that it expects clusters to be of similar size (equal variance) and shape (distribution of variance is spherical). The Compare Clusters task can also be used to help determine the optimal number of K-means clusters.
We recommend normalizing your data prior to running K-means clustering, but the task will run on any counts data node.
Click the counts data node
Click the Exploratory analysis section of the toolbox
Click K-means clustering
K-means clustering produces a K-means Clusters result data node; double-click to open the task report which lists the cluster statistics (Figure 2). If Compute biomarkers was enabled, top markers will be available by double-clicking the Biomarkers result data node. If clustering was run with Split by sample enabled on a single cell counts data node, the cluster results table displays the number of clusters found for each sample and clicking the sample name opens the sample-level report.
The total number of clusters is listed along with the number and percentage of cells in each cluster.
The K-means Clustering result data node includes the input values and adds cluster assignment as a new attribute, K-means, for each observation.
Choose which distance metric to use for cluster distance calculations. Options include Euclidean, Absolute Value, Euclidean Squared, Kendall Correlation, Max Value, Min Value, Pearson Correlation, Rank Correlation, Average Euclidean, Shape, Cosine, Canberra, Bray Curtis, Tanimoto, Pearson Correlation Absolute, Rank Correlation Absolute, and Kendall Correlation Absolute. The default is Euclidean.
Choose between specifying a set number of clusters or a range to test for the best fit number of clusters. The best fit is determined by the number of clusters with the lowest Davies–Bouldin index. The default is set to 10 for a fixed number of clusters. The initial values for the range option are 3 to 20 clusters.
Choose whether to run the ANOVA test comparing each cluster to all other observations to identify features that have higher values in that cluster. Default is Enabled.
This option is present in single cell data. If enabled, K-means clustering will be run separately for each sample. If disabled, K-means clustering will be run on all cells from the input data. Default is set by the Split single cell by sample option in the user preference page.
If enabled, the initial cluster centroids will be selected randomly from among the data points. If disabled, the initial cluster centroids will be selected to optimize distance between clusters. Default is Disabled.
This sets the random seed used if Random cluster initialization is enabled. Use the same random seed to reproduce results.
If enabled, all cluster centroids will be recomputed at the end of each iteration. If disabled, each cluster centroid will be recomputed as the members of the cluster change. Default is Enabled.
The maximum number of iterations to perform before setting on a set of clusters. Default is 1000.
If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.
Click Finish to run (Figure 1)
<figure><img src="../../../../.gitbook/assets/image (92).png" alt=""><figcaption><p>Figure 1. K-means clustering configuration dialog</p></figcaption></figure>